Yield Distortion Dynamics

YIELD DISTORTION DYNAMICS

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Copyright © 2020 by Raoul Kennedy All rights reserved.

This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the use of brief quotations in a book review.

ISBN: 9798623392190

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Dedicated to my family for their patience, once again

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TABLE OF CONTENTS INTRODUCTION… 1 Foundations and Summary of Book Structure Qualifications and Shortcomings…3 PART 1. DIMENSIONS OF DISTORTION… 8 1.1. Rates Distortion: Policy Rates, Natural Rates 1.1.1. Natural Rates Microeconomic-Financial Framework 1.1.2. Natural Rates vs. Policy Rates 1: Before Debt Service 1.1.3. Natural Rates vs. Policy Rates 2: After Debt Service 1. Debt Bias Feedback Loop: Extended Commentary 2. Framework Overview and Definitions 1.2. Yield and Asset Price Distortion: Equities… 41 1.2.1. Distortion Indicators 1.2.2. Distortion Dynamics: Extended Analysis at Firm Level 1.3. Debt Monetization Overview… 48 PART 2. FIRM LEVEL ANALYSIS BY SECTOR… 61 2.1. Introduction 2.1.1. Sample and Groupings 2.1.2. Tabular Results and Interpretation Financing Obligations Coverage Stress Testing 2.1.3. Default Assumption for Theoretical Models 2.2. Financing Obligations Coverage & Stress Testing… 64 2.2.1 Basic Materials Sector (2 subsectors, 6 firms) 2.2.1.1. Oil & gas production SIC 1311 iv

2.2.1.2. Petroleum Refining SIC 2911 2.2.2. Consumer Goods Sector (3 subsectors, 8 firms)… 78 2.2.2.1. Auto Makers SIC 3711 2.2.2.2. Beverages SIC 2080, 2086 2.2.2.3. Household & Personal Products SIC 2840 2.2.2.3.1 Stress Testing 2.2.3. Healthcare & Industrial Sector (2 subsectors, 6 firms)…95 2.2.3.1. Pharmaceutical SIC 2384 2.2.3.1.1 Stress Testing 2.2.3.2. Diversified (Med/Ind) Equipment SIC 3821, 3841, 3861 2.2.3.2.1. Stress Testing 2.2.4. Technology Sector (3 subsectors, 9 firms)…111 2.2.4.1. Semiconductors SIC 3674, 3663 2.2.4.1.1. Stress Testing 2.2.4.2. Software SIC 7371, 7372 2.2.4.3. Telecommunications equipment SIC 4813 2.2.4.3.1 Stress Testing 2.3. Cash Flow Analysis…133 2.3.1. Mixed Industries 1 2.3.2. Mixed Industries 2 PART 3. CONCLUSIONS AND FUTURE DIRECTIONS…146

3.1. Summary of Findings 3.1.1. Financing Obligations Coverage…146 3.1.2. Stress Testing…153 3.2. Reforming Capital Markets: Reflections…157 3.2.1 Firm Level: Pre-emptive Debt Restructuring…157 3.2.2. Policy Reforms…171 3.2.2.1. Recognition of Taxpayer Ownership…171 3.2.2.2. Addressing Monetary Asymmetry…175 3.2.2.3. Other Asymmetries and Choice Expansion Policy…177 v

Community Provider Choice and Legal Monopolies Adjudication Choice and Markets APPENDICES A1. Firm Level Analysis: Sample Data Detail…179 A2. Methodology Background Notes…184 A2.1. Financing Obligations Coverage…187 A2.2. Stress Testing…193 A2.3. Yield and Asset Price Distortion…195 A2.3.1. Definitions and Methodological Notes...195 A2.3.2 .Distortion Dynamics: Transitions and Trajectory…202 A3. Physical Yields and Returns: Agronomy Field Study…207 Addendum…228 References…229

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INTRODUCTION What forces contribute to economic decay? This compilation of studies centers on the dynamics of a disconnect between yields and natural rates of return that can be traced to policy actions: Borrowing spreads unnaturally widen, generating artificial returns that encourage unmanageable debt growth and asset price distortions. Together with debt monetization, economies are gradually fragilized to adverse shocks, compounding the severity of financial crises. The debt burden coupled with income disruption could threaten millions who depend on an unsustainable system.

The author’s natural rates framework is combined with modeling of the underlying stochastic processes to provide further insights, including dividend and debt service coverage capacity, bank and business default exposure, and interest rate determination absent monetary central planning. The findings on 29 global firms in 10 industries and 4 sectors with a combined market cap of approximately $US 5 trillion are presented.

Given worldwide debt, including some US$15 trillion of which is negative yielding (Fitzgerald 2019), pre-emptive measures and policy reforms are proposed towards building a more sustainable and just economic future. To underscore the oft-disregarded link between financial activity and physical production, an agronomic science study is featured in the appendix.

Policymakers, corporate financial and credit officers, portfolio managers, speculators and those hoping to solve economic issues may also find the analyses and methodologies informative.

Brief Overview of Prior Research. The author’s previous Financial Distortion Dynamics (2018) lays the initial groundwork for the research presented here, and the central thesis of that book—that sustainable credit and economic growth rely upon undistorted interest rates-- may be supported by the findings of this work. Traditional approaches to stimulate growth through policy-directed low-cost borrowing can lead to debt-fueled, inflationary distortion dynamics. This poses risks of asset bubbles, financial and banking crises, unjust wealth transfer, impoverishment through declining real purchasing power and 1

financial repression on savers. Ultimately, and crucial to understand is that the debt burden coupled with income disruption could threaten millions who depend on an unsustainable system.

The natural rates framework developed is a microeconomic financial approach to interest rate determination. Natural interest rates are assumed to be principally based on the underlying natural rates of return (NRR) of individual firms (i.e. firm- or entityspecific). Firm-specific natural rates of interest reflecting natural returns are viewed as key drivers of self-regulating, sustainable debt and economic growth, rather than policy interest rates and monetary inflation as vehicles to direct and service debt, respectively.

Foundations and Summary of Book Structure. The book is divided into four major parts including the Appendices.

Part 1 looks at the extent of current distortions in yields and spreads by asking: what would interest rates be without policy rates? The proximate answer begins with an overview of the natural returns framework. Employing actual data from multiple non-financial firms, natural rates on 10-year fully-amortizing term debt are estimated; the challenge presented by such an exercise in the absence free and undistorted capital markets is recognized.

The second section applies a methodology to determine the current level of distortion in asset prices, likely future trajectories and the economic risks going forward. The third section focuses on the history of a potential contributor to distortion dynamics-debt monetization.

Part 2 explores distortions emerging at the firm level from the policy environment and backdrop described in Part 1. Two major types of analyses are performed on 29 firms in four sectors (Basic Materials, Consumer Goods, Healthcare/Industrial and Technology) and 10 subsectors (industries) with a total market capitalization of approximately US$ 5 trillion as of Q1 2020. The first analysis considers whether firms can cover their financing obligations (i.e. dividends and debt service). The second analysis consists of stress-testing to obtain an estimated criticality level 2

that corresponds to the extent of the firm’s vulnerability to specified shock scenarios (e.g. net free cash flow and declines in revenue growth). A cash flow analysis is also conducted for a variety of firms in diverse industries.

Part 3 begins with the findings from the Part 2 analyses to ascertain in the aggregate the firms’ capacity to cover their financing obligations, and their exposure to adverse events. Part 3 ends proposals on preemptive measures that firms can take, as well as capital market and other reforms to mitigate the destabilizing positive feedback loops and introduce a counterbalancing effect (negative polarity).

The Appendices provide detail on sample data and the methodologies employed in the following analyses: Financing obligations coverage, stress testing and asset price distortion. Note that the Natural Rates analytical framework is presented in Part 1 rather than in the Appendix. The last section aims to highlight the link between financial sector activity and the actual physical production processes underlying economic output, yields and returns: A study in agronomic science conducted by the author at the University of California, Davis.

References. The references are cumulative and comprise those of the author’s previous works.

Qualifications and Shortcomings Background. This book consists in part of a compilation of reports and articles that were originally published online under three principal topical headlines: Dividend Coverage Roundup (DCR), Cautionary Stress Testing (CST) and Natural Rates Review (NRR), followed by various subtopics. The cash flowfocused reports (section 2.3 in this book) were part of the Dividend Coverage Roundup reports.

The book is intended as a record-keeping exercise to document the published articles as well as other related research notes in a single location for reference.

Some modifications of the articles have been made for this book.

Dividend coverage is generally referred to here as Financing 3

Obligations Coverage to highlight the combination of both dividends and debt service in the analysis. Cautionary stress testing is generally abbreviated Stress Testing, Natural Rates Review is generally abbreviated to Natural Rates.

There may exist redundancies because the reports tend to keep to a single format and share similar information; all may not have been eliminated. Due to time constraints there are unedited portions of the original reports and other writings, and the transition between some sections may lack fluidity.

Most of the originals were published in 2019, with the earliest in late 2018 and the latest in 2020; these may also be archived and searchable online. Note that updated versions of some of the documents may eventually replace the earlier versions.

For cross-referencing, the sections in this book and the original titles corresponding to them are listed next: Part 1 1.1.1. Natural Rates: Microeconomic-Financial Framework Original title: Natural Rates Review: Introduction 1.1.2. Natural Rates vs. Policy Rates 1: Before Debt Service Original title: Natural Rates Review: What the Right Tweak for Policy Rates?

1.1.3. Natural Rates vs. Policy Rates 2: After Debt Service Original title: Natural Rates Review: What’s the Right Tweak for Policy Rates 2 1.2. Yield and Asset Price Distortion: Equities Original title: Natural Rates Review: Bubble Checkpoint FY 2019 1.3.1. Debt Monetization Original title: Natural Rates Review: Debt Monetization, Yields and Lessons from History?

Part 2 All reports in Part 2 were originally titled under their respective "Dividend Coverage Roundup" or "Cautionary Stress Testing" headline followed by the [industry name] and [fiscal years]; in this book they are ordered by sector and industry.

Part 3 3.1.1. Financing Obligations Coverage: Summary of Findings 4

Original title: Dividend Coverage Roundup: Summary of Findings FY 2018-19 3.1.2. Stress Testing: Summary of Findings Original title: Cautionary Stress Testing: Summary of Findings FY 2018-19 3.2.1. Firm Level: Pre-emptive Debt Restructuring Original Title: Natural Rates Review: Debt Restructuring- A Preemptive Approach Appendices A2.1 Financing Obligations Coverage: Methodology Original title: Dividend Coverage Roundup: Background Notes A2.2 Stress Testing: Methodology Original title: Cautionary Stress Testing: Background Notes Although not exhaustive, shortcomings of the studies are noted wherever possible in the text and appendices. An overview of shortcomings is provided here: Significance of Research. While any events occurring after 2019 are not accounted for, the implications of the findings may nevertheless remain far reaching: The results suggest a significant problem of debt service capacity which is expected to fragilize companies in response to adverse events, plunging firms into debt default and economies into recession, potentially with a greater level of severity and more protracted than otherwise.

Moreover, it should be emphasized that the methodologies and application of an extension of natural interest rate theory of the analyses are relevant to analysis covering any time period.

Ranking the firms according to measures of performance is generally avoided as such an exercise may be of very limited value if not misleading, as detailed in the Appendix (Financing Obligations Coverage Methodology).

Cash Flow Analysis. In several cases, a ready explanation for the source of cash to cover the debt service was not found given the author’s time limitations. A ready explanation for potential 5

source(s) of cash is not clarified given the author’s time limitations.

No easily identifiable source for coverage other than its NFCF (6%, rounded) and some debt issuance (4%, rounded). Reduction in cash balances provided only a small source of cash (1% in FY 2018). Another look at the company’s financial statements revealed that “Sales and maturities of marketable and other securities” were a source of cash of about 17% of revenues over the three fiscal years 2016-2018, but these were largely offset by purchases of the same.

Debt service Opacity. Correct annual debt service figures are not considered reliable, and therefore in many cases estimates must be made. Ascertaining debt service amounts should be generally be viewed as opaque even if clear debt service figures are found in the financial statements. The reason for this has been stated in previous observations by the author, but in addition to concerns regarding contingent and off-balance sheet liabilities, there is reason to believe that significant amounts of debt on the firms’ balance sheets may not be adequately amortized; one example of this is the failure to term out and amortize working capital credit lines that can no longer be cleaned up.

In sum, the reliability of any financing obligations coverage (i.e. both dividends and debt service) results and their comparability across firms is in question. Accordingly, while the Financing Obligations Coverage analysis (aka Dividend Coverage Roundup as published online) remain an informative exercise, annual frequency of reports may not be warranted.

Time Period of Observations on Data. Uniform time periods of financial data are preferred but not all the firms have identical fiscal year ends. For example, the fiscal year 2018-19 financial data span the period from 1/1/2018 to 9/29/2019. Most data tend to be concentrated in a single calendar year (1/1-12/31), but 8 of the 29 firms (28%) have non-calendar fiscal year ends (3/31, 6/30 and end of September), representing approximately 39% of the market capitalization of all 29 firms combined.

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Theoretical Model Accuracy. None of the curves should be thought of as “correct” versions of the processes examined; at best they are theoretical estimates that may tend to approximate the actual. The default assumption of fat tails for the theoretical models of each firm is generally made in favor of higher-ranking distributions to err on the side of caution; while this is not viewed as a shortcoming, because it involves a value judgement, had other distributions been selected based on rank alone, the results may have differed.

Fat tails or not, even if the models accurately capture reality and sample sizes are adequate (they are usually not), much of realworld phenomena are expected to be distributed in ways that are essentially non-computable. Therefore, any predictive value for purposes of forecasting should not be assumed. Accordingly, while security-level analysis is invaluable, it may be best supported by a strategy of optionality (e.g. buying equity puts).

For a better exposition of some of these points, see Cook, John D. (2018: “Six Sigma Events,” May 31; “Obesity Index” April 23, also citing Cooke, Roger, Fat-Tailed Distributions 2014), and studies by Taleb, Nassim, et al. (2020, 2012) to cite some examples.

Expertise. Exploring solutions to complex problems that straddle several disciplines can call for deferring to seasoned specialists and professionals. For example, Cirillo, Cook, Fontanari and Taleb (Re: References of this book, terminology including explosive uncertainty; fat tails) Therefore, portions of this research might be viewed at best as an incomplete sketch of an intended problem-solving exercise. Despite these failings, as well as typos and errors, omissions and oversights, misinterpretations, contradictions, oversimplifications, unconventional approaches and writing style, it is hoped that some elements of this work might provide some insights towards reducing economic and financial distortions and their potentially devastating long-term consequences.

Natural Rates and Policy. Any conclusions drawn, including from the estimation of natural interest rates in Part 1, does not 7

imply that it would be advisable for policy to attempt to apply such approaches; in addition to the impossibility of trying to replicate natural rates via policy, the likely implication would be the removal of policy interventions to allow undistorted credit/loanable funds market to function. That said, at some point should there be policy-guided transition towards undistorted markets, management of the existing/outstanding debts would be a major undertaking involving some form of large-scale and longterm restructuring until the debts are fully amortized.

Technical parts familiar to readers can be skipped over or corrected. The reader is encouraged to consult other resources on individual topics to supplement an understanding of the material presented here, although the analytical approach of the natural rates framework may not have many counterparts at present.

Terms in italics and the abbreviation "Re:" are added to highlight some of the relevant terminology. Conventions adopted in the book and other notes are found in an Addendum before the References.

Financial Visualizations. For selected 3D stylized and other sketches of underlying theoretical models of selected companies, see the author’s art websites; given the qualifications and shortcomings, at the very least it is hoped that such models might be appreciated for their aesthetic value.

The author may also hold positions in securities of companies, including through ETFs, that are covered herein. The discussion and any visuals may contain significant errors, are subject to revisions and are provided 'as is' solely for informational purposes, not for trading or investment advice. The analyses are deemed exploratory and preliminary; no claims are made as to the validity of data, assumptions, theoretical models and methodologies; results may be based on prior data that do not reflect the most current market events.

PART 1. DIMENSIONS OF DISTORTION 1.1. Rates Distortion: Policy Rates, Natural Rates 1.1.1. Natural Rates (Microeconomic-Financial) Framework 8

Summary *This report provides an introductory history of natural interest rates for a microeconomic alternative to official estimates of the natural or neutral rate of interest. *Natural rates of interest (NRI) are linked to natural rates of return (NRR) of individual firms. These firm-specific NRR may also serve as a financial performance indicator. *Since no single interest rate can fit all firms, an imperfect option is to derive estimates of the NRI based on a composite of the firms’ financial data. *Long-term debt restructuring is considered preferable to the continued lowering of interest rates which may reinforce a potentially destabilizing debt-bias feedback loop.

Historical Overview Modern Approaches. The natural or neutral rate of interest as modelled by central banks rely largely on macroeconomic variables; Knut Wicksell (1898) with whom the term ‘natural’ interest rates appears to have originated may be referenced Wicksell’s role was important as he was clearly familiar with a classical school of interest theory detailed below and was able to provide a market-based guideline for central banking in setting policy rates (re: Brainard 2015; Holston, Laubach and Williams 2016, Manrique and Marqués 2004, Salerno 2016; Spitznagel 2017).

Also marking a critical shift to the modern view is the work of Irving Fisher (1907, 1930) that redefined the classical term ‘real interest rates’ to mean something entirely different, and which remains predominant in modern interest rate theory (re: Fisher equation; Fabozzi and Modigliani, 1992).

Classical View. The classical literature regarding interest, which dates at least as far back as John Locke (1668), is associated with a view of natural interest rates reflecting profitability, rate of profits or expected rates of return; these business rates of return are generated in the process of productive economic activity including capital investment. Crucial to note is that the natural rates of interest derived from the rates of return of firms are sharply distinguished from the interest rates offered through the banking system (i.e. bank loans as inducements to borrow which 9

were criticized for distorting economic activity through malinvestment). Also refer to Cantillon (1755), Smith (1776), Ricardo (1826), Bohm-Bawerk (1901), Mises (1912), Fetter (1914), Ropke (1936), among others. Terminology can be confusing because different words describe similar concepts: The term natural interest (Wicksell), real interest rates, normal interest rates, market interest rates all had meanings centering around this classical concept. Keynes (1936) added to the variety by coining the term marginal efficiency of capital.

Fisher, noted above, used the term ‘real interest rate’ but altered the meaning of ‘real’ away from the classical sense by defining ‘real’ as equal to ‘nominal’ less inflation in his eponymous Fisher equation.

An approach to defining the classicists’ rates of return is detailed next.

Natural Returns: Framework and Methodology. A shortcoming of the classicists may be their limited deconstruction of the financial dynamics of firms to define rates of return.

Bridging this gap is a primary aim of this microeconomicfinancial approach and previous studies by the author (e.g.

Financial Distortion Dynamics 2018, Rate-of-Return Analytics 2016). The term ‘natural’ is retained; the natural rate of return (NRR) refers to a measure that captures some fundamentally inherent value for the entity's cash rate of return on its cost. Cash, rather than accrual basis is fundamental because rates of return are ultimately accounted for physically (i.e. in terms of physical cash inflows and outflows, including existing debt service); cash pays the bills. The observed inherent or natural behavior of the entity can be defined by a spatial element which includes a central tendency and surrounding dispersion which overall defines an 'occupied' space or distribution of the firm’s return behavior. The time element captures changes in behavior over time: Observing the behavior not only for a single, specified period (e.g. 10-years as is done here), but also over the same time period (e.g. 10-years), sequentially for at least several 10-year periods. It is also important to identify possible 'shape shifts' or 'regime' changes when there are transformations of spatial dimensions over these sequential time periods. Because of the changing nature of things as well as non-computability, a universally 'true' return measure is 10

not seen as attainable, but for the purposes of decision-making in economic activity such as lending, borrowing and investing, 'best guesses' can be made for the required purpose.

In a context of a financial metrics nomenclature, these natural rates of return (NRR) have been termed rates of return on cost (RRC); both NRR and RRC are the same. RRC is defined as the firm’s net free cash flow (NFCF) / Cost; both are annual figures in the same fiscal year, cash basis. NFCF is free cash flow (FCF) less acquisitions and may also be referred to as equity income; cost is the total of the cash cost of sales, S, G &A, capital expenditures and acquisitions.

The estimated RRC for each firm is derived from theoretical curves constructed from datasets of 10 years of observations and is an estimate of a central point within the model space (i.e. spatial centrality; reference to ‘centrality’ generally mean this spatial centrality unless otherwise noted). The models and estimates are subject to serious limitations: For further explanations, see the Appendix (Financing Obligations Coverage: Methodology) or the author’s Financial Distortion Dynamics (2018). The theoretical models may be presented as 2-D, 3-D and other stylized sketches for selected firms; see the author’s page for links.

Natural Interest Rates (NRI) in Undistorted Markets The firm-specific natural returns (NRR) serve as a reference for theoretical natural interest rates (or ‘natural rates of interest’ abbreviated as NRI). NRI are not currently knowable but would be negotiated and settled in undistorted credit markets if such existed. Therefore, making predictions of the actual NRI is futile.

However, because of the close relationship between the two variables, the observed firm-specific NRR can be used to estimate a hypothetical NRI. NRI determination. How would natural interest rates be determined in free, undistorted credit markets?

Relevant variables include NRR as a theoretical upper bound for NRI (as detailed above; also see the paragraph on inequalities next), supply of and demand for loanable funds, desired returns and debt service ratios by lenders, the principal balance of the loan and its amortization term, and the debt service capacity of the firm.

The following chart taken from the author’s Financial Distortion Dynamics shows a schedule of the interest rate against the 11

principal balance of a hypothetical loan (i.e. the initial loan amount) expressed as a proportion of the maximum principal balance (Pmax) at which the interest rate is zero. The debt service coverage (DSC) ratio is in this scenario 1.5:1, and the loan term is 10 years with matching amortization.

Since both the interest rate and the loan balance are initially unknown, the natural rate is determined according to a schedule – various potential interest rates at various potential quantities of credit to be lent/borrowed within the constraints of credit guidelines (i.e. debt service capacity for a given amortization term). These computations can be made for individual firms: A table from the same book is shown here for reference. Notes: CNRI refers to Credit-based Natural Interest Rates which is synonymous with natural interest rates; ‘credit-based’ emphasizes the lending element; P/SOR the principal balance/source of repayment; DS is debt service.

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In competitive undistorted markets, firms with higher returns are more capable of paying higher interest rates and lenders may seek to improve their returns by prioritizing lending to them*; firms with lower NRRs would either borrow less or would need to raise their NRR to compete against the other firms for more credit– this process induces a self-reinforcing economization of costs. (*) Notes. For the same amortization term (i.e. a term loan) a higher interest rate corresponds to a lower initial principal balance of the loan; the greater the borrower’s debt service capacity, the more that can be borrowed (re: debt service coverage ratio). Just like NRR, NRI should be referred to in the plural form due to the diversity of firms/borrowing entities. It is likely that some firms, particularly in the tech industry, can have remarkably high NRR (>15%) for certain periods, but this should not suggest that market-based natural interest rates would necessarily settle so high – only that they define a theoretical upper bound, discussed next.

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Inequalities. A theoretical upper bound for the NRI is defined by an inequality. The nominal NRI would be expected to settle somewhere below the nominal NRR, or NRR>NRI. In a zeroinflation environment, if the firm’s net natural rate of return is 3%, then its theoretical NRI would generally be expected to settle somewhere below 3%. The logic behind this is that if a firm can generate a natural return of 3% on the use of its own funds (i.e. the firm incurs costs that tend to generate a 3% return) it would be unlikely to accept to borrow at a rate that exceeds this 3%; thus, the 3% NRR is a proximate ‘ceiling’ rate. However, the rate of inflation should be taken into consideration in the borrowing/lending dynamic, detailed further below.

Borrowing Spreads and Policy. Inflation, and debt rollovers (e.g. when debt cannot be repaid from existing borrower funds) would be expected to impact bond values (down) and yields (up), but to control this natural tendency, monetary policy is instrumental in bidding up bond values thereby suppressing yields (cost of funds), which means that negative yields are also a possible end result.

This dynamic results in a positive (i.e. reinforcing) feedback look that can engender further borrowing, as detailed briefly in the next section. As borrowings increase, especially for long-term debt, debt servicing requirements* are also expected to increase (assuming no debt restructuring).

It is also recognized that equity market shocks can also trigger a ‘flight to safety’ into bond markets, forcing down yields more than would otherwise be the case and helping to maintain a spread.

Inflation and Borrowing. In the presence of inflation, the real value of the borrowings on the company books depreciates over time, adding to the borrowing spread indirectly This is a form of added return from borrowing via inflation; an inflation return whereby the real value of borrowers’ existing debt outstanding* is reduced. For example, in a 6% inflationary environment, a lender offering nominal rates of 6% is offering a real interest rate of (6%-6%) =0%, or essentially ‘free’ money for firms to generate their respective returns. Although the dynamics are complex, as inflation (expanded currency units) begin to show a positive impact on their financials, firms may be more open to borrowing at nominal rates greater than they would otherwise be willing to borrow, such that the nominal borrowing rate could equal or 14

exceed the nominal natural rate of return (NRR<NRI) at least for a time. Inflation Expectations. If firms expect inflation +x% per annum to continue unabated, they may remain confident in continuing to borrow because of the implicit inflation return on their borrowings from the decline in the value of the debt in real terms; if disinflation or deflation are expected, incentives to borrow may be curbed. Inflation and Money Supply. While an increase in the money supply (currency unit expansion) may be defined as inflation itself, an expansion of currency units is not assumed to automatically correspond to a simultaneous and commensurate rise in consumer, producer and/or asset prices.

Factors such as the velocity of money, policy (e.g. interest on excess reserves IOER) and psychological factors (which may be interrelated with the other factors), are also considered critical.

Debt Bias Feedback Loop As explained in the section on borrowing spreads, if a wide spread is artificially maintained by control over the cost of funds (yields), in conjunction with inflation expectations and the ‘inflation return’, firms may tend to continue accumulating debt over time in a positive feedback loop (self-reinforcing) because of the expected unimpeded profitability of continued borrowing. The policy response to the increased debt loads may be circular because continuously lowered rates ease the debt servicing burden.

Over the long term, this could contribute to systemic risk leading to growing unsustainable debt loads and clustering of defaults (re: banking system crises Sigurjónsson 2015).

In undistorted markets, a timely self-regulating pricing mechanism would be expected to slow the impetus for borrowing via rising borrowing costs. For example, in a rising inflation and cost environment, lenders would likely be more averse to lending funds that will depreciate from inflation unless they can be compensated in the form of a higher interest rate. If policy can keep the COF from rising as much as they would rise in undistorted markets, borrowing/lending can continue unabated without market forces interfering with debt accumulation. A similar logic applies to borrowing by struggling firms, leading to excessive debt burdens (re: high-yield debt, leveraged loans). (re: BIS 2015; Borio and Distayat 2014; Juselius, Borio, et al.

2017) 15

Qualifications. It is recognized that even in distorted market environments, counteracting forces to slow borrowing do exist: Some management teams may consciously attempt to prevent overborrowing by monitoring their debt service coverage limits; moreover, prudent lenders may eventually cease to lend to weak borrowers and monetary policymakers may attempt to adjust their centralized policy rates. However, a persistent concern is delayed timing; in a policy-controlled environment action may tend to be taken more retrospectively, more likely leading to an excessive build-up of debt in the system. Moreover, political considerations can also contribute to further corrective delays.

Interest rates determined in undistorted free credit markets offer a possible solution towards a self-regulating counterbalancing (negative) feedback loop that naturally decelerates borrowing growth in a timely manner. If such cannot exist, then rather than continued lowering of interest rates, a policy of timely debt restructuring may help introduce a counterbalancing mechanism.

Addressing the Feedback Loop. To counteract further debt accumulation and address policy circularity concerns highlighted in the previous section, rather than lowering policy rates to further accommodate debt service, debt restructuring is a possible solution. It is recognized that due to over-borrowing, restructuring debts may be a challenge because debt service capacity of some firms may be severely limited. Note also the role of inflation in extinguishing existing debt balances over time in real terms, provided that further unsustainable borrowing is not encouraged.

NRI (10-year): Alternative Estimate. Based on the microeconomic approach explored here, interest rates (cost of funds) should remain roughly in alignment with natural returns. In fact, for some firms, current 10-year yields may already approximate their hypothesized 10-year natural rate of interest (NRI). However, a conundrum remains: How is a single policy rate compatible with countless disparate natural returns of firms?

Absent free credit markets, interest rate determination would likely result in multiple, decentralized interest rates.

An imperfect solution is to begin with an estimate of rates based on composite natural returns of the firms studied in this report, both unweighted and weighted versions.

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Based on the assumption of the NRI being one-half of the NRR, a hypothetical ‘one-size-fits all’ 10-year NRI is then computed. The firm-specific NRI are also estimated based on theoretical curves for each firm individually. The weighted composite interest rate is derived by weighting the firm-specific interest rates by their relative liabilities to (very roughly) account for their relative impact on credit markets. If there are significant differences between NRRs of the firms and they can be divided into tiers, a weighted value can be derived for upper and lower tiers, respectively. Highly indebted firms may tend to be in the lower tier, have lower natural rates of return and lower estimates of natural interest.

Terminology: Additional Detail. This section defines some of the terms used in the analyses as well as their variations and distinctions of meaning; some definitions may be specific to the framework of this analysis. Rates of Return (“Returns”). Yields, capitalization rates (cap rates), cost of funds (COF) and interest rates may all fall under a broad classification of rates or rates of return, although specifying the context of each term clarifies distinctions. The firm-specific natural rate of return formulation (NRR) is unique as a return measure in that it is the firm’s return on its own cost, (relating their net free cash flows to their costs synchronously). Natural rates of interest (NRI) is a hypothetical negotiated interest rate in a free undistorted market that represents both a hypothetical cost of funds to the borrowing entity and a rate of return to the lender (with a spread). Yields are typically a computed nominal return on an amount invested/lent (re: yield-tomaturity YTM), not the coupon or the stated interest rate on the Note (whether or not securitized). The capitalization rate is a hypothetical rate of return that relates the estimated stabilized income/cash flow generated by the issuer/asset to the amount invested/purchase price (re: income capitalization approach). The current (actual) cost of funds (COF) is the actual rate of return to lenders as well as a cost to the borrower; the lender’s return can be the yield noted above, say for a bondholder. Borrowing Spread or “Spread”. A lending institution typically provides funds with a spread or margin for profit above its own cost of funds. A spread can be thought of as a ‘gap’ between some rate 17

of return measure and a financing cost (re: cost of funds). In the analysis a 10-year term loan with matching amortization is hypothesized; a generic and convenient 10-year cost of funds reference is the 10-year U.S. Treasury bond yield (it is recognized that the COF for individual firms can markedly diverge from this reference yield). Certain factors can contribute to widening or narrowing of spreads including demand and supply of funds, or distortive forces in markets including regulatory changes and inflation, changes in firm outlook and performance, etc.

Distortions (financial and economic) may be caused when for example, spreads that should be narrowing are being prevented from doing so in some way. An unnatural spread allows for profits (i.e. gains that would not exist if the gap were allowed to narrow) that can contribute to excessive speculation and asset bubbles.

Debt Service. Typically, the debt service figure is the current portion of long-term debt (CPLTD). Debt service is assumed to be fixed and is not treated as a stochastic variable although it is recognized that the CPLTD figure can vary within a fiscal year subject to renegotiations, refinancing, early paydowns and payoffs among other factors. Also consider that the debt service figures used may not be comprehensive: There may be certain debts on company balance sheets that are not being amortized (e.g. permanent working capital lines of credit) which should be, depending on the nature of their relationship with lenders. This is an easily overlooked point such that long-term debt service may be significantly understated. Short-term credit lines are expected to be paid off periodically from collected receivables and normally should not be part of debt service. However, it is recognized that some lines of credit that fail to ‘clean up’ (permanent working capital lines of credit) until restructured may not show up as debt service. The reality is that such permanent working capital lines are not uncommon and may highlight a structural problem in the banking system whereby debt that cannot be paid down is left to stagnate in these serially-renewed revolving lines of credit – none of this is defined as long-term debt and until a debt restructuring occurs (if at all), there is little or no principal debt service associated with these outstanding amounts. Capitalization (“Cap”) Rates. To compare current yields in debt markets (e.g.

10-Year U.S. Treasury bond yields) to rates of return in equity 18

markets for each firm, a hypothetical NFCF value is computed.

The most recent fiscal year’s NFCF is adjusted to reflect its 3-year historical shift in spatial centrality as well as for the firm’s revenue growth. The denominator is a figure within the range of recent share prices. Outstanding shares used to compute per share values are typically sourced from the most recent fiscal year-end financial statements. Cap Rates and Cost of Funds. In many cases, the ‘catch-all’ current cost of funds (COF) reference measure is the 10-year U.S. Treasury yield. However, cap rates should also be referred to even though they may diverge markedly from yields; cap rates may better reflect firm-specific risks that are necessarily fully captured in the debt markets and in bond yields. Therefore, cap rates might on a case-by-case basis serve as a proximate cost of funds alternative, particularly if bond yield information is not readily available or if debt securities haven’t been traded recently or that match the approximate maturity or similar bond rating. Neutral Interest Rate. In policy circles, the neutral interest rate is roughly defined as a policy rate that is not excessively expansionary or contractionary, i.e. neutral in its effect on the economy. In the context of this analysis, a neutral rate is undefined because no single rate can fit all firms. See the section at the end titled Natural/Neutral Rate of Interest Alternative which attempts to make estimates for a single hypothetical policy rate based on a fictitious composite of firmspecific natural rates of interest (recall that natural rates are generally referred to in the plural form).

Closing Clarifications: Natural Rates Framework 1. The term "spread" in the context of the framework generally refers to the "borrowing spread" between cost of funds (COF) and natural returns (NRR) of firms; this "borrowing spread" should not be confused with the "yield spread" between the yields of securities with varying maturities.

2. "Policy Rates" in this context typically refer to the U.S. Federal Funds Rate (FFR) and more recently the Interest on Excess Reserves rate (IOER); note that the differences between these two rates can alter lending incentives and money expansion dynamics.

1.1.2. Natural Rates vs. Policy Rates 1: Before Debt Service 19

Summary *Are policy interest rates too low or too high? A microeconomic alternative to official estimates of the natural or neutral rate of interest, using natural rates of return (NRR) of individual firms. *The analysis applies actual firm financial data sourced from F, KO, 3M, and XOM 10-K filings to fiscal year 2018. * Given this framework, a single interest rate is incompatible with diverse firms. An imperfect solution is to estimate natural rates of interest (NRIs) based on composite firm data. *An imperfect solution is to estimate natural rates of interest (NRIs) based on composite firm data. *Estimates of 10-year NRIs range from 2.1% and 7.3% from lower to upper tier firms; overall unweighted and weighted rate estimates are 4.1% and 3.1%, respectively. *Debt restructuring is considered preferable to the continued lowering of interest rates which may reinforce a destabilizing debt-bias feedback loop.

Firm-Specific Rates of Return The framework first posits an inherent or natural rate of return (NRR) of individual business firms that links fundamentally to natural rates of interest (NRI), a hypothetical undistorted market cost of funds (COF) rate. These firm-specific NRR relate net free cash flows of firms to their costs synchronously. NRR are as diverse as the entities which generate them and are estimated for 10-year periods in order to correspond to a hypothetical fullyamortizing 10-year term loan. The nominal NRR shown in Chart 1 next is the nominal firm-specific NRR before debt service. The rate on the y-axis is percent (i.e. 0.10=10%).

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In the chart, all four firms have positive NRR with two apparent tiers: The lower tier in the 3-5% range for F and XOM, and the upper tier for KO and MMM --some 10 percentage points higher.

Sources: U.S. Treasury, SEC filings, author analysis.

Rates of Return Overview Short-term policy rates are expected to exert a certain influence on yields and cost of funds throughout the economy. While it is recognized that yields for debt with longer maturities (e.g. 10years) may not fully reflect policy, nevertheless they are not viewed as representative of undistorted, free-market yields.

Nominal Yields on Debt. Nominal rates of return to lenders are borrowing rates to borrowers with a spread embedded, also referred to here as the cost of funds (COF) for borrowers. A small sample of current borrowing rates was taken of U.S. dollardenominated government and investment grade (IG) corporate bond yields on bonds maturing in 8-10 years, as of early April 2019. The estimated central values of these yields are shown in Chart 2 along with the 10-year Treasury yield. Charts of the sampling distribution may be featured in the Additional Notes section at the end of this report; investment grade is defined as Moody’s A1.

Nominal Yields on Equities. There may be a policy rate crossover effect such that policy rates on debt instruments may exert a certain pull on yields and prices of equities. It is recognized that this rate of return is not a cost of funds of the firms, per se, but it is interesting to compare the difference between rates of returns in debt markets vs. equity markets. Note that in this 21

formulation, the ‘yields’ for equities are not dividend yields (although in some cases there may be similarities); these ‘yields’ refer to capitalization rates (abbreviated ‘cap’ rate or with symbol rc), discussed next.

A hypothetical yield on equities is estimated with a simple income capitalization approach using the classic perpetuity valuation formula rc=π/P where π is an estimate of future ‘stabilized’ net income of the firm, and P is the recent share price. Here, the numerator π is an estimate of future per-share net free cash flow (NFCF) of the firm (for further detail on NFCF, see Part 3 (Firm Level: Pre-emptive Debt Restructuring, or the author’s Financial Distortion Dynamics (2018).

Chart 2 next presents the rate of return measures including estimated cap rates for each firm.

Three of the four firms, KO, MMM and XOM all have estimated cap rates roughly in the vicinity of recent yields on USDdenominated debt instruments. Relative to the other three firms, Ford’s equity cap rate diverges significantly from its USDdenominated debt instruments. Notes: The column for F shows the yield on a bond maturing in just under 11 years (Moody’s Baa3 rating). The columns for KO, MMM and XOM shows the central value from a sample of investment grade (IG) yields maturing within 8-10 years (Moody’s A1 rating). Data were sourced as of early April for KO, MMM, XOM and late April for F. Cap rates 22

may not reflect certain significant recent changes in stock prices (e.g. MMM’s 8-standard deviation move im late April 2019).

Source: markets.businessinsider.com, U.S. Treasury, SEC filings and author computations.

Borrowing Spreads Wider spreads between rates of return and borrowing costs can incent firms to borrow more. This section examines spreads for each of the firms as defined by the difference between the firmspecific NRR and the COF. Chart 3. Nominal Spread on Additional Borrowings. In Chart 1 we saw the nominal natural rates of return (NRR) for each firm.

When considering adding on debt, the spread or difference between the NRR (natural rate of return after current debt service) and the cost of funds for each firm is important because for a given rate of return, borrowings are more profitable the lower the cost of funds. The 10-Year Treasury Yield is used as a generic 10-year cost of funds for this exercise, although significant differences with corporate borrowing rates and even cap rates should be kept in mind. The y-axis shows the spread in percentage points (i.e.

0.10 means a ten-percentage points). The total height of each column is equal to the NRR because NRR = Spread + COF.

All the firms have positive spreads at the current cost of funds level.

Source: U.S. Treasury, SEC filings and author computations.

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Natural Returns and Interest Rates: Breakdown of Components Chart 4 next shows the breakdown of nominal natural returns (NRR) before debt service to illustrate how natural interest rates (NRI) fit into the overall returns framework. Assumption. The NRI is assumed to be simply one-half of the NRR; therefore, when a firm borrows, half of its NRR in percentage terms goes to pay its dividends and principal, and the other half goes to pay interest (i.e. its natural interest or undistorted market cost of funds). In Chart 4, the full length of each column corresponds to the nominal natural rate of return (NRR) and is composed of two major parts: 1. The firm-specific NRI, and 2. the nominal borrowing spread; both are detailed next.

1.Firm-specific natural interest rate (NRI) is the firm’s undistorted free market cost of funds on borrowings and which is also labelled a natural cost of funds. As stated above, the assumption is made that the NRI is half of the NRR. Note that in the chart, the term NRI does not appear because it is composed of two sub-elements: (a) the current actual COF (recall that for this exercise the COF is assumed to be the 10-year U.S. Treasury yield) and (b) the interest rate gap which is the difference between this NRI and the current actual COF. A positive interest rate gap suggests that the current cost of funds is low (i.e. an artificially wide spread), allowing corporate borrowers to benefit from a greater spread on their borrowings than otherwise possible and facilitating a transfer of income from savers and lenders to the borrowers.

To clarify the breakdown: If the NRR is 10%, the NRI is 5% by the assumption above (10%/2); if the current actual cost of funds is 2%, then the interest rate gap is 3% (5%-2%=3%). Therefore, the NRI is the sum of the interest rate gap and the current COF (5%=2%+3%). Note that since NRRs (and NRIs) vary by firm, there are cases where estimated natural interest rates (NRI) for some firms may not differ greatly from current actual rates.

2. Nominal borrowing spread is the difference between the Nominal NRR and the hypothesized natural interest rate (NRI), representing the profit from borrowing in an undistorted market (“market” here meaning an undistorted free-market). Recall that in a free and undistorted market a firm’s natural interest rate would 24

be negotiated and vary according to factors including credit market conditions, the firm-specific natural returns, debt service capacity, the lenders’ desired debt service ratio, borrowing amount and amortization term.

Dynamics of Market Mechanism. An example of rate movements in undistorted market conditions is provided here: If there is a glut of borrowers relative to supply of loanable funds, or borrowers with rising NRRs that allow them to ‘afford’ higher interest rates on their borrowings, the cost of funds is expected to rise in response, and any existing interest rate gap shrinks. What about firms that have little or no interest rate gap now? Rising rates would automatically be an automatic disincentive for them to borrow more funds, or in other words, self-regulating credit rationing. If credit conditions become even more constrained as rising NRIs continue, after any gap disappears nominal borrowing spreads begin to shrink. In the tightest of credit markets, this nominal borrowing spread may shrink to some minimum, with the sum of the natural COF and NRI approaching the NRR. Natural Returns Profiles. In Chart 4 next, that the natural returns profiles vary; little or no interest rate gap may exist given the present cost of funds environment; this means that for some firms, the current, actual policy rates may in fact approximate their hypothetical NRIs.

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Comparing the firms, an interest gap emerges for KO and MMM, while Ford’s hypothetical natural interest rate roughly approximates the current cost of funds; for XOM, the interest gap turns negative because its assumed NRI is less than the current nominal COF of 2.5%. Source: U.S. Treasury, SEC filings and author computations.

Natural Returns after Debt Service Missing from the analysis above is (annual) debt service (i.e. the sum of principal and interest paid throughout the year for debt repayment). Widening borrowing spreads can encourage additional borrowing. If the borrowing spread is artificially maintained such that the nominal cost of funds (COF) does not vary with factors that would normally in undistorted markets serve to narrow the spread (e.g. inflation, or poor individual firm performance causing rising yields/COF) firms may take on too much debt over time. An important factor that also can encourage borrowing is inflation expectations discussed further below.

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Chart 5. NRR after Debt Service reduces the firm’s NRR by current debt service cash outflows (i.e. principal + interest payments). This can be referred to as net NRR (i.e. net of debt service); if a company has no debt service, both NRR and net NRR are identical. Note that net returns can be negative for indebted firms.

After debt service requirements are deducted, NRR after debt service for two of the firms, F and KO turn negative, while XOM’s NRR is reduced by roughly half. Only MMM maintains a substantial return after debt service. Source: SEC filings and author computations.

Debt Bias Feedback Loop As explained in the section on borrowing spreads, if a wide spread is artificially maintained by control over the cost of funds (yields), in conjunction with inflation expectations, firms may tend to 27

continue accumulating debt over time in a positive feedback loop (self-reinforcing) because of the expected unimpeded profitability of continued borrowing. The policy response to the increased debt loads may be circular because continuously lowered rates appear to ease the debt servicing burden. Over the long term, this could contribute to systemic risk leading to growing unsustainable debt loads and clustering of defaults.

In undistorted markets, a timely self-regulating pricing mechanism would be expected to slow the impetus for borrowing via rising borrowing costs. For example, in a rising inflation and cost environment, lenders would likely be more averse to lending funds that will depreciate from inflation unless they can be compensated in the form of a higher interest rate. If policy can keep the COF from rising as much as they would rise in undistorted markets, borrowing/lending can continue unabated without market forces interfering with debt accumulation. A similar logic applies to borrowing by struggling firms, leading to excessive debt burdens (re: high-yield debt, leveraged loans).

Inflation Expectations. If firms expect inflation +x% per annum to continue unabated, they may remain confident in continuing to borrow because of an implicit “inflation return” on their borrowings from the decline in the value of the debt in real terms; if disinflation or deflation are expected, incentives to borrow may be curbed. Qualifications. It is recognized that even in distorted market environments, naturally counteracting forces to slow borrowing do exist: Some management teams may consciously attempt to prevent overborrowing by monitoring their debt service coverage limits closely; moreover, prudent lenders may eventually cease to lend to weak borrowers and monetary policymakers may attempt to adjust their centralized policy rates.

However, a persistent concern is delayed timing; in a policycontrolled environment action may tend to be taken more retrospectively, more likely leading to an excessive build-up of debt in the system. Moreover, political considerations can also contribute to further corrective delays.

Interest rates determined in undistorted free credit markets offer a possible solution towards a self-regulating counterbalancing (negative) feedback loop that naturally decelerates borrowing growth in a timely manner. If natural interest determination is not 28

permitted to exist, then rather than continued lowering of interest rates, a policy of timely debt restructuring may help introduce a counterbalancing mechanism.

Conclusions, Notes and Qualifications Addressing the Feedback Loop. Recall the potential for excessive borrowing caused by artificially wide borrowing spreads, inflation (by extinguishing existing debt balances in real terms over time), and inflation expectations, engendering a debtbias positive feedback loop.

To counteract further debt accumulation and policy circularity concerns highlighted in the previous section, rather than lowering policy rates to further accommodate debt service, timely debt restructuring could offer a less destabilizing solution; it is recognized that restructuring debts may be a challenge because debt service capacity of some firms may be severely limited for some firms.

NRI (10-year): Alternative Estimate. Based on the microeconomic approach explored here, interest rates (cost of funds) should remain roughly in alignment with natural returns. In fact, as previously stated, for some firms, current 10-year yields may already approximate their hypothesized 10-year natural rate of interest (NRI).

However, a conundrum remains: How is a single policy rate compatible with countless disparate natural returns of firms?

Absent centrally controlled credit markets, interest rate determination would likely result in multiple, decentralized interest rates. An imperfect solution is to begin with an estimate of rates based on composite natural returns of the firms studied in this report, both unweighted and weighted versions. Unweighted Composite (all firms). A theoretical composite natural returns curve derived from combining all the firm datasets* is constructed to obtain an estimated central value for the overall NRR. Based on the assumption of the NRI being one-half of the NRR, a hypothetical ‘one-size-fits all’ 10-year NRI is then computed. The NRR central value was estimated at 8.2% and therefore the NRI was computed 4.1% (i.e. 1/2 of 8.2). The x-axis values on the fitted composite NRR curve below are the percentages (i.e.

0.1=10%).

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Firm-Specific NRI. The NRI are also estimated based on theoretical curves for each firm individually as seen in the table below. Weighted Composite (all firms). The weighted interest rate is derived by weighting the firm-specific interest rates by their relative liabilities* to (very roughly) account for their potential relative impact on credit markets. By Tier (weighted). If there are significant differences between NRR of the firms and they can be divided into tiers* a weighted value for upper and lower tiers, respectively, is computed.

(*) Notes: The individual firm datasets consist of 10 observations on natural returns to FY 2018 (i.e. 2018-2009 fiscal year data). The composite curve combines all these datasets for a total of 40 data points (4 firms x 10 observations for each).

Distributions. The resulting composite distribution may have power law attributes; in some cases, the theoretical curves may 30

have no mean or variance. Relative Liabilities. The amount of liabilities can vary greatly by firm. For example, as of Fiscal Year 2018, F and XOM constitute 80% of the total liabilities of the four firms combined; individually, the relative proportion of liabilities is as follows: F 48%, KO 14%, MMM 6%, XOM 32%.

Tiers. KO and MMM are categorized as upper tier, and F and XOM as lower tier. Note that the lower tiers correspond to relatively high indebtedness (80% of total) and a lower estimated interest rate.

Future Trend. The direction of nominal interest rates also depends on shifts in nominal natural returns. See Chart 6 next showing the (spatial) centrality shift of each of the firms over the past three years to fiscal year 2018: All of the firms are in a nearly unchanged or positive range. A zero-shift case is represented by a horizontal line at 1.0.

Cap Rates and Cost of Funds. In this exercise, the ‘catch-all’ current cost of funds (COF) reference measure is the 10-year U.S. Treasury yield. However, in some cases cap rates may more closely reflect firm-specific risks that are not necessarily fully captured in debt markets and in bond yields. Therefore, cap rates might be considered an alternative cost of funds indicator, particularly if bond yield information is not readily available or if debt securities haven’t been traded recently or that match the approximate maturity or similar bond rating.

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Bond Yield Distributions. The charts below are constructed from a small sample of U.S. dollar-denominated investment grade bond yields maturing in 8-10 years as of early April 2019; government bond yield distribution (left) and corporate bond yield distribution (right), respectively:

The central value (spatial) for the yield on 10-year Treasuries was estimated at 2.49 (rounded; identical value for mean, mode, and 32

x(P) where P=0.50). For corporates, the central value estimate for the yield is 3.17 (rounded; mean of 3.22). Comparing the two yields suggests a premium for corporate bonds of approximately 70 basis points over Treasury yields of similar maturities.

Closing Commentary. Regarding mapping these (multiple) natural rates to a policy rate, an allocation problem presents a challenge: If for example, the monetary authorities were to set various tiers of rates (for amounts of credit in rough proportion to the debt outstanding of borrowers) 'queues' may develop as borrowers opt to wait for lower tier rates--otherwise competition may cause rates to be bid up. Meanwhile, because the public sector has no expectation of a positive natural rate of return, special allowances for public sector borrowing as a legally separate category might be necessary [also re: (public) debt monetization].

For additional background information on the history and theory framework, refer to Part 1 (1.1.1. Natural Rates: MicroeconomicFinancial Framework), or the author’s Financial Distortion Dynamics (2018).

1.1.3. Natural Rates vs. Policy Rates 2: After Debt Service Summary *This report is a continuation of the previous analysis (F, KO, MMM and XOM, FY 2018), adjusting for estimated debt service. *In that report a microeconomic alternative to official estimates of the natural or neutral rate of interest was introduced, using natural rates of return NRR of individual firms. * Given this framework, a single (policy) interest rate is incompatible with diverse firms. An imperfect solution is to estimate natural rates of interest based on composite firm data. *However, these findings suggest that once net borrowing spreads turn negative, natural rates of interest cease to function as a guide for rates; as of FY 2018 only MMM shows a positive net borrowing spread. *In conclusion, pre-emptive debt restructuring can restore positive natural returns, financial soundness and offset policy actions that may reinforce a destabilizing debt-bias feedback loop.

Introductory Remarks 33

This extended exercise updates the analysis to account for debt service. The natural rate of return NRR is now reduced by any debt servicing obligations (defined as the sum principal and interest payments).

Recall that the borrowing spread represents the hypothetical profitability of taking on additional debt at a given NRR and cost of funds COF; this report compares a policy-based COF to the cost of funds based on the natural rate of interest (NRI or natural COF).

For uniformity, the policy COF used in the preceding report is kept here at 2.5% although the yield has declined since. The borrowing spread refers to additions to existing debt; existing debt may however be refinanced and rolled into new debt.

The disappearance of a borrowing spread acts as a natural (selfregulating) brake on borrowing based on the firm’s internally generated cash flow. The diminishing cash flow also represents a higher loan risk and thereby a disincentive to lend additional funds.

For an overview of this microeconomic natural returns framework, see Part 1 (Natural Rates: Microeconomic-Financial Framework).

Borrowing Spreads at Policy Rates after Debt Service Chart 6 below updates Chart 3 from the previous report to arrive at the nominal borrowing spread at the policy-based cost of funds after accounting for debt service obligations. It becomes clear that after debt service is deducted, apart from MMM, this net borrowing spread at the policy-based COF ceases to exist.

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Natural Returns Profiles after Debt Service Chart 7 next updates Chart 4, showing the breakdown of the nominal natural rate of return NRR and natural rates of interest NRI, after debt service (i.e. “Net” NRR). Only MMM has a positive borrowing spread given its estimated debt service requirements.

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1.1.3.1 Debt Bias Feedback Loop: Extended Commentary This section continues with possible outcomes stemming from a systemic debt bias. Recall that in a policy-led financing environment, a borrowing spread can be artificially maintained by controls over the (policy) cost of funds. If the spread is kept positive, there is an incentive for firms to continue accumulating debt in a positive (self-reinforcing) feedback loop because of the expected unimpeded profitability of continued borrowing.

However, borrowing spreads after debt service that reach zero essentially mark the end of cash flow-based lending and incentives to borrow or lend based on the company’s selfgenerated cash flow. This point also represents a form of default as defined by cash flow coverage of the firm’s obligations which companies may begin to address through debt restructuring, but

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their efforts could be limited by overriding policy actions to be discussed next.

Policy Response and Priorities. The fundamental issue of excessive debt engendered by a debt bias feedback loop may remain unaddressed as policy priorities are likely to take precedence. Policymakers are faced with strong political pressures and mandates to maintain price stability and responding to demographic trends, notably adequate returns for pension portfolios to mitigate unfunded liability risks; as many pension funds are invested in equities, stock market growth will be given close consideration. Moreover, dividend payments also may be increasingly regarded as a viable supplement to pension/Social Security payments.

These priorities may result in policies designed to consolidate the debt bias: Lenders may be given incentives to continue to finance cash flow-negative firms coupled with a low-yield bond environment that no longer reflects the lending risk involved.

Without a rise in rates reflecting risk and supply-demand conditions in credit markets, funding gaps may emerge if financial institutions are unwilling to absorb additional supply of Treasuries (and other debt securities) at prevailing policy rates. To suppress rising yields, monetary intervention may be required provide liquidity by purchasing the debt during such funding gaps through the repo markets and debt monetization. These monetary actions may also serve to sustain rising asset market prices via the financial industry, particularly if an ample supply of sovereign debt emerges from deficit spending that can be monetized. Central banks may also intervene directly to purchase equities.

Price Impacts. It is often assumed that the immediate impact of a systemic debt bias is price inflation but without specifying which kind: Rather than consumer (producer) price inflation, a more likely outcome may be asset price inflation accompanied by some deflationary characteristics in consumer and producer prices which would be consistent with the above policy objectives.

How does price growth slow with additions of debt to the system?

In addition to loan forbearance and evergreening, additional forms of borrowing (summarized in the next section) may help companies that no longer have adequate internally generated cash 37

flow to continue in operation by replacing cash flow with debt.

Continued production despite cash flow losses allows products to be supplied to the market that may have otherwise been discontinued (due to lower margins) or which would have been subject to price rises. The overall impact of policy is expected to be a general slowing of consumer price growth (or slower than otherwise), in conjunction with declining corporate tax revenues owing to sustained losses. In turn, declining tax revenues may contribute to deficits which may add to the supply of sovereign debt to be monetized. (Note that in an undistorted market, firms can also realize price reductions, but not with the aid of debt as a cash flow replacement, but by pressures to restore cash flow profitability through productivity-enhancing improvements and natural price declines for consumers.)

Post Cash-Flow Financing. In a post cash flow lending environment in the corporate sector, policy priorities can continue to drive a debt-bias dynamic through other forms such as collateral-based lending (CBL) whereby the company pledges collateral in exchange for debt. The collateral types may include the following: Accounts receivable and inventory as a borrowing base (e.g. asset-based lending). In such a case, an adequate loan-to-value LTV ratio must be maintained, and the company must be able to cover the interest costs. Short-term investments. The lender also would have LTV requirements based on net value (net of any loss provisions on those securities), and the capacity to cover interest costs. Treasury Stock. The company borrows for share repurchases and pledges the company’s treasury stock as collateral for the loan(s). This type of borrowing offers the benefit of reducing net cash outflows if the savings from reduced dividend payments exceed the added debt service. An interesting aspect of this form of lending that is tied to debt monetization noted above is that rising stock prices could induce further lending against corporate treasury stock as loan-to-value ratios rise with the stock markets, reinforcing a debtbias positive feedback loop that can drive stock prices significantly higher.

Policy Risks and Possible Outcomes. The integrity of this post cash-flow financing phase requires that the collateral value remain relatively intact and that the debt be rolled over or refinanced.

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However, this policy may initially involve a corporate “zombification” state marked by a slowing of innovative cost reductions and efficiencies coupled with a creeping financial distress. Eventually, impairment of collateral values is likely to be unavoidable, either through outright default or bankruptcy (corporate debt) or resurgence of consumer price inflation (affecting longer-dated sovereign debt). Policymakers may move ahead of bankruptcy to bail out bondholders in the form of selective takeovers or nationalizations.

An inflationary recession marked by tempered consumer price growth, corporate and wage stagnation relative to asset price rises could be sustained for a considerable time.

However, if pre-emptive debt restructuring efforts are not undertaken, eventually the scenario may transform into some form of unstable inflationary-deflationary recession/depression affecting both asset prices (alternating bubbles and crashes) and consumer prices (inflationary tendency). Over the longer term, unsustainable debt loads may result in a clustering of defaults and bankruptcies that trigger equity market and supply shocks coupled with further declines in tax revenues and associated rising budget deficits. Assuming no Black Swan events, the course of such a trajectory may lead to supply interruptions, which when coupled with debt monetization from accelerating deficit financing, may spark higher inflationary pressures into consumer prices. Bond values and yields may be adversely impacted by inflation expectations, leading to rising borrowing costs that at some point cannot be fully suppressed, triggering a further cascading of defaults. As the consequences of systemic debt bias unravel, a semblance of natural rates may eventually reemerge if permitted.

Addendum: Although impacts on the currency are not noted, a weakening (depending on currency pairs and external financing conditions) in response to the scenario as outlined above would also be expected to put upward pressure on import prices, feeding into overall consumer price inflation.

1.1.3.2 Framework Overview and Definitions. This section provides a brief overview of the framework of natural interest rates in contrast with policy interest rates. A table is constructed 39

given the following assumptions (Percentages are written as proportions: i.e. 5% is written as 0.05): Loan Amortization Term (in years): 10 Natural rate of return (NRR) per year: 0.10 Policy-based cost of funds, per year: 0.025 Natural Interest (=Natural cost of funds): 0.05 Debt Service (% of Cost): 0.04

Illustration of Policy-Based and Natural Rate Assumptions Category Policy-Based Natural Natural Rate of Return (NRR) 0.10 0.10 Cost of Funds (COF) 0.025 0.05 Borrowing Spread before debt service 0.075 0.05 Borrowing Spread after debt service 0.035 0.01 Interest or COF Gap n/a 0.025 Summary of Events. In the table above, borrowing remains profitable as both cases show positive borrowing spreads before debt service. Policy rates (cost of funds) are lower than natural rates giving rise to an interest (of COF) gap and making policy rates more profitable for borrowers. Accounting for debt service, the borrowing spread is further reduced in both scenarios but more rapidly approaches zero in the natural rate case.

The following table shows the point at which debt service has doubled to 0.08 (8% of cost) following added debt accumulation:

Illustration of Policy-Based and Natural Rate Assumptions Category Policy-Based Natural Natural Rate of Return (NRR) 0.10 0.10 Cost of Funds (COF) 0.025 0.05 Borrowing Spread before debt service 0.075 0.05 Borrowing Spread after debt service -0.005 -0.03 Interest or COF Gap n/a 0.025 The borrowing spread after debt service falls below zero in both cases. In the policy-based case further lowering of policy rates can artificially “reopen” a borrowing spread to incentivize borrowing.

However, this drives a positive feedback loop that leads to further debt service and repeatedly lower policy rates which may leave central banks as sole lenders even with policy measures to spur lending from other financial institutions. In the natural rates case,

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further borrowing incentives would be expected to emerge from higher natural rates of return NRR, or a reduction of debt service obligations as debt is restructured and/or paid off.

Definitions Loan amortization term is the time for the principal of the loan to be fully amortized.

Natural Rate of Return NRR is defined as the firm's annual net free cash flow NFCF/annual cost; cost is cash basis, cash revenues less cash outflows.

Cost of Funds COF: Policy: Policy rates are influenced by centrally planned monetary policy.

Cost of Funds COF: Natural: This is another name for the natural rates of interest which are microeconomic at the firm-level based on natural rates of return. Natural rates of interest are assumed to be at the midpoint (one-half) of the NRR and viewed as a proxy for a market-based rate for loanable funds.

The borrowing spread is an indicator of the profitability associated with borrowing based on the firm’s internally generated cash flow.

Borrowing spread before debt service is the NRR less the applicable COF. Borrowing spread after debt service is the NRR net of debt service (i.e. Net NRR) less the applicable COF. Debt service figures should be regarded as estimates and may not be uniform across firms due to variations in financial reporting practices.

The interest (or COF) gap is the difference between natural and the policy cost of funds and approaches zero as the policy COF approaches the natural rate.

For additional background information, refer to Part 1 (Natural Rates: Microeconomic-Financial Framework) or the author’s Economic Distortion Dynamics (2017) and Financial Distortion Dynamics (2018). For details on pre-emptive debt restructuring, see Part 3 (Firm Level: Pre-emptive Debt Restructuring).

1.2. Yield and Asset Price Distortion: Equities Summary

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*This report examines asset price and yield distortions employing a microeconomic natural rates framework and alternative set of metrics. *The financial data is sourced from the 10-K filings for selected firms from diverse industries to fiscal year 2019 (DEO, HMC, MSFT, PG, QCOM). *Asset inflation and yield distortion are apparent; increased reliance on collateral and debt monetization may be expected to support added borrowings. *However, a contradiction exists because the value of collateral rests ultimately upon cash flow to provide returns; weak cash flow as a justification for collateral support undermines the value of the collateral from the outset. *Debt restructuring is considered preferable to further debt accumulation that could reinforce a destabilizing debt-bias feedback loop.

1.2.1. DISTORTION INDICATORS Introduction Asset mispricing and yield distortions are explored from a variety of perspectives using the natural rates framework. A fundamental point from which distortions are estimated is the nominal net natural rate of return or Net NRR; “net” refers to after debt service. For further details on definitions, framework and methodology, see Part 1 (Natural Rates: MicroeconomicFinancial Framework).

Chart 1. Excess Stock Price Growth A figure of one signifies that Net NRR is growing in line with nominal stock price growth. “Excess” is defined as greater than 1.

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Conclusion: PG and QCOM have shown the greatest stock price growth relative to their core net natural rates of return (i.e. Net NRR --after debt service). HMC’s lag may reflect weaker forward-looking prospects and investor perceptions. Other indicators below may offer explanations for these values.

Chart 2. Valuations This chart presents an alternative measure of valuation, the Price-Net NFCF multiple. Negative multiples are removed but appear as zero in the chart.

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Conclusion: Only MSFT and QCOM’s multiples remain positive, at moderately elevated values of 40 and 33, respectively.

Notes. Lack of historical data does not yet permit comparison with long-term averages of this multiple.

For comparison of the conventional P-E ratio with this ratio before debt service, see the Appendix (Asset Price Distortion).

Chart 3. Yields Comparison Alongside the dividend yield for each firm is an alternative yield measure referred to as the Net NFCF yield or cap rate, the comprehensive cash flow generated per share relative to the share price.

Conclusion: The Net NFCF yields are negative and are exceeded by dividend yields for three of the five firms, DEO, HMC and PG. Investors may be receiving added yield when the underlying net free cash flow after debt service provides weak support for the dividends. QCOM’s dividend yield is roughly in line with its cap rate, while MSFT’s cap rate exceeds its dividend yield.

Chart 4. Yield Distortion Zero yield distortion means that the firm’s underlying natural return Net NRR is equal to its Net NFCF yield (aka the cap rate) shown in the previous chart. A (positive or negative) yield distortion arises from a difference between the yield and the firm’s 44

underlying return; both variables are “net,” meaning after debt service.

Conclusion: MSFT’s yield is the most distorted downwards (negative distortion) at -25 percentage points, followed by QCOM’s at -10%. HMC, DEO and PG’s yields are distorted upwards (positive distortion). To see what this means in terms of over/undervaluation and asset mispricing, see the next section.

Chart 5. Asset Mispricing The next chart shows mispricing of the equities of each firm (growth of capitalized share value based on their natural returns (Net NRR) relative to actual share price growth). The absence of mispricing is shown at the center of the radar chart at axis=1.

Over(under)valuation correspond to values greater (less) than 1; in overvaluation the lines extend towards the outside boundary of the radar chart. Negative values are not shown but represent overvaluation because share prices exceed or equal zero.

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Conclusion: Among the firms with positive returns, MSFT is most mispriced in terms of overvaluation, followed by QCOM. The other firms have negative returns indicating overvaluation and are removed from the chart.

1.2.2. Distortion Dynamics: Extended Analysis at Firm Level Post Cash-Flow Financing. In the above analysis, three of the firms (DEO, HMC and PG) appear to lack cash flow to service additional borrowings. This section discusses a post cash-flow financing phase that can prolong distortions in prices and yields: Collateral-based lending.

Collateral Lending and Loan-to-Values. In a post cash flow lending environment in the corporate sector, policy priorities can continue to drive a debt-bias via collateral-based lending whereby the company pledges collateral in exchange for debt.

The collateral types may include the following: Accounts receivable and inventory as a borrowing base (e.g. asset-based lending). In such a case, an adequate loan-to-value LTV ratio must be maintained, and the company should be able to cover the interest costs. Short-term investments. The lender also might have LTV requirements based on net value of the collateral (“net” of any loss provisions on those securities), and the capacity to cover interest costs. Treasury Stock. The company can borrow for share repurchases and pledge the company’s treasury stock as 46

collateral for the loan(s). In addition to becoming interest-only if the loans can be continuously rolled over, this type of borrowing offers the benefit of reducing net cash outflows if the savings from reduced dividend payments exceed the added debt service. An interesting aspect of this form of lending that could be tied to debt monetization is that rising stock prices can induce further lending against corporate treasury stock as loan-to-value ratios rise with the stock market; this can reinforce a debt-bias positive feedback loop driving stock prices higher. Collateral types and quality rankings are summarized next for reference: Cash Equivalents (maturity=3 months or less, interest-bearing, highly liquid); fair value is assumed to be close to carrying values; Short-Term Investments (mat>3 months<1 year); Short-Term Investments (mat>1 year) classified as short-term based on "highly liquid nature.” A loss factor is estimated based on unrealized losses on short-term debt investments (re: MSFT FY 2019 financial data).

Fair value level 1: U.S. government securities.

Fair value level 2: Commercial paper, CDs, U.S. agency securities, foreign government bonds, Mortgage-and asset-based securities, corporate notes and bonds, municipal securities.

Fair value level 3: Other corporate notes and bonds, other municipal securities.

Chart 6. Collateral Lending and LTV Comparisons. Two firms, MSFT and PG are hypothetically compared in the next chart. The bars in the left half of the chart are marked “LTV 1” to indicate the loan-to-value ratios based on short-term investments alone (abbreviated S-T investments). LTV2 indicates the loan-to-value ratio based on short-term investments plus a certain percentage of the firm’s treasury stock.

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In the right half of the chart, PG’s loan-to-value ratio is significantly improved by pledging 25% of its treasury stock (aka repurchased shares) as collateral, now coming in line with MSFT’s LTV1.

See the Appendix (Asset Price Distortion) for further detail including definitions, methodology and possible phases of distortion dynamics.

1.3. Debt Monetization Overview Summary *In conjunction with policy rates, debt monetization can contribute to debt accumulation and asset inflations. *This post investigates debt monetization and yield suppression since World War II to gain insight into the outlook for markets, recession and even political shifts. *Monetizing debt initially can absorb heavy bond issuance and temper rising yields but may contribute to secular overborrowing and asset price inflation exacerbating wealth inequality. *An unjust wealth gap can trigger calls for greater redistribution policies rather than addressing the less obvious but foundational problem of debt monetization and yield suppression.

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*To mitigate adverse outcomes, debt restructuring, public spending restraint and progressive removal of capital market and interest rate distortions are proposed.

Debt Monetization (1941-2018) The following chart shows year-to-year per capita changes of monetized U.S. debt since 1941.

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(*) Notes: The y-axis figures are changes in per capita monetized debt from the prior fiscal year (y=1 signifies no growth, y>1 positive growth and y<1 negative growth, respectively).

From 1792 to 1939 there was no recorded monetized debt; the data series begins in 1941 because values are growth rates, i.e. changes year-on-year (e.g. Year 2=1.25, Year 1=1, change is 1.5); figures are on a per capita basis to adjust for population changes over time.

In this discussion, interest rates and yields may be referred to roughly interchangeably; the oversimplification is recognized. If not stated otherwise, ‘interest rates’ can generally be viewed as policy-based (policy interest rates) and may sometimes be abbreviated as ‘rates.’

Sources: usgovernmentdebt.us (series 1: monetized debt -fed; series 2: U.S. population); author analysis.

The central value estimated for the entire period (1941-2018) of 1.0557 per year (rounded to 1.06 or 6% year-on-year growth per capita) is shown by the vertical line in the following fitted distribution for the variable.

Probability Density Function 7.2 6.4 5.6

f(x)

4.8 4 3.2 2.4 1.6 0.8 0 0.8

0.9

1

1.1

1.2

1.3

x Cauchy (0.04294; 1.0557) Note that significant historical outliers have occurred far into the tails and this theoretical curve has no moments. Refer to the tables below for specific examples of extreme values.

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Period Detail. Focusing in on certain key events, we find major spikes during the World War II and during the Great Financial Crisis (GFC) of 2008 and its aftermath. Lesser but notable sustained growth exceeding the historical central estimate of 6% are during the Vietnam War and the Reagan years. This is summarized in the following table: Central Estimates for Selected Events All Years (1941-2018) 1.06 World War II (41-45) 1.52 Vietnam War (62-73) 1.08 Reagan (82-88) 1.09 Great Financial Crisis (2008+) 1.55, 2.03 *GFC estimates for 2009 and 2011, respectively; time periods of events are approximated

It is understood that the years selected for each event may change the results so that the estimates should be viewed as approximate.

Annual Detail. The following tables specify the top and bottom ten years of positive and negative growth, respectively. The assumed relevant period is indicated for proximate reference but may be inaccurate.

Top 10 Growth Years. It is interesting yet disconcerting that the debt monetization growth rates during and following the GFC (2009, 2011, 2013, 2014) are comparable to those of World War II, suggesting heavy absorption of debt. The four instances of relatively high monetized debt growth post GFC could have been a contributor to asset inflation.

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Debt Monetization Growth: Top 10 Years Year 1943 1944 2011 2009 1945 2013 1951 1942 2014 1983

Growth Rate (YoY) 2.67 2.06 2.04 1.55 1.44 1.25 1.23 1.19 1.17 1.15

Period World War II World War II Post GFC GFC World War II Post GFC Korean War World War II Post GFC Reagan

*Per Capita values, Growth Rate is Year on Year (YoY).

GFC= Great Financial Crisis (2008+)

Interest Rate Cuts. Of these top 10 highest growth years in the table above, five (i.e. half) of the years correspond to the top 10 steepest year-over-year interest rate cuts (2009, 2011, 2013, 2014 for 90-day Treasuries, and 1983 for the 10-yr T-bond).

Refer to the Table below.

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Top 10 Steepest Rate Cuts (1963-2017) Year 2009 2008 2011 2002 2014 2001 2003 1992 2013 1971

90-Day 0.11 0.31 0.36 0.47 0.50 0.58 0.63 0.63 0.67 0.68

Year 2012 1986 2008 1998 2001 1993 1971 2015 1985 1983

10-Year 0.65 0.72 0.79 0.83 0.83 0.84 0.84 0.84 0.85 0.85

Rates are year-over-year changes. 90-day

treasuries are constant maturity from 1982; 10-Year U.S. Treasury Bonds are constant maturity for all years.

Source: Federal Reserve historical interest rate data (1963-2017 annual data series); results may vary according to data series used.

Values less than 1 show year-over-year declines (negative growth); value of 1 means no year-over-year change.

Bottom 10 Growth Years. As seen in the table below, the GFC 2008 not only is the worst year on record in terms of (negative) monetized debt growth but also figured among the steepest rate cuts (table above) for both 90-day Treasuries and 10-year bonds: In this data series (1963-2017) only the year 2008 was marked by this anomaly. Speculating as to the reason for this, significant losses and charge-offs on bonds and other debts in that year may have been promptly matched by action to aggressively add liquidity by purchases in debt markets at premium prices and lower yields (re: inverse relationship between yields and prices).

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Debt Monetization Growth: Bottom 10 Years Year 2008 1941 1949 1947 1955 2018 1950 1989 1957 1948

Growth Rate (YoY) 0.62 0.87 0.89 0.91 0.93 0.93 0.94 0.95 0.95 0.96

Period GFC World War II Post-WWII Post-WWII Post-Korea QT Korean War Recession (1990) Recession of '58 Post-WWII

*Per Capita values, Growth Rate is Year on Year (YoY).

GFC= Great Financial Crisis (2008+); QT= quantitative tightening.

Recession Outlook. Recessions may emerge with up to a oneyear lag from a negative growth year (e.g. 1958, 1990, 2008). The policy of quantitative tightening (QT) might in part have been intended to temper frothiness in the stock market, culminating in negative debt monetization growth in 2018 which may have partly contributed to the stock market turmoil in the final months of the year; it remains to be seen whether a recession will emerge from this (with a lag), although policy actions with regards to debt monetization and yield suppression in 2019 and beyond are also likely to figure into the outcome.

Historically, negative per capita debt monetization growth may not always be associated with recessions. The Post-WWII years (1947, 1948, 1949) and post-Korean war period may have been marked by expansions/recoveries with higher economic growth and concomitant tax revenues that helped pay down public debt.

Source: Wikipedia (historical recession periods) Commentary and Qualifications Historical Relationships. It is recognized that there may not necessarily be a clear and consistent observable relationship 54

between debt monetization and specified variables or outcomes.

A few cases are examined here: Impacts of debt monetization on asset inflation, wealth inequality and yields (interest rates). Asset Inflation and Wealth Inequality. Due economic complexity it is not expected that there will always be a consistent and or immediate one-to-one correspondence between rising debt monetization and asset inflation (e.g. stock and real estate markets); moreover, historical debt monetization growth of 6% (rounded) per capita may arguably be inadequate to ignite major asset inflation. However, the cumulative effect of this growth, year after year over many years, combined with certain years or periods with significant spikes of monetized debt (including four in the past 10 years to 2018) could plausibly be expected to contribute to asset inflations of varying degrees, when combined with other factors including political events and investor psychology.

Accordingly, asset inflation may exacerbate unequal wealth distribution particularly between those reliant primarily on income growth vs. those who have primarily accumulated assets that have inflated over time (including via borrowing to acquire those assets). It should be clarified that a Pareto-like wealth distribution pattern may persist even where distortions are absent or less pronounced; the main thrust here is that policy should not exacerbate wealth inequality beyond that of a relatively undistorted economy.

For a recent study on wealth distribution and inequality see Adamou, Berman and Peters (2017).

Yields. Because debt monetization policy may be instrumental in preventing a rise in yields, the available data compares the growth of one variable (the monetized debt) to the absence of growth of another (the interest rate rise that may have been partially prevented by the debt monetization). The following section provides some detail on the history of yields.

Interestingly, based on a theoretical distribution of yields (90-day Treasuries and 10-year bonds, U.S.; 1963-2017 data series), estimates of a spatial centrality indicates a tendency towards invariability in year-on-year yields: For Treasuries (top curve below), the estimate is 0.999, while for the 10-year (bottom curve below), the estimate is 1.0003. This near standstill could reflect an inflexible and controlled policy rate determination, until some 55

event eventually forces a policy shift which can be radical in some cases; some extreme examples are shown below.

Extreme Values. Extreme changes in year-on-year yields correspond to a hypothetical monetary policy tightening or easing, respectively. For Treasuries (top curve below), the extreme high (2015, 6.33) is too far in its right tail to appear in the chart, and the extreme lows occurred during the GFC (2009 0.107 and 2008 0.31; refer back to the table of Top 10 steepest rate cuts above).

For 10-year bond yields changes, the extreme highs were in 2017 (1.34) and 2013 (1.31), both post-GFC (refer to the table below of the Top 10 steepest rate hikes) Probability Density Function 1 0.9 0.8 0.7

f(x)

0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.5

1

1.5

2

2.5

x Dagum (0.72437; 4.1903; 1.1186)

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3

3.5

4

Top 10 Steepest Rate Hikes (1963-2017) Year 2015 2017 2005 2012 1973 2016 2006 1994 1979 1978

90-Day 6.33 3.97 2.30 1.80 1.73 1.68 1.51 1.42 1.40 1.37

Year 2017 2013 1981 1980 1994 1969 1966 1978 1984 1979

10-Year 1.34 1.31 1.22 1.21 1.21 1.18 1.15 1.13 1.12 1.12

Rates are year-over-year changes. 90-day

treasuries are constant maturity from 1982; 10-Year U.S. Treasury Bonds are constant maturity for all years.

Debt Bias Positive Feedback Loop. A positive (self-reinforcing) feedback loop is posited whereby the debt monetization process and interest rate suppression can lead to secular undesirable outcomes. These include wealth inequality, low (interest) returns on savings due (re: financial repression), incentives to overborrow relative to debt service capacity, inflation (both as asset inflation and in rising living costs for citizens) and economic/financial crises. Detail. A simplified conceptual model of the dynamics of this system may involve several approximate steps, detailed here: Political pressures can tend to favor increased public spending over increased taxes, leading to a bias towards debt issuance that exceeds the ability of private debt markets to absorb the debt. Debt monetization absorbs excessive debt by suppressing rising interest rates. This injects new money (‘liquidity’) into the economy that can be used as the basis for purchases of more assets (e.g. stocks, real estate), which can initiate or perpetuate asset price inflation; this, in combination with the lowered returns on savings due to low interest rates (i.e. financial repression), can worsen wealth inequality.

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A parallel dynamic created by the interest rate suppression above lowers the cost of funds for borrowing economy-wide, maintaining an artificially wide borrowing spread* (i.e. unnatural profitability of borrowing). This in turn can encourages financial institutions and banking system to lend more in part towards the acquisition of more assets/investments, thereby further pushing up asset prices (e.g. real estate, stocks, bonds, and capital investment* for businesses). Compounding this is the potential for added inflationary forces via fractional reserve banking and deposit reserves expansion of previously monetized debt). For background on financialization also see Stockman (2013).

At some point the over-investment triggered by false* interest rate/cost of funds signals can lead to an oversupply of goods, falling prices (re: general gluts; Sowell 1973, Hutt 1974), reduced cash inflows, layoffs as part of cost reductions. The debt loads may overshoot the debtors’ debt service capacity resulting in defaults, non-performing debt and plummeting values of debt securities (i.e. impaired debt securities may undergo sudden spikes in yields) and subsequent bank and financial crises. Policy response to unsustainable debt loads may be circular, further contributing to the feedback loop as yields are suppressed from spiking further, and rate cuts are enacted to ease the debt servicing burden. (Curdia, 2019) In business downturns/recessions, lower tax revenues may result in greater debt issuance and monetization.

Notably, the evolving environment can become simultaneously inflationary and depressed (re: inflationary depression) marked by increasing volatility in equity markets, with melt-ups followed by crashes.* Adding to this, political solutions will be offered to increase public spending --requiring further debt monetization and yield suppression--and perpetuating this process. Over the long term, the dynamics become unstable and eventually unsustainable as the build-up of systemic risk may be insurmountable for traditional policy responses to handle. *Notes: 1. This borrowing spread can include the following definitions: (a) The difference in the nominal rate of return (e.g. total returns on assets) and the cost of funds to borrow (including to acquire said assets); (b) The difference between the natural rates of return of firms (NRRs) and the cost of funds which can encourage further unwarranted debt accumulation. For additional 58

background information on natural interest rates (borrowing spreads) refer to Part 1 (Natural Rates: Microeconomic-Financial Framework) or the author’s Financial Distortion Dynamics (2018). (This definition of ‘borrowing spread’ here should not be confused with the spread on yields of varying maturities). 2. Asset acquisition by businesses (and perhaps some individuals) can also include investment in capital equipment (i.e. capital expenditures).

3. The term 'false' is defined as not reflecting the reality of actual costs and benefits in the economy--a distortive signal. 4. The impact of collateralization of debt obligations or emergency asset sales within and across markets can play a role in crashes; for example, after covenant default or insufficient margin an investment firm with a failing debt-financed securities portfolio must liquidate other assets to meet its obligations to its creditors.

Truncating the Debt Bias Feedback Loop. A proximate* solution centers on three areas: (1) Debt restructuring. To counteract further debt-asset accumulation and the policy circularity problem as noted above, rather than lowering policy rates to further accommodate debt service, timely debt restructuring could offer a less destabilizing solution; it is recognized that restructuring debts may be a challenge because debt service capacity of some firms may be severely limited for some firms.

(2) Public spending restraint. Budget management to avoid unleashing too much public debt onto the bond markets that can only be absorbed through debt monetization. This restraint slows the amount of newly created money to acquire further assets and can reduce interest rate distortion and secular inflation. See Christ (1979) regarding the government budget constraint.

(3) Self-Regulating Markets. By progressively removing distortions in capital markets to allow for interest rate fluidity (re: natural interest rates) amid changing environments and among individual entities (e.g. businesses), a timely self-regulating pricing mechanism can emerge that can slow the impetus for borrowing via an economic process of rising borrowing costs (cost of funds).* These higher rates naturally create a disincentive for excessive additional borrowing. Removal of distortions would likely need to be a measured, long-term process. (*) Explanatory Notes 59

1. Shortcoming. This three-part solution is labelled ‘proximate’ because there is little inherent incentive within existing political frameworks to move in such a direction even if the outcomes are deemed desirable. 2. Tax Hikes and Deficits. It can be argued that the problem of government deficits can be easily ‘solved’ by raising taxes to finance increased government spending. The problem with this logic is that politically there may be limited resistance to increased spending while there are probably limits to the ability of governments to tax their citizens before political pushback occurs. The reason is that existing and intended beneficiaries of public largesse are likely to favor politicians and policies who deliver the benefits, while at the same time taxpayers (those who must pay for the largesse) are likely to push in the opposite direction. The result is a likelihood of government budget deficits financed by the central bank (debt monetization). A backdoor approach to taxation is via inflation: Any secular inflationary impacts of money creation can be viewed as an alternative form of taxation (i.e. ‘inflation tax’) that may wind up as a default ‘solution’ if the impacts are not as obvious and can generally be passed onto the citizens over time if not too noticeable.

Closing Comments: Debt Monetization 1. Spreads that may be heavily influenced by policy actions include yield spreads (borrowing short-term to lend longer-term) and cross-currency spreads (e.g. borrowing in JPY to invest in higher-yielding USD denominated debt); policy that prevents spreads from naturally closing (obstructing self-regulation) can contribute to a build-up of excesses and subsequent dislocations/crises in multiple markets.

2. Although well-intentioned and politically popular, redistributionist policies aiming to narrow the wealth gap may produce the opposite effect due to the positive feedback loop as described above that tends to result in further asset inflation. 3. On Pareto and the "power law" formula in the context of wealth see Edward Thorp's "A Man for All Markets" (2017), Chapter 23.

3. Historical note. The data do not reflect lending to the U.S. Treasury by the 1st and 2nd Bank of the United States, most

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notably, provision of credit for the War of 1812. (re: Stephen Girard 1750-1831) PART 2. FIRM LEVEL ANALYSIS BY SECTOR 2 Introduction Part 2 presents the results and analyses for the period covering the firms’ fiscal years ending in 2018 and 2019. These reports were originally published online under two main headlines: Dividend Coverage Roundup (DCR) and Cautionary Stress Testing (CST). The cash flow-focused reports (see section 2.3 here) were part of the Dividend Coverage Roundup reports. Here, the former is referred to as Financing Obligations Coverage, and the latter is abbreviated Stress Testing. Financing obligations are defined as the sum of dividends and debt service, which is viewed as a more comprehensive and accurate way of evaluating dividend coverage since both dividends and debt service compete for the same net free cash flow.

2.1.1. Sample and Groupings. The 29 firms studied are grouped into four sectors and ten industries; “industry” may also be referred to as “subsector.” Market capitalization of the firms is US$5T as of early 2020 for the 29 firms combined. There are five stress tests for the FY 2018-19 period, corresponding to five industries.

For further detail on the industry data and sample methodology, see the Appendix (Firm Level Analysis). A summary of the groupings is provided below: Basic Materials Sector (2 subsectors, 6 firms) Oil & gas production SIC 1311 Petroleum Refining SIC 2911 Consumer Goods Sector (3 subsectors, 8 firms) Auto Makers SIC 3711 Beverages SIC 2080, 2086 Household & Personal Products SIC 2840 Stress Testing 61

Healthcare & Industrial Sector (2 subsectors, 6 firms) Pharmaceutical SIC 2384 Stress Testing Diversified (Med/Ind) Equipment SIC 3821, 3841, 3861 Stress Testing Technology Sector (3 subsectors, 9 firms) Semiconductors SIC 3674, 3663 Stress Testing Software SIC 7371, 7372 Telecommunications equipment SIC 4813 Stress Testing 2.1.2. Tabular Results and Interpretation Each industry report begins with a table summarizing the results that are also shown in the subsequent charts. To avoid redundancy, the following section provides the interpretation of the values presented in the table.

Financing Obligations Coverage: Interpretation of Values Dividend Coverage (Chart 1): The coverage variable is NFCF (net free cash flow as a percentage of annual company revenues).

Positive (negative) values suggest enough (inadequate) cushion to cover dividends. A figure of zero suggests breakeven coverage.

Note that dividend coverage may not be comparable since dividends paid can vary considerably across firms, including negligible amounts.

Deviations 1-Year (Charts 2 & 3: NFCF, Revenue Growth; latest FY vs. prior): Negative values suggest a worsening over the past year because the latest fiscal year’s value has moved below the prior period central tendency. Zero or near-zero values suggest that the latest values are in line with historical.

Shifting Centrality (Charts 4 & 5: NFCF, Revenue Growth; 3year): Here the baseline for a shift=1, not zero. Smaller values suggest secular deterioration. This category identifies any shift of each company’s own “core” performance over the latest 3 years.

Coverage 2 (Chart 6: Dividends+Debt Service or DDS): Positive (negative) values suggest an adequate (inadequate) 62

cushion to cover DDS. The coverage variable NFCF is compared to the sum of dividends and debt service* (abbreviated DDS or d+ and may also be termed financing obligations). (*) Definitions: Reference to “cash flow” as an abbreviation is intended to mean net free cash flow NFCF unless otherwise specified. Revenue growth is expressed as a YoY (year-overyear) percentage change. Debt service is the sum of principal and interest payments (which may be estimated). Performance. The basis for this analysis is spatial, where the central tendency or core performance may also be referred to as “spatial centrality” or simply centrality. Negative signs do not indicate ‘negative’ space but are added to highlight that the estimated area defines a shortfall.

Stress Testing Stress Tests specify scenarios of varying severity to identify a criticality level, i.e. extent to which a scenario could become a reality. For example, in the case of maximum criticality the specified scenario generates a critical zone estimate of 1; this means that the scenario corresponds to all possible outcomes (i.e.

100% or 1) and therefore in theory describes the closest possible to an actual outcome. In contrast, a critical zone value of zero corresponds to no possible outcome (in theory an impossibility); note that the language “in theory” underscores that although quantitatively derived, the critical zone estimates should be regarded as theoretical. The results of stress testing should not be equated with predictions or forecasting; at best they can be viewed as imprecise indicators of the vulnerability of firms to adverse impacts. Applicability. Firm-level stress testing may aid in assessing vulnerabilities within financial institutions (loans), capital markets and investment portfolios (securities). Moreover, criticality levels may offer insights as to the prospects for economic recession and policy countermeasures. Shocks experienced by individual firms may exert impacts on capital markets, financial institution credit portfolios and economic output.

2.1.3. Default Assumption for Theoretical Models

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For more recent analyses, a default assumption of fat tails is generally made in favor of higher-ranking distributions for reasons detailed in the methodology; See the Appendix (Financing Obligations Coverage: Methodology, and Stress Testing: Methodology).

2.2 Financing Obligations Coverage & Stress Testing 2.2.1 Basic Materials Sector (2 subsectors, 6 firms) 2.2.1.1 Oil & gas production SIC 1311 Summary *This report examines the capacity of oil and gas producers to cover dividends and debt service to FY 2018. *The firms reviewed are CNQ, PTR, and SSL (SIC 1311) with a combined market cap of about $140B. *Apart from SSL, net free cash flow and revenue growth range between breakeven to positive; also see qualifications. *Sustained coverage of financing obligations may rely on some form of monetary easing due to debt loads.

Tabular Results The table below shows the results for Canadian Natural Resources (CNQ) and PetroChina (PTR) to FYE 12/31/18 and Sasol (SSL) to FYE 6/30/18: Charts Category Variables CNQ PTR SSL 1 Coverage 1 Dividends -0.21 -0.16 0.13 2 Deviations NFCF 0.44 0.34 -0.39 3 Rev Growth 0.22 0.22 -0.03 4 Shift NFCF 1.02 1.00 0.95 5 Rev Growth 1.03 1.00 0.95 6 Coverage 2 Debt Service + Divs -0.38 -0.45 -0.30 Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts

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The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. Dividend coverage may not be comparable across firms as dividends may vary significantly; for some firms the amounts can be negligible. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and qualifications below.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. Apart from SSL, the most recent fiscal year, net free cash flow NFCF exceeds the firms’ own prior centrality.

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REVG. As of the most recent fiscal year, revenue growth for CNQ and PTR exceeds their historical prior centrality while SSL dipped slightly below breakeven.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. To the most recent fiscal year, net free cash flow either remains above or in line with its own historical centrality for CNQ and PTR, while SSL’s net free cash flow has shifted downwards over the 3-year period.

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REVG. CNQ and PTR have slightly exceeded or maintained their core of revenue growth, while SSL has shifted downwards to a greater extent over the period to its most recent fiscal year end.

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service, detailed in this section. Debt Service is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds, with debt service typically being contractual. The 67

chart below shows the relative performance of the firms when coverage is estimated for dividends and debt service combined; the centrality is represented by zero. All firms appear to fall considerably short in coverage capacity; see note and qualifications below for further details.

Note: CNQ’s current portion of long-term debt as of FY 2018 was significantly lower than for its scheduled future maturities beyond FY 2018: In particular, debt maturities of 1476 for FY 2018 rise to 5996 for FY 2019. To ignore this large discrepancy in the subsequent fiscal year arguably understates the extent of the company’s debt obligations. Therefore, an intermediate estimate averaging the two fiscal years’ debt maturity figures (FY 2018 and FY 2019) was used instead for a more representative debt service figure.

Qualifications 1. Chart 1. Dividend coverage may not be comparable across firms: For example, the ratio of dividends to revenues for CNQ, PTR and SSL as of FY 2018 was approximately 7%, 1.8% and nearly zero, respectively.

2. The basis for comparison between firms is less uniform due to the 6-month lag between the fiscal year ends of SSL and the other two firms. Also note that the financial

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information for SSL is for FY 2018, not FY 2019 (see next comment regarding the delay).

3. SSL’s financial statements are typically released in August. For fiscal year ending 6/30/19, there has been a delay as stated in the company’s SEC Form 6-K Report of Foreign Private Issuer. According to the report, the financial statements for FY 2019 are expected to be released by the end of October 2019.

4. See the Note with Chart 6 regarding CNQ’s current portion estimate.

5. Due to the author’s incomplete dataset for CNQ, there was a single missing observation for FY 2007 revenue growth. Therefore, an estimate was generated from the average of all subsequent fiscal years to FY 2018.

2.2.1.2. Petroleum Refining SIC 2911 Summary *This report addresses the underlying capacity of firms to cover their dividend payments. *Petroleum refiners (SIC 2911) CVX, EQNR, and XOM, with a combined market cap of approximately $600B, are reviewed. *The analysis draws from annual company data to fiscal year 2018, finding signs of positive net free cash flow and revenue growth. *However, dividend and debt service coverage points to overindebtedness, suggesting further lowering of policy interest rates.

Results and Interpretation. The table below shows the results for Chevron, Equinor (formerly Statoil) and Exxon for each category of the coverage analysis:

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Charts 1 2 3 4 5 6

Category Variables Coverage 1 Dividends Deviations NFCF Rev Growth Shift NFCF Rev Growth Coverage 2 Debt Service + Divs

CVX -0.22 0.37 0.19 1.00 1.03 -0.33

EQNR -0.28 0.38 0.39 1.01 1.04 -0.35

XOM -0.31 0.29 0.30 1.00 1.01 -0.51

Dividend Coverage: The coverage variable applied is net free cash flow NFCF (as a percentage of annual company revenues).

Positive (negative) values suggest enough (inadequate) cushion to cover dividends. A figure of zero suggests breakeven coverage.

Deviations (NFCF, Revenue Growth*; latest FY vs. prior): Negative values suggest a recent (1-year) worsening. Negative values suggest that the latest fiscal year’s value has moved away from the prior period central tendency. * Zero or near-zero values suggest that the latest values are in line with historical.

Shifting Centrality (NFCF, Revenue Growth; 3-year): Here the baseline for a shift is not zero, but 1 and smaller values suggest secular deterioration. This category identifies any shift of each company’s own “core” performance (i.e. its own central tendency*) over the latest 3 years.

Coverage 2 (Dividends+Debt Service): Positive (negative) values suggest an adequate (inadequate) cushion to cover DDS. Just as in Dividend Coverage above, the coverage variable is net free cash flow/revenues. Debt service, which is the sum of both principal and interest payments, is now added together with dividends; this combined figure may be abbreviated DDS or d+. (*) Notes: Revenue growth is year on year percentage change. The basis for this analysis is spatial, where the central tendency may also be referred to as “spatial centrality” or simply centrality; see the Appendices for details.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

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Chart 1. Dividend Coverage. All the firms appear to have inadequate coverage for dividends based on the historical estimate of NFCF centrality.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. In the most recent fiscal year, net free cash flow NFCF of all firms suggest positive deviations from historical centrality of the prior period.

REVG. As of the most recent fiscal year, all firms also show positive deviations in revenue growth.

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Charts 4 and 5. Shifting Centrality (3-year) NFCF. Net free cash flow has moved very slightly positive for all firms to varying degrees.

REVG. Over the 3-year period examined, all three firms' revenue growth centralities have shifted upwards.

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Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service, detailed in this section.

Debt Service is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds, with debt service typically being contractual.

The chart below shows the relative performance of the firms when coverage is estimated for dividends and debt service combined; the centrality is represented by zero. All firms appear to fall short in coverage capacity.

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Methodology Detail Data. The data is sourced directly from annual company financial statements (typically 10-K or 20-F).

Sample. Unless otherwise specified, the sample for each variable consists of ten observations corresponding to the company’s fiscal years (e.g. FY 2018-2009). Each dataset is a 10-year set of observations: For example, if FY 2018 is the most recent (current) fiscal year, then the current or latest period refers to the 10-year dataset comprised of the observations for FY 2018 back to FY 2009; the prior period refers to the 10-year dataset spanning FY 2017-2008 with the prior fiscal year being FY 2017.

Company Selection. If more than one company is analyzed, they may be grouped either by 1. industry/SIC code, or 2. according to their proximity to the release of financial information (the latter causes firms from different industries to be reviewed together but is intended to keep the analysis as current as possible). Many of the firms are selected from the original database of firms surveyed in Equity Income Analytics (2014), an overview by the author of 32 global non-financial firms in 20 sub-sectors. The primary goal of sample selection was to diversify geographically and by subsector; audited financial information was also a key criterion.

Coverage Analysis. A common measure to gauge a firm’s underlying dividend capacity is dividend cover, which compares net income to dividends. An alternative is to use free cash flow in 74

place of net income which is accrual-based. This dividend coverage report aims for a more expansive view of the dynamics underlying dividend coverage along with a more rigorous treatment of cash flow. The approach adopted here has the following main features: Net Free Cash Flow. Traditional free cash flow is reduced to adjusted to account for various types of corporate acquisitions; this more conservative cash flow variable is referred to as net free cash flow NFCF (which may alternatively be referred to as equity income EI by the author). Spatial Dimension. The behavior of the variables is expressed by the theoretical area or “space” that they occupy; the data are organized in terms of the relative frequency of their occurrence over the time period examined (typically 10 fiscal years); these results are then fitted to theoretical density curves that form the basis for an estimate of a central tendency of the historical data called “spatial centrality” or abbreviated centrality, detailed next. (Spatial) Centrality is estimated for each 10-year dataset from the sample data and refers to an estimated value where the area occupied to both the left and the right of the point is exactly equal (50% each).

For the analysis of deviations (Charts 2 and 3), the term “prior” refers to the centrality estimate of the prior fiscal year based on the 10-year dataset up to and including that fiscal year.

It is recognized that other indicators of central tendency such as arithmetic averages could be used in analysis. However, this is not the approach adopted in these reports because an examination of the stochastic properties of the key variables suggests that in many cases there is no average. Even if an average does exist, limiting the study to averages may critically fail to capture the extent of risks, particularly where fat tails are concerned; see the next section for details.

Shortcomings and Qualifications Relative Risk Profiles and Ranking. Ranking the firms according to measures of performance is generally avoided as such an exercise may be of very limited value if not misleading.

Some general observations: 1. A greater number of combined negative factors from the table above could suggest greater risk.

For example, suppose that ABC Co. and XYZ Co. have identical estimates for coverage of debt service + dividends; weaker 75

revenue growth for XYZ Co. may raise its risk profile relative to ABC Co. 2. Static vs. dynamic view: Assessing relative performance of firms may differ greatly between performance over a single period versus over many periods. 3. The best relative performer does not necessarily merit a positive evaluation in an absolute sense as all the firms may be faced with major challenges.

4. Any performance ranking also may depend critically on the weight placed on each factor which may be at least in part subjective. 5. This analysis overlooks intricacies of each firm and industry that can be gleaned from coverage by specialized analysts.

6. Comparisons between firms in different industries subject to distinct environmental factors and phases in business cycles may lead to inappropriate conclusions about relative performance; even companies in the same industry may not be entirely comparable.

Debt service is computed only for the upcoming fiscal year. Using the example of fiscal year 2018 financial statements, the debt service amount refers to the sum of: interest expense in the most current year’s income statement (i.e. FY 2018), and the current maturities due within the coming year as shown in the FY 2018 balance sheet. However, debt maturities typically extend out many years, and there may be a substantial amount of debt maturing in a fiscal year a few years hence that is not accounted for in the debt service figure used for this analysis, thus arguably understating debt service coverage capacity.

Role of Political Economy. Interest costs may vary according to the firm’s loan origination location. Firms may be over-indebted due to a prior policy rate environment that incentivized them to borrow heavily. While inadequate debt service coverage may be alarming, the firms’ recurring cash flow may still be strong and stable. Moreover, future political factors cannot be discounted: In recessions and amidst cascading loan defaults, political pressures can easily lead to an easing of the debt servicing burden through monetary policy and lower policy interest rates. In turn, higher inflation that nominally raises company revenues (nominally but not in real terms) can later develop. Insofar as debt repayment amounts are fixed, these higher nominal revenues coupled with cost-cutting can eventually mean improved debt service capacity over the longer term as the economy recovers.

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Preemptive Debt Restructuring. While avoiding excessive debt should be a priority, if an over-borrowing situation can be identified early enough, management can undertake a preemptive debt restructuring which can include equity issuance to pay down debt, spin-offs and asset sales and renegotiation of loan terms: For additional details see Part 3 (Firm Level: Pre-emptive Debt Restructuring).

Theoretical models upon which the results of this study are based are at best rough estimates and a subjective approximation of the financial processes. Most of the highest-ranking fitted curves appear to be sub-exponential and devoid of moments, rather than normal (Gaussian); crucially, many fits suggest fat-tailed distributions. If in fact the “true” distribution is fat-tailed, very high-impact extreme negative or positive events may be far more likely to occur. This poses a dilemma illustrated by the two sideby-side fitted curves below (thin tailed Normal at left and fattailed Cauchy at right). These two curves are different theorized versions of the same “reality”: A petroleum refiner’s NFCF/Revenues during its fiscal years from 2008 to 2017.

Suppose the Normal curve at left above is found to be the “best” fit according to certain testing procedures, and the Cauchy a lower-ranked alternative based on the same testing. Should the fattailed curve be disregarded as the “true” representation of reality?

We cannot be sure that the fat-tailed distribution should be categorically rejected in favor of the Normal. If we reject the assumption of fat-tailed, a dangerous underestimation of the company’s risks is possible. For example, consider a potentially catastrophic event such as a cash flow loss worse than negative 25% of revenues: Under fat tails, this scenario occupies a nontrivial space of approximately 3% of the total area under the curve 77

(located in the left tail). In contrast, the area under a Normal curve is estimated at nearly zero—i.e. virtually non-existent or impossible. In conclusion, to err on the side of caution, it may be more prudent to assume fat tails.

Fat tails or not, even if the models accurately capture reality and sample sizes are adequate (they are usually not), much of realworld phenomena are expected to be distributed in ways that are essentially non-computable. Therefore, any predictive value for purposes of forecasting should not be assumed. Accordingly, while security-level analysis is invaluable, it may be best supported by a strategy of optionality (e.g. buying equity puts).

For a better exposition of some of these points, see Cook, John D. (2018: “Six Sigma Events,” May 31; “Obesity Index” April 23, also citing Cooke, Roger, Fat-Tailed Distributions 2014), and studies by Taleb, Nassim, et al. (2020, 2012) to cite some examples.

2.2.2. Consumer Goods Sector (3 subsectors, 8 firms) 2.2.2.1. Auto Makers SIC 3711 Summary *This report addresses the underlying capacity of firms to cover their dividend payments, drawing from annual data to the fiscal year ended 3/31/2019. *Auto makers (SIC 3711) F, HMC, and TTM, with a combined market cap of approximately $92B, are reviewed. *Results are mixed as to impacts of an economic downturn; TTM’s recent trends may be more idiosyncratic. *All firms have heavy debt loads from an overall coverage standpoint; this could eventually spur action to lower policy rates to provide debt service relief.

Tabular Results The table below shows the results for Ford to FYE 12/31/18, Honda to FYE 3/31/19 and Tata Motors to FYE 3/31/19.

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Charts 1 2 3 4 5 6

Category Variables Coverage 1 Dividends Deviations NFCF Rev Growth Shift (3-yr) NFCF Rev Growth Coverage 2 Debt Service + Divs

F 0.40 0.01 -0.03 1.00 0.99 -0.49

HMC 0.10 -0.19 -0.11 1.00 1.01 -0.46

TTM n/a -0.43 -0.28 1.00 0.96 -0.37

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. Ford has positive dividend coverage (shown as a column). TTM is left blank because their dividends are negligible or zero. For coverage of dividends and debt service together, see Chart 6.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. In the most recent fiscal year, net free cash flow NFCF has roughly been within a central and low F and HMC, while TTM has diverged significantly from its prior centrality.

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REVG. As of the most recent fiscal year, all firms show declines in revenue growth to varying degrees.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. Over a 3-year period to the most recent fiscal year, net free cash flow is roughly within range of its centrality (note that the finer scale makes the divergence look greater).

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REVG. Over the latest 3-year period, F and HMC have roughly maintained within their core of revenue growth, while TTM has shifted downwards.

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service, detailed in this section. Debt Service is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds, with debt service typically being contractual. The chart below shows the relative performance of the firms when coverage is estimated for dividends and debt service combined; the centrality is represented by zero. All firms appear to fall considerably short in coverage capacity; of the three firms, F diverges the furthest downward.

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A default assumption of fat tails is generally made in favor of higher-ranking distributions for reasons detailed in the Appendix (Financing Obligations Coverage: Methodology).

2.2.2.2. Beverages SIC 2080, 2086 Summary *This report examines the capacity of beverage industry firms to cover their dividend payments, drawing from annual data to FYE 6/30/2019. *Firms reviewed are DEO, FMX, and KO (SIC 2080.2086) with a combined market cap of approximately $500B. *Cash flow management is overall positive, while recent revenue growth trends may signal economic slowdown, subject to qualifications noted. *All firms are heavily indebted which coupled with possible recession could spur policy rate cuts to provide debt service relief.

Tabular Results The table below shows the results for Diageo (DEO) to FYE 6/30/19, Fomento Económico Mexicano dba FEMSA (FMX) and Coca Cola (KO) to FYE 12/31/18:

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Charts Category Variables DEO FMX 1 Coverage 1 Dividends 0.25 0.08 2 Deviations NFCF 0.27 0.09 3 Rev Growth -0.09 -0.38 4 Shift NFCF 1.07 1.00 5 Rev Growth 1.01 1.00 6 Coverage 2 Debt Service + Divs -0.43 -0.35 Note: For interpretation of the results in the above table, the Introduction to Part 2.

KO -0.43 0.27 -0.34 1.00 0.95 -0.48 refer to

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. Although differences exist between the firms with DEO the apparent leader in coverage, the estimates here do not give a complete picture of the coverage of financing obligations: For a more comprehensive estimate (dividends and debt service together), see Chart 6.

Chart 2 and 3. Deviations (from Prior FY Centrality).

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NFCF. In the most recent fiscal year, net free cash flow NFCF exceeds the firms’ own prior centrality.

REVG. As of the most recent fiscal year, revenue growth for all firms still falls short of their historical prior centrality although DEO has fallen the least.

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Charts 4 and 5. Shifting Centrality (3-year) NFCF. Over a 3-year period to the most recent fiscal year, net free cash flow either remains above or in line with its own historical centrality.

REVG. Over the latest 3-year period, DEO and FMX have kept close to their core of revenue growth, while KO has shifted downwards to a greater extent.

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Note: The estimated revenue growth shift for KO of 0.95 above was left unchanged from the previous dividend coverage analysis of KO. However, the figure improves to 0.98 based on the default assumption of fat tails; refer to Appendix (Financing Obligations Coverage: Methodology) for details.

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service, detailed in this section. Debt Service is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds, with debt service typically being contractual. The chart below shows the relative performance of the firms when coverage is estimated for dividends and debt service combined; the centrality is represented by zero. All firms appear to fall considerably short in coverage capacity.

Note on DEO: The FY 2018 analysis of DEO’s DDS coverage was based on a model* that has since been updated to the default assumption of fat tails: The updated FYE 6/30/2018 result for DEO is -0.42 (compared to -0.43 above) which suggests that coverage is virtually unchanged over the two fiscal years. (*) The 86

previous highest-ranking model resulted in an invalid “off-thescale” extreme result for dividends + debt service combined; thus, an arbitrary figure of -0.99 was devised.

Qualifications. 1. Although all firms can be categorized as “beverages,” differences between the firms should be recognized as indicated in part by their SIC codes (2080 for DEO and KO, 2086 for FMX).

2. Due to similarities in product lines, the revenue trends noted for KO and FMX may not stem entirely from economic conditions. 3.

The basis for comparison between firms is less uniform due to the 6-month lag between the fiscal year ends of DEO and the other firms. 4. Notes regarding the model differences that may have affected (a) revenue growth results for KO and (b) DDS coverage results for DEO, are provided with Charts 5 and 6, respectively.

Methodology and other details are consolidated in the Appendix (Financing Obligations Coverage: Methodology).

2.2.2.3. Household & Personal Products SIC 2840 Summary *This report examines coverage capacity of household & personal products industry firms PG and UN (SIC #2840), with a combined market cap of about $500 billion. *Most recent FYE estimates suggest weakened cash flow (PG) and revenue growth (UN); both firms face similarly high levels of financing obligations. *Stress testing analysis confirms revenue growth concerns for UN, while cash flow issues may be roughly similar for both firms; also see notes and qualifications. *Policy is expected to be more accommodative to assist firms with similar financing burdens.

Tabular Results The table below shows the results for UN to FYE 12/31/18 and PG to FYE 6/30/19:

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Charts 1 2 3 4 5 6

Category Coverage 1 Deviations Shift Coverage 2

Variables Dividends NFCF Rev Growth NFCF Rev Growth Debt Service + Divs

PG 0.23 -0.33 0.09 0.983 1.004 -0.46

UN -0.22 0.28 -0.33 1.003 1.003 -0.46

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. The chart gives the impression that PG’s dividend coverage is superior. However, this can be misleading given that UN’s ratio of dividend payments to debt service is 1.5x that of PG’s. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications below.

Chart 2 and 3. Deviations (from Prior FY Centrality).

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NFCF. Net free cash flow relative to prior centrality suggests that PG has experienced recent cash flow pressures in its most recent fiscal year.

REVG. As of their most recent fiscal year ends, PG’s revenue growth showed a bounce relative to its historical prior centrality while UN’s results may reflect a recent revenue downturn.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. To the most recent fiscal year, core net free cash flow for UN remains roughly in line with its own historical (3-year) centrality, while PG’s has declined.

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REVG. Both firms slightly exceed their core revenue growth over the 3-year period to their most recent fiscal year ends (note the fine scale of the chart).

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service which is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds. The chart below shows the relative performance of the firms when coverage 90

is estimated for dividends and debt service combined; the centrality is represented by zero.

The firms’ overall financing obligations coverage is estimated to be virtually identical and substantially negative; also see any notes and qualifications below.

Qualifications 1. Business Models. While the firms are classified within the same SIC code (SIC 2840), there may still be significant differences between product types and markets.

2. Time Differences. The basis for comparison between firms lacks uniformity due to a 6-month time lag between the fiscal year-ends of the firms (12/31/18 for UN, and 6/30/19 for PG).

3. Dividends. As noted along with Chart 1, focusing on dividend coverage alone suggests that UN falls short in this category of analysis. However, because of the significant difference between the estimated ratio of dividend payments to debt service (0.73 for PG and 1.06 for UN), overall financing obligations as shown in Chart 6 better clarifies the relationship.

4. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be regarded as estimates and may not be perfectly 91

comparable across firms due to variations in financial reporting.

2.2.2.3.1. Stress Testing: Household & Personal Products SIC 2840 Introduction. The stress tests specify scenarios of varying severity to identify a criticality level, i.e. degree to which the severe outcome could become a reality. The tests estimate a “critical zone” (area) for each scenario: In theory, the larger this value, the greater the cause for concern. The range of critical zone values is zero to one with values in between shown as proportions (e.g. 0.015, 0.30, 0.56). The variables in the scenarios are NFCF/Revenues (also which may be abbreviated as NFCF) and revenue growth REVG. Each scenario occurs within a single fiscal year. For further details, the Appendix (Stress Testing: Methodology).

Micro to markets linkage. This analysis may aid in assessing overall market vulnerability as shocks experienced by individual firms may have increased impact on the overall stock and bond markets the larger their market capitalization relative to the overall market.

Tabular results of the estimated critical zones are shown below, with brief interpretations provided alongside the charts further below.

Charts Scenarios PG UN 1 NFCF/Revs<0% 0.041 0.054 2 NFCF/Revs < -0.25% 0.014 0.012 3 Rev Growth <0% 0.338 0.548 4 Rev Growth <-25% 0.019 0.047 Note: NFCF is net free cash flow; NFCF/revenues may also be abbreviated NFCF. Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service); this is a potential marker for

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insolvency. The estimates for the firms suggest a slightly higher criticality level for UN.

CHART 2 NFCF/Revs < -0.25. This scenario posits a drop in annual company net free cash flow below 25%. The estimates are similar for both firms.

CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. While the estimates for both companies suggest elevated criticality, UN’s recent fiscal 93

year revenue performance may have contributed to a relatively higher estimate.

CHART 4 Rev Growth <-25%. In this scenario, revenue growth drops by more 25% in one fiscal year. The critical zone is reasonably contained for PG at just under 2%; as noted above, the recent decline in UN’s revenue growth has likely contributed to a notably higher criticality.

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Also refer to the Appendix (Financing Obligations Coverage: Methodology).

2.2.3. Healthcare & Industrial Sector (2 subsectors, 6 firms) 2.2.3.1 Pharmaceutical SIC 2384 Summary *This report examines the coverage capacity of pharmaceutical industry firms PFE, SNY and TEVA (SIC 2834), with a combined market cap of about $300 billion. *Most recent FYE estimates suggest cash flow, coverage (SNY) and revenue growth (TEVA) concerns; also see any qualifications and separate Cautionary Stress Testing reports. *Policy is expected to be more accommodative to assist firms with similar financing burdens.

Tabular Results The table below shows the results for Pfizer (PFE), Sanofi (SNY) and Teva Pharmaceuticals (TEVA) to FYE 12/31/18: Charts Category 1 2 3 4 5 6

Coverage 1 Deviations Shift Coverage 2

Variables Dividends NFCF Rev Growth NFCF Rev Growth Debt Service + Divs

PFE

SNY

TEVA

0.38 -0.09 0.25 0.999 0.998 -0.01

0.34 -0.48 -0.33 0.989 1.023 -0.28

0.36 -0.14 -0.43 0.979 0.948 -0.09

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. The similarity in dividend coverage in the following chart can be misleading due to relative 95

differences in dividend payments to debt service. For example, TEVA’s dividends are negligible and therefore its ratio is nearly zero. PFE and SNY offer a closer comparison, as their ratio is 1.31 and 1.21, respectively. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications below.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. Net free cash flow relative to prior centrality suggests that SNY has experienced more severe recent cash flow pressures in its most recent fiscal year.

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REVG. As of their most recent fiscal year ends, PFE’s revenue growth showed a bounce relative to its historical prior centrality while the other firms’ results may reflect a recent revenue downturn.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. To the most recent fiscal year, core net free cash flow for PFE remains roughly in line with its own historical (3-year) centrality, while the other two firms have suffered declines.

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REVG. Both PFE and SNY equal or slightly exceed their core revenue growth over the 3-year period to their most recent fiscal year ends (note the fine scale of the chart) while TEVA has moved downward.

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage involves adding debt service onto dividends. This may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds. Debt service is defined as the sum of 98

principal and interest on debt obligations. Dividends and debt service combined may also be termed financing obligations. In the chart below, centrality is represented by zero.

SNY’s financing obligations coverage capacity is estimated to have dropped significantly below the other two firms; however, recall that TEVA’s dividend payments are negligible. Also see any notes and qualifications below.

Qualifications 1. Business Models. While the firms are classified within the same SIC code (SIC 2834), there may still be significant differences between product types and markets.

For example, TEVA may be described as a specialty drug manufacturer.

2. Time Period. The financial data is from fiscal year 2018 which does not reflect current conditions.

3. Coverage Uniformity. As noted along with Chart 1, due to the significant difference between the estimated ratio of dividend payments to debt service, overall financing obligations as shown in Chart 6 may better clarify the relationship. However, a uniform basis for comparison may still be lacking: For example, firm A’s coverage can appear superior to Firm B’s only because of Firm B’s negligible or zero dividend payments.

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4. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be regarded as estimates and may not be perfectly comparable across firms due to variations in financial reporting.

Also refer to the Appendix (Financing Obligations Coverage: Methodology).

2.2.3.1.1. Stress Testing: Pharmaceutical Industry to FY 2018 Summary *Stress tests are run on pharmaceutical industry firms PFE, SNY and TEVA (SIC 2834), with a combined market cap of about $300 billion. *Firm-level stress testing may offer further insights into the prospects for dividends, recession, policy and financial institution default exposure. *Scenario estimates suggest greater vulnerability in cash flow (TEVA) and revenues (PFE); also see any qualifications.

Tabular results of the estimated critical zones for each scenario are shown below, with brief interpretations provided alongside the charts further below.

Charts Scenarios PFE SNY TEVA 1 NFCF/Revs<0% 0.054 0.058 0.142 2 NFCF/Revs < -0.25% 0.028 0.023 0.050 3 Rev Growth <0% 0.605 0.231 0.276 4 Rev Growth <-25% 0.061 0.037 0.077 Note: NFCF is net free cash flow; NFCF/revenues may also be abbreviated NFCF. Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service) --a potential marker for insolvency.

The estimates suggest a substantially higher criticality level for TEVA while the others are closely aligned.

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CHART 2 NFCF/Revs < -0.25%. This assumed shock is a drop in annual net free cash flow below 25%. This scenario suggests some flattening, although TEVA maintains the highest criticality.

CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. Notably, PFE’s estimate for a negative revenue growth scenario substantially exceeds that of the other firms at 0.61 (rounded).

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CHART 4 Rev Growth <-25%. In this more severe revenue scenario, there is some flattening across firms.

Also refer to the Appendix (Stress Testing: Methodology).

2.2.3.2. Diversified (Med/Ind) Equipment SIC 3821, 3841, 3861 Summary

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*This report examines the coverage capacity of diversified equipment makers CAJ, MMM and TMO (SIC 3800s), to FY 2018, with a combined market cap of approximately $250 billion. *Most recent FYE estimates suggest issues in overall coverage (MMM) and revenue growth (CAJ); also see any qualifications and the separate Cautionary Stress Testing reports. *Future policy is expected to be more accommodative to assist firms with financing burdens.

Tabular Results The table below shows the results for Canon (CAJ) SIC 3861, 3M (MMM) SIC 3841, and Thermo Fisher Scientific (TMO), SIC 3829 to FYE 12/31/18: Charts Category 1 2 3 4 5 6

Coverage 1 Deviations Shift Coverage 2

Variables Dividends NFCF Rev Growth NFCF Rev Growth Debt Service + Divs

CAJ

MMM

TMO

0.26 -0.29 -0.42 1.001 0.998 0.01

0.20 0.35 0.08 1.020 1.001 -0.21

0.36 0.31 0.44 1.024 1.000 0.15

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. The similarity in dividend coverage in the following chart can be misleading due to relative differences in dividend payments to debt service. For example, MMM’s annual dividends are estimated at twice its debt service, CAJ about 1.7 times, and TMO only 0.14 times. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications.

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Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. Net free cash flow relative to prior centrality suggests that CAJ has experienced more severe recent cash flow pressures in its most recent fiscal year.

REVG. As of the most recent fiscal year end, CAJ has also seen slowing revenue growth relative to its historical prior centrality while the other firms’ results are roughly breakeven (MMM) or positive (TMO).

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Charts 4 and 5. Shifting Centrality (3-year) NFCF. Core net free cash flow remains roughly aligned with, or exceeding, historical (3-year) centrality for all three firms.

REVG. On a 3-year basis, CAJ has seen a shift downwards in its core revenue growth while MMM and TMO are almost unchanged or slightly positive (note the fine scale of the chart).

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Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage involves adding debt service onto dividends. This may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds. Debt service is defined as the sum of principal and interest on debt obligations. Dividends and debt service combined may also be termed financing obligations. In the chart below, centrality is represented by zero.

MMM’s financing obligations coverage capacity is estimated to be weaker than the other two firms; however. Also see any notes and qualifications below.

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Qualifications 1. Business Models. The firms are classified within the same 3800 SIC code block (3829, 3841 and 3861) but there are still significant differences between product types and markets. “Equipment” is broadly defined to include various types of devices and instruments. TMO may be more closely described as a health care company although all have a medical-health care component of their business.

2. Time Period. The financial data is from fiscal year 2018 which does not reflect current conditions.

3. Coverage Uniformity. As noted along with Chart 1, due to the significant difference between the estimated ratio of dividend payments to debt service, overall financing obligations as shown in Chart 6 may better clarify the relationship. However, a uniform basis for comparison may still be lacking: For example, firm A’s coverage can appear superior to Firm B’s only because of Firm B’s negligible or zero dividend payments.

4. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be regarded as estimates and may not be perfectly comparable across firms due to variations in financial reporting. Underreporting. The current portion of long-term debt (due within 1 year) can be underrepresented relative to the amount of subsequent debt maturities. This appeared to be the case with all three firms. Therefore, in order to improve uniformity for purposes of comparison, the average of debt maturities for four fiscal years (FY 2019 to 2022) was computed and used to estimate the coverage for all firms. Had this adjustment not been made, the debt service coverage show in Chart 6 above would have improved to 0.20 for CAJ, -.10 for MMM and .25 for TMO. Also refer to the Appendix (Financing Obligations Coverage: Methodology).

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2.2.3.2.1. Stress Testing: Diversified Equipment to FY 2018 Summary *Stress tests are run on diversified equipment makers CAJ, MMM and TMO (SIC 3800s), with a combined market cap of about $250 billion. *Firm-level stress testing may offer further insights into the prospects for dividends, recession, policy and financial institution default exposure. *Scenario estimates suggest a higher criticality level in cash flow (TMO, CAJ) and revenues (CAJ); also see any qualifications and analysis of the firms in the Financing Obligations Coverage report.

Tabular results of the estimated critical zones for each scenario are shown below, with brief interpretations provided alongside the charts further below.

Charts Scenarios CAJ MMM TMO 1 NFCF/Revs<0% 0.111 0.081 0.133 2 NFCF/Revs < -0.25% 0.026 0.027 0.043 3 Rev Growth <0% 0.572 0.258 0.075 4 Rev Growth <-25% 0.068 0.033 0.017 Note: NFCF/revenues may also be abbreviated NFCF (net free cash flow).

Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service) --a potential marker for insolvency.

The estimates suggest greater vulnerability for TMO and CAJ.

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CHART 2 NFCF/Revs < -0.25%. This assumed shock is a drop in annual net free cash flow below 25%. Here, TMO stands out in terms of criticality.

CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. CAJ’s estimate for negative revenue growth scenario substantially exceeds that of the other firms.

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CHART 4 Rev Growth <-25%. In this more severe revenue scenario, CAJ remains more exposed.

Qualifications. A question arises as to why the cash flow results for TMO appear to differ in the reports (i.e. elevated cash flow criticality in this Stress Testing vs. relatively strong net free cash flow history per the Financing Obligations Coverage). As noted elsewhere, the analyses are based on fitted distributions; in the case of TMO, the continuous scale parameter of the fitted curve 110

for NFCF is estimated to be considerably larger than for the other two firms, as follows (rounded): CAJ 0.03, MMM 0.03, TMO 0.05.

Also refer to the Appendix (Stress Testing: Methodology).

2.2.4. Technology Sector (3 subsectors, 9 firms) 2.2.4.1. Semiconductors SIC 3674, 3663 Summary *This report explores the coverage capacity of semiconductor industry firms INTC, QCOM and TSM (SIC 3674 & 3663) with a combined market cap of approximately $600 billion. *Most recent FYE estimates suggest overall strength (INTC), coverage and revenue growth concerns with improved cash flow (TSM); however, note the significant time lag between the firms’ fiscal year ends. *Also see other qualifications and the separate Cautionary Stress Testing reports for the firms.

Tabular Results The table below shows the results for Intel (INTC) to FYE 12/31/18, Qualcomm (QCOM) to 9/29/19, and Taiwan Semiconductor Manufacturing (TSM) to FYE 12/31/18: Charts Category 1 2 3 4 5 6

Coverage 1 Deviations Shift Coverage 2

Variables

INTC

QCOM

TSM

Dividends NFCF Rev Growth NFCF Rev Growth Debt Service + Divs

0.39 0.27 0.38 1.003 1.035 0.32

0.34 0.12 0.22 1.011 1.005 0.03

-0.01 0.22 -0.18 1.020 1.002 -0.17

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts

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The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. The firms appear to cover their dividends with a positive margin (INTC, QCOM) or at roughly breakeven (TSM). The similarity in dividend coverage in the following chart can be misleading due to relative differences in dividend payments to debt service as well as reporting discrepancies. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. A positive recent FY trend in cash flow is apparent for all firms.

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REVG. As of the most recent fiscal year end, TSM has seen the greatest decline in revenue growth relative to its historical prior centrality.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. Core net free cash flow remains above historical (3-year) centrality for all firms, with TSM showing the strongest trend.

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REVG. On a 3-year basis, INTC remains above its core revenue growth while the other two firms are approaching breakeven; (note the fine scale of the chart).

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage involves adding debt service onto dividends. This may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds. Debt service is defined as the sum of 114

principal and interest on debt obligations. Dividends and debt service combined may also be termed financing obligations. In the chart below, centrality is represented by zero.

Based on the most representative value identifiable for debt service, INTC coverage margin appears to be the greatest, while QCOM is approaching breakeven and TSM appears to fall short; Also see any notes and qualifications below.

Qualifications 1. Business Models. The firms are not classified under the same SIC code (3674 for INTC and TSM and 3663 for QCOM) although QCOM can be described as a semiconductor industry firm; QCOM’s SIC code (radio & TV broadcasting/communications equipment) may reflect a focus on certain elements of past and current production.

2. Time Differences. The basis for comparison between firms lacks uniformity due to a 9-month time lag between the fiscal year-ends of the firms: 12/31/18 for INTC and TSM, and 9/29/19 for QCOM. 3. Coverage Uniformity. As noted along with Chart 1, due to the significant difference between dividend payments relative to debt service obligations, coverage of overall financing obligations as shown in Chart 6 may be more 115

informative. However, a uniform basis for comparison may still be lacking: For example, firm A’s coverage can appear superior to Firm B’s only because of Firm B’s negligible or zero dividend payments. The ratio of dividends to debt service (also see the next comment) for INTC, QCOM and TSM is estimated at 2.0, 1.1. and 5.5, respectively.

4. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be regarded as estimates and may not be comparable across firms due to variations in financial reporting. For INTC and QCOM, figures deemed approximately representative of debt service obligations were used based on reported current and future debt maturities. However, for TSM, future debt maturities data was not found in the FY 2018 financial statements or Notes in the time allotted.

5. Financial Audits. The auditors highlight several Critical Audit Matters for QCOM. Related party transactions are noted for TSM. These issues may or may not significantly alter the presentation of financial information and any conclusions drawn.

Also refer to the Appendix (Financing Obligations Coverage: Methodology).

2.2.4.1.1. Stress Testing: Semiconductor Industry FY 2018-19 Summary *Stress tests are run on semiconductor firms INTC, QCOM and TSM (SIC 3764 and 3663), with a combined market cap of approximately $600 billion. *Firm-level stress testing may offer further insights into the prospects for dividends, recession, policy and financial institution default exposure. *Scenario estimates suggest higher criticality in cash flow (QCOM, TSM) and revenues (INTC, QCOM); also see any qualifications and the Financing Obligations Coverage report.

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Tabular results of the estimated critical zones for each scenario are shown below, with brief interpretations provided alongside the charts further below.

Charts Scenarios INTC QCOM TSM 1 NFCF/Revs<0% 0.064 0.085 0.103 2 NFCF/Revs < -0.25% 0.026 0.042 0.047 3 Rev Growth <0% 0.300 0.351 0.200 4 Rev Growth <-25% 0.063 0.080 0.069 Note: NFCF/revenues may also be abbreviated NFCF (net free cash flow).

Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service) --a potential marker for insolvency.

The estimates suggest greatest exposure for TSM, and moderately high levels for INTC and QCOM.

CHART 2 NFCF/Revs < -0.25%. This assumed shock is a drop in annual net free cash flow below 25%. QCOM and TSM are in a higher range than INTC.

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CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. INTC and QCOM’s estimates suggest elevated vulnerability to a revenue shock.

CHART 4 Rev Growth <-25%. In this more severe revenue shock scenario, QCOM remains more vulnerable than INTC and TSM.

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Qualifications. Incompatible results may be apparent across reports (this Stress Testing vs. the Financing Obligations Coverage FOC). The analyses are based on fitted curves where stress testing accounts for both the centrality estimates (that are employed in the FOC) and scale parameters (estimates of dispersion). For further detail see the next section.

Scale Parameter Estimates. INTC is lowest in terms of estimated criticality (both for NFCF/Revs and Revenue growth): For the NFCF/Revs variable, this scale parameter estimate is 0.035, 0.064, and 0.067, for INTC, QCOM and TSM, respectively. For revenue growth, the estimates are 0.059, 0.074 and 0.079 for the firms, respectively. Centrality Estimates. QCOM’s higher estimate of centrality may somewhat offset its scale parameter estimate to yield a lower relative criticality for NFCF/Revs. These NFCF/Revs centrality estimates are 0.17, 0.23, and 0.20 for INTC, QCOM and TSM, respectively. QCOM’s higher, and TSM’s lower, vulnerability to a revenue growth shock may be in part explained by their respective centrality estimates; the revenue growth estimates are 0.06, 0.04 and 0.11 for the INTC, QCOM and TSM, respectively. (*Note that the ‘centrality estimates’ refer to the estimates of location parameters).

Also refer to the Appendix (Stress Testing: Methodology).

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2.2.4.2. Software SIC 7371, 7372 Summary *This report examines the capacity of global software firms to cover dividends and debt service to FY 2019. *The firms reviewed are INFY (SIC 7371), MSFT and SAP (SIC 7372) with a combined market cap of about $1.2 trillion. *MSFT appears to exhibit strength in all categories including coverage. *As of their fiscal year ends, debt service capacity concerns are not apparent for the firms; also see notes and qualifications.

Tabular Results The table below shows the results for SAP to FYE 12/31/18, Infosys to FYE 3/31/19 and Microsoft to FYE 6/30/19: Charts 1 2 3 4 5 6

Category Variables Coverage 1 Dividends Deviations NFCF Rev Growth Shift NFCF Rev Growth Coverage 2 Debt Service + Divs

INFY -0.02 -0.17 0.00 0.994 0.986 -0.43

MXFT 0.44 0.21 0.40 1.014 1.006 0.41

SAP 0.26 -0.31 -0.24 1.142 0.950 0.12

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

Chart 1. Dividend Coverage. The firms show coverage within close range of, or exceeding, breakeven. Also see note below.

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Note: Dividend coverage may not be comparable across firms as dividends can vary significantly. For example, as a percentage of revenues, INFY pays a more generous dividend of 17% compared to MSFT and SAP’s 11% and 7% (rounded), respectively. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications below.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. In the most recent fiscal year, net free cash flow NFCF relative to prior centrality for INFY and SAP suggests recent cash flow management pressures.

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REVG. As of the most recent fiscal year, revenue growth for INFY and MSFT equals or exceeds their historical prior centrality while SAP may be encountering revenue pressures.

Charts 4 and 5. Shifting Centrality (3-year) NFCF. To the most recent fiscal year, “core” net free cash flow either remains above or in line with each firm’s own historical (3year) centrality.

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REVG. MSFT has slightly exceeded its core of revenue growth, while INFY and SAP have shifted downwards to varying degrees over the 3-year period to their most recent fiscal year ends.

Chart 6. Dividends + Debt Service A more comprehensive analysis of coverage requires examination of debt service, detailed in this section. Debt Service is the sum of principal and interest on debt obligations. Adding debt service onto dividends may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds, with debt service typically being contractual. The 123

chart below shows the relative performance of the firms when coverage is estimated for dividends and debt service combined; the centrality is represented by zero.

MSFT and SAP’s coverage is estimated to be positive. Rather than due solely to debt service, INFY’s relatively higher proportion of dividend payments contributes to the coverage shortfall in this category; also see any notes and qualifications below.

Note: Debt service as a percentage of revenues for INFY, MSFT and SAP were 7%, 7% and 5% (rounded), respectively.

Qualifications 1. Business Models. The basis for comparisons between firms is less uniform due to differences in the core businesses. While all three firms can be described as of the software industry, INFY’s classification is in technical and system software (SIC 7371) whereas MSFT and SAP are classified as prepackaged computer software (SIC 7372).

2. Time Differences. The basis for comparison between firms lacks uniformity due to 3- or 6-month time lags between the fiscal year-ends of the firms.

3. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be 124

regarded as estimates and may not be perfectly comparable across firms due to variations in financial reporting.

Also refer to the Appendix (Financing Obligations Coverage: Methodology).

2.2.4.3. Telecommunications equipment SIC 4813 Summary *This report examines the coverage capacity of the global telecom firms CHL, T and VIV (SIC 4813), with a combined market cap of approximately $450 billion. *Most recent FYE estimates suggest issues in overall coverage (T, VIV) and revenue growth (CHL, VIV); also see any qualifications and the separate Cautionary Stress Testing reports. *Future policy is expected to be more accommodating to assist firms with financing burdens.

Tabular Results The table below shows the results for China Mobile (CHL), AT&T (T), and Telefonica Brasil (VIV) to FYE 12/31/18: Charts Category 1 2 3 4 5 6

Coverage 1 Deviations Shift Coverage 2

Variables

CHL

T

VIV

Dividends NFCF Rev Growth NFCF Rev Growth Debt Service + Divs

0.15 -0.39 -0.41 0.933 0.984 0.15

0.18 -0.91 0.40 1.001 0.997 -0.42

0.13 -0.33 -0.17 0.988 0.959 -0.39

Note: For interpretation of the results in the above table, refer to the Introduction to Part 2.

Comparative Overview in Charts The following charts display the data from the table above. Note that spatial centrality is also shown either by a zero (for Charts 1, 2, 3 and 6), or the figure 1 (for Charts 4 and 5).

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Chart 1. Dividend Coverage. All the firms appear to cover their dividends with a positive margin. The similarity in dividend coverage in the following chart can be misleading due to relative differences in dividend payments to debt service. For a more comprehensive estimate of financing obligations (dividends and debt service together), see Chart 6 and any qualifications.

Chart 2 and 3. Deviations (from Prior FY Centrality).

NFCF. T’s net free cash flow dropped precipitously relative to prior centrality in connection with significant acquisitions activity in the most recent fiscal year 2018. Recall that NFCF accounts for cash outflows from acquisitions, which free cash flow does not; see the note below the chart regarding adjustments.

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Note: Adjustment. To obtain a roughly “normalized” figure for NFCF for AT&T in FY 2018, a long-term annual average* of its acquisitions was computed; this raises the company’s NFCF/revenues to a positive 0.08 (rounded) in FY 2018 and the estimated deviation from prior centrality decreases from -0.91 to -.16. *this average was drawn from the fiscal years 2008-2018, inclusive.

REVG. As of the most recent fiscal year end, CHL and VIV have experienced slowing revenue growth while T has exceeded its historical prior centrality.

Charts 4 and 5. Shifting Centrality (3-year) 127

NFCF. Core net free cash flow remains roughly in the range of historical (3-year) centrality for T and VIV, while CHL seems to have suffered a drop-off in NFCF over the period.

REVG. On a 3-year basis, VIV has experienced the greatest downwards shift its core revenue growth while the other two firms are either slightly down (CHL) or near-breakeven (T); (note the fine scale of the chart).

Chart 6. Dividends + Debt Service

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A more comprehensive analysis of coverage involves adding debt service onto dividends. This may result in significantly different conclusions about dividend coverage capacity since both compete for limited company funds. Debt service is defined as the sum of principal and interest on debt obligations. Dividends and debt service combined may also be termed financing obligations. In the chart below, centrality is represented by zero.

CHL’s financing obligations coverage capacity appears to be far superior to the other two firms. Recall that CHL has virtually no debt service other than interest payments; Also see any notes and qualifications below.

Qualifications 1. Business Models. The firms are classified within the same SIC code (4813) but there may be significant differences between product types and markets.

2. Time Period. The financial data is from fiscal year 2018 which does not reflect current conditions.

3. Coverage Uniformity. As noted along with Chart 1, due to the significant difference between the estimated ratio of dividend payments to debt service, overall financing obligations as shown in Chart 6 may better clarify the relationship. However, a uniform basis for comparison may still be lacking: For example, firm A’s coverage 129

can appear superior to Firm B’s only because of Firm B’s negligible or zero dividend payments.

4. Debt Service Discrepancies. The figures used for debt service (principal repayments plus interest) should be regarded as estimates and may not be perfectly comparable across firms due to variations in financial reporting.

5. Related Party Transactions. The auditors note the presence of related party transactions for CHL which may or may not significantly alter the presentation of financial information and any conclusions drawn.

Also refer to the Appendix (Financing Obligations Coverage: Methodology).

2.2.4.3.1. Stress Testing: Telecommunications Equipment Summary *Stress tests are run on global telecom firms CHL, T and VIV (SIC 4813), with a combined market cap of $450 billion. *Firm-level stress testing may offer further insights into the prospects for dividends, recession, policy and financial institution default exposure. *Scenario estimates suggest higher criticality in cash flow (CHL) and revenues (VIV); also see any qualifications and the Financing Obligations Coverage report.

Tabular results of the estimated critical zones for each scenario are shown below, with brief interpretations provided alongside the charts further below.

Charts Scenarios 1 NFCF/Revs<0% 2 NFCF/Revs < -0.25% 3 Rev Growth <0% 4 Rev Growth <-25% Note: NFCF/revenues may also cash flow).

CHL T 0.144 0.081 0.046 0.022 0.115 0.283 0.026 0.021 be abbreviated NFCF

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VIV 0.076 0.023 0.342 0.038 (net free

Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service) --a potential marker for insolvency.

The estimates suggest greatest vulnerability for CHL, and moderately high levels for T and VIV.

CHART 2 NFCF/Revs < -0.25%. This assumed shock is a drop in annual net free cash flow below 25%. CHL remains at approximately double the level of criticality.

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CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. VIV and T’s estimates suggest elevated vulnerability to a revenue shock.

CHART 4 Rev Growth <-25%. In this more severe revenue shock scenario, VIV remains more vulnerable than the other firms.

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Qualifications. A question arises as to the possibility of incompatible results across reports (this Stress Testing vs. the Financing Obligations Coverage). As noted elsewhere, the analyses are based on fitted curves; the continuous scale parameters (estimates of dispersion) may help explain discrepancies. For example, in this stress testing, CHL (NFCF/Revs) and VIV (Revenue growth) are the standouts in terms of criticality: For the NFCF/Revs variable, this scale parameter estimate is 0.05, 0.024, and 0.025, for CHL, T and VIV, respectively; for revenue growth, the estimates are 0.026, 0.017 and 0.032 for the firms, respectively.

Also refer to the Appendix (Stress Testing: Methodology).

2.3. Cash Flow Analysis 2.3.1. Cash Flow Analysis Mixed Industries 1 Financing Obligations Coverage: Cash Flows to FY 2018 DEO, MSFT, PG, QCOM Summary * *This report focuses on financing activity cash flows for four firms in diverse industries: DEO, MSFT, PG and QCOM.

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*The movement of cash can give a broader picture of the underlying dynamics of firms and their coverage capacity. *The data is sourced from the most recent annual financial statements of the firms, all released in the last half of 2018.

Introduction The focus of this analysis is on the inflows and outflows of cash beginning with net free cash flow (NFCF), centering on six major variables labelled in the table below as Net Free Cash Flow, Dividends, Debt Issuance, Stock Issuance, Debt Service and Share Buybacks.

Note that there are other variables that are potential sources of cash flow (including drawing upon cash itself) that are excluded from the table and charts below, but that may be looked at separately if there appear to be significant unidentified sources of cash.

Variables Detail The variables are expressed as a proportion of revenues: For example, the label “Dividends” is the dividend amount paid in FY 2018 divided by revenues in FY 2018. The variables are 3-year averages, unless otherwise indicated.

Net Free Cash Flow (NFCF): NFCF is the measure of the company’s internally-generated net free cash flow that best reflects the net cash available to equity holders of the firm and to service debt. The figure can be positive, zero or negative.

Note that this variable may alternatively be called equity income (EI).

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Dividends should be negative if the company pays dividends, otherwise zero.

Debt Issuance: This figure can be negative, zero or positive depending on how much the company has been paying down debt (an outflow of cash) relative to raising cash through added debt.

Note that this figure may be computed from the change in total liabilities rather than the cash flow statement.

Stock Issuance: This figure is typically positive if the company is issuing new shares, or zero if no shares are being issued.

Debt Service: This figure is the sum of principal and interest (P+i) and should be negative, or if there is no debt to service, zero.

Note that debt service may be at best an approximation. First, the interest figure is often taken from the income statement (interest expense) for expediency; this is not necessarily equal to the cash outflow for interest payments; the cash paid for interest figure is preferred and will be used if it is consistently provided as a supplemental disclosure to the cash flow statement. Second, the principal portion of the debt service is typically the current portion of long-term debt (CPLTD; aka current maturities) from the balance sheet. However, CPLTD may not necessarily capture all the principal due in the coming year depending on ongoing financing arrangements and whether there could be short-term debt that is being restructured in a term-out. In some cases, CPLTD is not separated out from other short-term debt on the balance sheet; in such cases the Notes to the financial statements are expected to readily provide this information.

Share Buybacks: This figure should normally be a negative, since it a cash outflow to the purchase the shares of the company, or zero if there are no share buybacks.

Remainder: Although not shown in the table, anything left over after these items is assumed to be a change in cash balances or short-term marketable securities/cash equivalents. Because estimates are involved, the figures may not reflect the actual change in those balances.

Possible Warning Signs Net free cash flow (NFCF) should be able to cover dividends and debt service with a cushion as detailed in previous Dividend Coverage analyses, Part 1 and Part 2. However, a potential 135

concern arises when debt and stock issuance are being heavily relied upon to cover dividends and debt service rather than the company’s internally generated cash flow, i.e. net free cash flow (NFCF).

As for share buybacks, there may be differences of opinion on how much financing would be acceptable since they can help firms save on cash outflows by reducing future dividends to be paid; simplistically stated, there is an argument for financing if more cash is saved than spent.

Comparative Overview The charts below display the relative inflows and outflows of cash by variable for each firm. Note that an extra line representing “zero percent of revenues” is included in each chart; it may be superfluous but is added for a visual reference point. Recall that the cash flows are 3-year averages, unless otherwise indicated.

For brevity, any tentative conclusions are provided along with the charts.

Net free cash flow (NFCF): Internally-generated cash flow in the form of NFCF has ranged between 12-17% of revenues for the firms, clearly in positive territory.

Dividends: Cash outflows due to dividend payments are in a range between 11% to 15% of their revenues. Recall that dividend payments are shown as negative because they are outflows of cash.

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Debt Issuance In this chart, negative figures represent cash outflows from net paydowns of debt while positive figures represent cash inflows due to net additions to debt; MSFT and QCOM have generated the most cash as a percentage of revenues from debt issuance.

Stock Issuance: Apart from QCOM, the firms have issued either minimal (<1% of revenues for MSFT) or no* stock (PG, DEO) to raise cash. *Note that in some cases, the amount of stock issued is small enough that when rounded is treated as zero.

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Debt Service. By far, at 26% of revenues (rounded), DEO has the greatest cash outflows to service its debt. The others range between 3 and 5.5% of revenues. Recall that debt service should usually be considered an approximation.

Share Buybacks Cash outflows due to share buybacks range from 6% to 11% for PG, DEO and MSFT. Remarkably, QCOM’s cash outflows for share buybacks have exceeded 50% of revenues; this may be part of a strategy to reduce future cash outflows from dividend payments. See Shortcomings below for further discussion.

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Shortcomings Recall from above that the variables examined in this analysis do not capture all possible sources of cash; note that year-to-year decreases in the company’s cash on the balance sheet can be a source of cash.

Buybacks. Because of the relatively large buyback figure for QCOM, the question arises where cash flow/cash for the buybacks originated. Another look at the company’s financial statements revealed that “Proceeds from sales and maturities of available-forsale securities” were a source of cash totaling about 95% of revenues for the three fiscal years 2016-2018. Moreover, in FY 2018, cash and cash equivalents were drawn upon totaling over 100% of FY 2018 revenues (102%).

Debt Service. For DEO, other than reduction of net cash and cash equivalents (net of overdrafts), a ready explanation for the source of cash to cover the debt service was not found given the author’s time limitations.

2.3.2 Cash Flow Analysis: Mixed Industries 2 Summary *This report focuses on financing activity cash flows for four firms in diverse industries: F, KO, MMM and XOM. *Tracking financing can help highlight underlying company dynamics and dividend coverage capacity.

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*The data is sourced from the most recent annual financial statements of the firms, released last month in their SEC 10-K filings.

Introduction This analysis centers on financing activity cash inflows and outflows which involve external sources and uses. The table below begins with Net Free Cash Flow (NFCF) of each firm, followed by selected financing variables: Dividends, Debt Issuance, Stock Issuance, Debt Service and Share Buybacks.

Variables Net Free Cash Flow

Dividends Debt Issuance Stock Issuance Debt Service Share Buybacks

F 0.06 -0.02 0.04 0.00 -0.34 0.00

KO 0.10 -0.19 -0.01 0.05 -0.15 -0.08

MMM 0.12 -0.09 0.42 0.02 -0.04 -0.11

XOM 0.04 -0.05 -0.02 0.00 -0.02 0.00

Variables Detail The figures are for the most recent 3-year average of the individual fiscal years’ figures expressed as a percentage of revenues: For example, the Dividends figure in the table above is the average of the dividends/revenues figure of each of the most recent three fiscal years 2018, 2017 and 2016.

Net Free Cash Flow (NFCF). NFCF is not part of (external) financing activity; it measures the company’s internally-generated net free cash flow available to equity holders of the firm and to service debt. This figure is defined as free cash flow less any outflows for most kinds of acquisitions activity. Values can be positive, zero or negative. Note that this variable may alternatively be called equity income (EI).

NFCF should be able to cover dividends and debt service with a cushion as detailed in the previous roundup report. A concern for coverage as discussed in Part 1 of this series is when financing (borrowings, and/or stock issuance) is consistently being heavily relied upon to cover dividends and debt service rather than the company’s internally-generated cash flow.

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The financing variables are: Dividends are shown as negative if the company pays dividends, otherwise zero.

Debt issuance can be negative, zero or positive depending on how much the company has been paying down debt (paydowns being an outflow of cash) relative to raising cash through added debt.

Note that this figure, comprises all forms of debt as it is generally computed from the change in total liabilities unless otherwise indicated.

Stock issuance is typically positive if the company is issuing new shares, or zero if no shares are being issued.

Debt service is the sum of principal and interest (P+i) and is negative or zero if there is no debt to service. Note that debt service may be at best an approximation of cash outflows: First, the interest figure is often taken from the income statement (interest expense) for expediency and is not necessarily equal to the cash outflow for interest payments (i.e. cash paid for interest is sometimes shown as a supplemental disclosure to the cash flow statement, or in the Notes to the financial statements).

Second, the principal portion of the debt service is typically the current portion of long-term debt (CPLTD; aka current maturities) from the balance sheet, and therefore refers to the debt service to be paid in the coming fiscal year. However, this CPLTD figure may not necessarily capture all the principal due in the coming year due to the possibility of ongoing financing arrangements and whether there could be short-term debt that is being restructured in a term-out.

In some cases, the financial statements do not separate out CPLTD from other short-term debt on the balance sheet and the Notes to the financial statements may supply the information.

Share buybacks are cash outflows to purchase the company’s own stock and therefore negative, or zero if none. This type of activity is often the subject of controversy, and there may be differences of opinion on how much debt financing for the purpose of buying back shares is acceptable. In some cases, taking on low-cost debt to do so can help firms reduce future net cash outflows by reducing dividends to be paid relative to the cash outflows required to service the debt.

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Omitted Variables. Not shown but possibly significant sources of cash include a change in short-term marketable securities/cash equivalents (a financing variable) or the company’s own cash balances.

Comparative Overview The charts below display each variable from the table above for the companies examined. Note that an extra line representing “zero percent of revenues” is included in each chart which may seem superfluous but is added as a visual reference point. Recall that the figures are 3-year averages, unless otherwise indicated.

For brevity, any tentative conclusions are provided along with the charts.

Net Free Cash Flow (NFCF). The results indicate two performance tiers for internally-generated cash flow: 3M, KO in the 10-12% range (rounded) and F, XOM with a significantly lower NFCF percentage (4-6%).

Dividends. Recall that dividend payments appear below the flat 0% line because they are outflows of cash. KO leads the others in relative terms

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Debt Issuance. In this chart, negative figures represent cash outflows from net paydowns of debt while positive figures represent cash inflows due to net additions to debt; 3M appears to have been relatively more aggressive over the three-year period.

Stock Issuance. Of the group, KO relied most on stock issuance, followed by 3M. The other firms have issued either minimal or no* stock to raise cash. *Note that in some cases, the amount of stock issued is small enough that when rounded is treated as zero.

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Debt Service. Recall from above that debt service is the sum of principal and interest, and the figures used should be assumed to be approximations. F is by far the most burdened by debt service, followed by KO.

Share Buybacks. Relative to revenues, 3M used the most cash to buy back their own stock, followed by KO; as touched upon above, this may be part of a strategy to reduce future cash outflows from dividend payments with cheap financing.

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Tentative Conclusions and Shortcomings An overall concern stated above and in Part 1 of this series involves persistent reliance upon financing (borrowings, and/or stock issuance) to cover dividends and debt service rather than internally generated cash flow. From this standpoint, the firms all may fall short of coverage with NFCF. Share buybacks were responsible for significant cash outflows for 3M and KO (11% and 8%, respectively, rounded). Referring to the charts above, it becomes apparent that 3M has relied heavily upon debt financing (42%) to buy back shares among other uses, while KO has relied in part upon stock issuance (5%, rounded).

KO’s relatively high dividend payments (19%) and debt service (15%) were in part covered by its NFCF of 10% (rounded), as well as the stock issuance noted above.

Debt Service. Ford’s debt service (34%, rounded) does not have an easily identifiable source for coverage other than its NFCF (6%, rounded) and some debt issuance (4%, rounded). Reduction in cash balances provided only a small source of cash (1% in FY 2018). Another look at the company’s financial statements revealed that “Sales and maturities of marketable and other securities” were a source of cash of about 17% of revenues over the three fiscal years 2016-2018, but these were largely offset by purchases of the same. A ready explanation for potential source(s) of cash is not clarified given the author’s time limitations.

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For other shortcomings and qualifications regarding the analysis refer to the Appendix (Financing Obligations Coverage: Methodology).

PART 3. CONCLUSIONS AND FUTURE DIRECTIONS 3.1. Summary of Findings This summary of findings section is in two parts: (1) Financing Obligations Coverage: Whether firms in the aggregate are capable of adequately covering their financing obligations (i.e. dividends and debt service); (2) Cautionary Stress Testing: The vulnerability of firms to specified financial shocks.

The analyses may provide insight into overall prospects for dividends, recession, policy and financial institution default exposure.

3.1.1.

Financing Obligations Coverage: Summary of Findings Summary *This report aggregates the data for the 2018-19 fiscal year Financing Obligations Coverage reports for all firms studied. *Estimated centralities and their curves are presented to gain insight into dividend prospects, recession, policy and financial institution default exposure on a global basis to FY 2019. *These results cover all 29 firms in 10 industries and 4 sectors with a current combined market cap of approximately $5 trillion.

Tabular Results The table below indicates the centrality estimate for each category of analysis for all 29 firms. Below the table is a brief interpretation of each category of analysis accompanied by the corresponding charts of fitted curves.

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Charts

Category 1 Coverage 1 2 Deviations 3 4 Shift (3-Year) 5 6 Coverage 2

Variables Centrality Dividends 0.1436 NFCF 0.053 Rev Growth -0.0117 NFCF 1.0007 Rev Growth 1.0016 Debt Service + Divs -0.328

Notes: Values may alternatively be presented as a percentage (e.g.

0.12 as 12%). The vertical lines within the curves represent the estimated centrality value. The wording “overall” may refer to the results being based on the aggregate data of all firms studied.

Dividend Coverage (Chart 1): The coverage variable is NFCF (net free cash flow as a percentage of annual company revenues).

Positive (negative) values suggest enough (inadequate) cushion to cover dividends. A figure of zero suggests breakeven coverage.

The vertical line at 14% (rounded) initially suggests overall positive dividend coverage capacity. Note however that dividend coverage may not be comparable since dividends paid can vary considerably across firms, including negligible amounts.

Deviations 1-Year (Charts 2 & 3: NFCF, Revenue Growth; latest FY vs. prior): Negative values suggest a worsening over the past year because the latest fiscal year’s value has moved below the prior period central tendency. Zero or near-zero values suggest that the latest values are in line with historical.

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Deviations over the past 1-year period appear to have been somewhat positive for NFCF/Revs at 5% (rounded, left chart), or slightly negative for revenue growth at -1% (rounded, right chart).

Shifting Centrality (Charts 4 & 5: NFCF, Revenue Growth; 3year): Here the baseline for a shift=1, not zero. Smaller values suggest secular deterioration. This category identifies any shift of each company’s own “core” performance over the latest 3 years.

In both cases, the overall shift appears to be minimal.

Coverage 2 (Chart 6: Dividends+Debt Service or DDS): Positive (negative) values suggest an adequate (inadequate) cushion to cover DDS. The coverage variable NFCF is compared to the sum of dividends and debt service* (abbreviated DDS or d+ and may also be termed financing obligations). The negative value of -0.32 suggests that in the aggregate, the firms are 148

struggling with the dividend and debt service requirements that tend to exceed their cash flow.

(*) Definitions: Reference to “cash flow” as an abbreviation is intended to mean net free cash flow NFCF unless otherwise specified. Revenue growth is expressed as a YoY (year-overyear) percentage change. Debt service is the sum of principal and interest payments (which may be estimated). Performance. The basis for this analysis is spatial, where the central tendency or “core performance” may also be referred to as “spatial centrality” or simply centrality. Negative signs do not indicate ‘negative’ space but are added to highlight that the estimated area defines a shortfall. Also refer to the Appendix (Financing Obligations Coverage: Methodology).

Shortcomings An important observation on the reliability of dividend and debt service data is noted here: Dividends. Generally, dividend amounts can be ascertained with reasonable confidence from the financial statements, but coverage may not be easily comparable between firms since dividends paid can vary considerably across firms, including negligible amounts. Debt service. Correct annual debt service figures are not considered reliable, and therefore in many cases estimates must be made. Ascertaining debt service amounts should be generally be viewed as opaque even if clear debt service figures are found in the financial statements. The reason for this has been stated in previous observations by the author, but in addition to concerns regarding 149

contingent and off-balance sheet liabilities, there is reason to believe that significant amounts of debt on the firms’ balance sheets may not be adequately amortized; one example of this is the failure to term out and amortize working capital credit lines that can no longer be cleaned up.

Accuracy of curves. None of the curves should be thought of as “correct” versions of the processes examined; at best they are theoretical estimates that may tend to approximate the actual.

Note also that a default assumption of fat tails for the theoretical models of each firm is generally made in favor of higher-ranking distributions to err on the side of caution (Re: Appendix A2.1 Financing Obligations Coverage: Methodology); had other distributions been selected the results may differ.

Time Period. Uniform time periods of financial data are preferred but not all the firms have identical fiscal year ends. For example, the fiscal year 2018-19 financial data span the period from 1/1/2018 to 9/29/2019. Most data tend to be concentrated in a single calendar year (1/1-12/31), but 8 of the 29 firms (28%) have non-calendar fiscal year ends (3/31, 6/30 and end of September), representing approximately 39% of the market capitalization of all 29 firms combined.

Conclusions Results. The overriding concern is the coverage capacity of financing obligations, which at -0.33 (rounded) suggests a heavy debt load that cannot be securely covered by core cash flow (Coverage 2). Dividend coverage capacity (Coverage 1) is apparently positive but is not a viewed as a comprehensive or necessarily reliable estimate for reasons noted elsewhere by the author. Deviations (1-year) suggest some growth in cash flow NFCF accompanied by a slight decline in revenue growth.

Shifting Centrality over the past three fiscal years overall appears to be minimal.

While a significant broad-based downturn was not yet apparent from the results as of FY 2019, the level of debt to service appears to pose a heightened threat of non-performance of financing obligations, whether in the form of dividend cuts or debt defaults.

Moreover, a link between debt and downturns warrants highlighting: A high debt load can increase the fragility of firms to a sudden adverse event, not only triggering a recession but one 150

that is potentially far worse than had the debt been at more manageable levels.

Debt restructuring offers a solution See Part 3 (Firm Level: Preemptive Debt Restructuring) for further information. It is likely that in the absence of adequate cash flow coverage, expanded lending on collateral values will be pursued. Policy actions may also be taken to reduce debt service through further rate cuts.

Sustained inflation aided by monetary policy may also serve to reduce debt by decreasing its value in real (not nominal) terms.

Report Frequency. As stated in the previous section on shortcomings, the reliability of any financing obligations coverage (i.e. both dividends and debt service) results and their comparability across firms is in question. Accordingly, while the Financing Obligations Coverage analyses remain an informative exercise, annual frequency of reports may not be warranted.

Therefore, the frequency of analysis may best be reduced to once every few years (to be determined). The Cautionary Stress Testing reports may offer an adequate alternative resource. The stress tests check for vulnerability to shocks at the firm level by specifying scenarios of varying severity and performing simulations based on theoretical models. Based on the generated estimates, a criticality level of the firm’s vulnerability (i.e. the extent to which an adverse shock could in theory become a reality) can be assigned. The stress test results also can help gain insight into dividend prospects of firms, recession, policy and default exposure of lending institutions in a relatively streamlined way.

Extension. Further background information on the curves and their properties is shown here in tabular form.

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0.055 0.09 None None None None 0.42 0.11 None 0.0179 None None 1.0007 None 1.0016 None None None

Mean Variance Mode

1 2 3 4 5 6

Dagum 1,2,7 Gen Pareto 1,1,2 Johnson SB 2,1,7 Cauchy 1,2,1 Cauchy 1,1,28 Johnson SB 5,29*, n/a

-0.52 -0.56 -0.45 -INF -INF -0.5

+INF 0.44 0.45 +INF +INF 0.49

Actual Centrality MinMax -0.5, 0.5 0.1436 -0.5, 0.5 0.053 -0.5, 0.5 -0.0117 -INF, +INF 1.0007 -INF, +INF 1.0016 -0.5, 0.5 -0.328 Max Min Rank Distribution Chart #

(*) Notes: None of the curves should be thought of as “correct” versions of the processes examined, rather they are estimates that may approximate the actual. Also noted previously is the small sample. Rank. Three goodness of fit test results are shown, separated by a comma: Kolmogorov-Smirnoff K-S, AndersonDarling A-D and Chi-Squared C-S; distribution selection can be subject to interpretation and modifications. For example, in Chart 6, although a rank of 29 for the A-D test appears low, it remains higher than the A-D ranking (30) for an alternative distribution 152

with a higher K-S rank. Actual MinMax. The actual minimum and maximum values were known and are as shown in the table: 3.1.2. Stress Testing: Summary of Findings Summary *This report aggregates the data for the 2018-19 fiscal year Cautionary Stress Testing reports. *Estimated centralities and their curves are presented to gain insight into prospects for dividends, recession, policy and financial institution default exposure on a global basis to FY 2019. *These results cover 14 firms in 5 industries and 3 sectors with a current combined market cap of approximately $2 trillion, furnishing a benchmark to assess levels of criticality.

Tabular results of the estimated centrality of critical zones for each scenario are shown below for all 14 firms. Below the table is a brief interpretation of each category of analysis accompanied by corresponding charts of fitted curves.

Charts Scenarios Centrality 1 NFCF<0% 0.079 2 NFCF/Revs <-.25% 0.029 3 Rev Growth<0% 0.29 4 Rev Growth=-0.25% 0.047 Note: NFCF/revenues may also be abbreviated NFCF (net free cash flow). Values may alternatively be presented as a percentage (e.g. 0.12 as 12%). The vertical lines within the curves below represent the estimated centrality value.

Results in Charts CHART 1 NFCF/Revenues <0%. In this scenario, the firm no longer has adequate NFCF to cover its financing obligations (i.e. dividends and debt service) --a potential marker for insolvency.

The vertical line indicates the value of estimated (spatial) centrality at 8% (rounded).

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CHART 2 NFCF/Revs < -0.25%. This assumed shock is a drop in annual net free cash flow below 25%. In this more severe scenario, the estimated central critical zone drops down to 3% (rounded) from 8% (rounded) in the previous scenario.

CHART 3 Rev Growth <0%. In this scenario, the company’s annual revenues fall below zero growth. The criticality level in this revenue growth shock is considered relatively high given the estimated critical zone value of 29%.

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CHART 4 Rev Growth <-25%. In this more severe revenue shock scenario, the critical zone value drops off to 5% (rounded) from 29% (rounded) in the previous milder scenario.

Shortcomings.

Accuracy of curves. The curves should not be thought of as “correct” versions of the processes examined; at best they can be viewed as theoretical estimates that may approximate the actual.

Note also that in the analysis of individual firms, the default assumption of fat tails for the theoretical models of each of the variables is made in favor of higher-ranking distributions (Re: 155

Appendix Stress Testing: Methodology); had other distributions been selected the results may have differed, although the default assumption is considered more cautious.

Time Period. Uniform time periods of financial data are preferred but not all the firms have identical fiscal year ends. For example, the fiscal year 2018-19 financial data span the period from 1/1/2018 to 9/29/2019. Most data do tend to be concentrated in a single calendar year (1/1-12/31), but some have non-calendar fiscal year ends such as 3/31, 6/30 and end of September.

Extension. Further background information on the curves and their properties is shown here in tabular form.

Chart #

Distribution

Rank

Min

Max

1 2 3 4

Dagum Log Pearson 3 Cauchy Error

1,14,6 3,5,43 2,2,16 2,1,31

0 0 -INF -INF

+INF 0.23 +INF +INF

Actual MinMax 0-1 0-1 0-1 0-1

Centrality Mode 0.079 0.029 0.29 0.047

0.067 0.025 0.29 0.047

Mean Variance 0.09 0.03 None 0.047

0.0025 1.7 E-4 None 5.03 E-4

(*) Notes: To reiterate from above, the curves are not necessarily “correct” versions of the processes examined and are estimates that may approximate the actual. Also noted previously is the small sample. Rank. Three goodness of fit test results are shown, separated by a comma: Kolmogorov-Smirnoff K-S, AndersonDarling A-D and Chi-Squared C-S; distribution selection can be subject to interpretation and modifications. Actual MinMax. The actual minimum and maximum values were known and are as shown in the table.

Cautionary Stress Testing Background Detail Applicability. Firm-level stress testing may aid in assessing vulnerabilities within financial institutions (loans), capital markets and investment portfolios (securities). Moreover, criticality levels may offer insights as to the prospects for economic recession and policy countermeasures. Shocks experienced by individual firms may exert impacts on capital

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markets, financial institution credit portfolios and economic output.

Methodology and other details are consolidated in a separate post titled Cautionary Stress Testing: Background Notes, referenced in the Author’s page.

Clarifications & Qualifications: A criticality level is assigned based on the critical zone estimates and refers to the extent to which a given scenario could in theory become a reality (e.g. critical zone values of a minimum of 0 and a maximum of 1 correspond in theory to “no possible outcomes,” and “all possible outcomes,” respectively). However, the results of stress testing should not be equated with predictions or forecasting; at best they can be viewed as imprecise indicators of the vulnerability of firms in various scenarios.

3.2. Reforming Capital Markets: Reflections The first part of this section proposes measures at the firm level to address debt issues early on: Preemptive Debt Restructuring. The second part proposes two types of policy reform to reduce what the author terms economic injustices. The policies are (1) liberalization to address certain legal monopolies; and (2) ownership recognition in connection with fiat money creation processes within the financial system. Any such reforms are intended to minimally intervene to the existing institutional framework.

3.2.1.

Firm Level: Pre-emptive Debt Restructuring

Summary *Nonfinancial corporate business debt now approaches $U.S. 10 trillion according to recent Federal Reserve statistics. *Borrowers and lenders may delay debt restructuring only until after the company formally defaults, potentially aggravating the severity of recessions. *This post presents a simple early warning analysis to identify excessive debt, with an overview of pre-emptive restructuring steps and policy interactions.

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*A long-term solution towards a self-regulating system of credit would feature flexible interest rates that reflect market signals free of policy distortion.

Early Detection of Coverage A first step in a preemptive approach to restructuring is to identify the coverage of financing obligations defined as dividends and debt service (DDS). Net Free Cash Flow (NFCF) is the primary internally generated cash flow source to cover these financing obligations.

For further details and definitions see the Explanatory Notes at the end of this post. Coverage of these financing obligations (also abbreviated DDS coverage) is examined with (1) averages and actual (same-year) data in Chart 1 (2) compound annual growth rates (CAGR) in Chart 2, and (3) spatial centrality (in several frames featuring curves). This generic analysis employs actual data to fiscal year (FY) 2018 of a non-financial firm (KO) for which enough historical observations were available to construct extended charts.

Chart 1 below shows DDS coverage over fiscal years 1996-2018.

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Average. DDS coverage is estimated by comparing the 10-year average of NFCF to the sum of debt service and dividends (DDS) in each of the fiscal years 1996-2018 (e.g. the average to FY 2018 is for FY 2009-FY 2018 inclusive). The data are as a percentage of revenues (i.e. NFCF/Revs and DDS/Revs). The arithmetic 159

mean is shown for NFCF/Revs (labelled AM) along with the same year values labelled actual. (The geometric mean is not shown but does not appear to lead to different conclusions). DDS coverage appears to have been approximately adequate until FY 2013 per the averages (arithmetic and geometric); however, since FY 2014, the DDS/Revs figure has surpassed NFCF/Revs. As of FY 2018, DDS stood at a total of 39% of revenues (=18% for dividends/revenues and 21% for debt service/revenues). Actual (Same-Year). Now comparing the firm’s financing obligations to the NFCF/Rev values in the same year it appears that after rebounding in FY 2013, coverage began to fall short again by FY 2014.

Chart 2. CAGR. In terms of compound annual growth rates (10year), FY 2014 also appears to have been a turning point in financing coverage. Revenue growth may have been an early warning indicator after peaking in FY 2011.

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For additional reference, based on slightly longer period of 13 years of observations to FY 2018 (FY 2018-2006) the CAGR

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approximates negative 1% per year. The next section focuses on coverage from the standpoint of spatial centrality.

Spatial Centrality (Charts 3-9). In this section, financing coverage for each fiscal year of a total of six fiscal years is represented in each chart below as a fitted distribution of NFCF/Revenues. Results for FY 2013-FY 2018 are shown in the charts below, along with an interpretation of these charts. For example, in the chart labelled ‘fiscal year 2018’ (FY 2018), the underlying data for the distribution is for the 10 fiscal years from 2009 through 2018.

FY 2013-14. The frame on the left suggests hypothetically adequate coverage in FY 2013 (adequacy represented by green color-coding). The frame on the right is FY 2014 and shows a marked shift to a tendency towards inadequate coverage (represented by red color-coding). For further explanation, see the interpretation below.

Interpretation. Green and red color-coding between vertical lines signifies hypothetically adequate or inadequate coverage, respectively. The word ‘hypothetical’ is used because this type of format presents tendencies around a central estimate based on a theoretical model. Vertical Lines: Each curve features two vertical lines with a solid color in between them: (1) One of the vertical lines (V1) represents a spatial central estimate of the curve (where 50% of the space is located to the left and right of the vertical line); also abbreviated as ‘centrality’ and which tends to appear in the dense area of each curve. (2) A second vertical 162

line (V2) represents the location of DDS/Revenues (i.e. the financing obligations) for the most recent fiscal year. This vertical line V2 can be anywhere around centrality V1: If V2 is to the left, the color coding is green, meaning hypothetically adequate. If V2 is to the right of V1, the color coding is red, suggesting hypothetically inadequate coverage.

FY 2015-2017. Compared to FY 2014, the next three frames suggest a slight worsening in 2015 (left), a stabilizing in FY 2016 (middle), followed by a further worsening in FY 2017 (right).

Fiscal Year 2018 (Most recent, below). By FY 2018, while the centrality is estimated at 13.8% of FY 2018 revenues, the DDS/Revs (financing obligations) total 39% of revenues and do not appear within the frame; therefore, a second ‘cutout’ frame is added (right) to show the DDS/Revs figure as a dot at 39% at the far right of the frame).

Shifts in Key Variables (Year-over-Year) In addition to DDS coverage as discussed above, year-over-year (YoY) changes in the individual NFCF/Revs and Revenue growth 163

variables can highlight if extra caution might be needed in debt and dividend decisions.

Chart 10. NFCF/Revs. The chart below shows YoY changes in the estimates of spatial centrality of NFCF/Revs (the figure 1 represents no change from the previous year). A simple trend line is added as a rough reference.

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Chart 11. Revenue Growth. The CAGR figures for revenues based on rolling 10-year periods were shown in the CAGR chart (Chart 2) above, indicating a turning point after the peak of FY 2011. A chart of YoY revenue growth is shown next with a simple trend line.

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As early as FY 2012, the significant change in revenues might have raised a red flag. The 10-year CAGR of revenues (FY 2018 to FY 2009) is nearly zero. The YoY revenue growth history since 2007 suggests a slight downtrend.

Tentative Conclusion: Early Detection. From the generic early warning analysis above, the firm could have begun taking a more cautious approach towards adding to its financing obligations as early as FY 2014; an even earlier signal occurred in FY 2012 with the significant drop in revenue growth from the previous fiscal year. This raises the question discussed next: Was the continued accumulation of financing obligations (DDS) an oversight on the part of management?

Preemptive Actions Oversight or Intent? If decisionmakers underwent a similar analysis and were aware of a deterioration in DDS coverage there may have been a conscious decision to add leverage after careful consideration of the trade-offs and risks. For example, one strategy might be to finance stock buybacks with low-interest debt to (a) maintain the stock price within a certain range, and (b) raise dividend growth rates on a smaller base of outstanding shares in hopes of a turnaround in revenues and improved cash flow. This may appear beneficial at least short-term if the share repurchases reduce net cash outflows such that reduced dividend payments exceed any additional debt service required from financing the repurchased shares. In the meantime, they may pursue strategies to raise revenues and cash flows.

However, if revenue and or cash flow growth do not materialize within a certain timeframe as determined by management, prompt action may be necessary before ‘officially’ defaulting (e.g. ‘official’ here can mean default on payment or possibly certain covenant defaults as per loan agreements which would force their lenders to initiate a debt restructuring plan). The following is a cursory overview of possible preemptive actions: 1. Equity Infusion/New Equity Issuance. At the risk of diluting the stock and weakening the stock price, debt can be paid off and preferred stock retired (re: see the Addendum below whereby policy action can be instrumental; Grant 1996). Also, key investors and those

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with a stake in the company’s future may also step up by purchasing newly issued shares.

2. Debt Refinancing. Rather than waiting for the lenders to move, management seeks a change of banks/lenders (e.g. for a ‘take out’) or proactive negotiation with current lenders to stretch out debt service over a longer period. 3. Dividends. One strategy to reduce dividend payments via share repurchases was described in the previous section; if this is unsuccessful, an undesirable yet possibly unavoidable strategy is simply to cut or eliminate dividends. 4. M&A. An acquisition of, or strategic merger with, (ideally a much less indebted company) can raise revenues from the combined entity while affording the opportunity to eliminate cost redundancies. 5. Spin-offs. Cash can be raised by selling off poorly performing parts of the business to be better managed individually than as part of the big entity that it is dragging down; the proceeds of the sale can then be used to pay down (or pay off) debt.

Policy and Systemic Causal Factors Firms may base some of their decisions on existing or new policies to be implemented (e.g. monetary policy and rate cuts). Lobbying by overextended firms for interventionist policies can play an important role, as well. Longer term, a major concern is the eventual ‘zombification’ of companies, financial institutions and economies as their dependence on one or more forms of state aid becomes endemic. A few examples are detailed next: 1.Monetary policy can be instrumental in reducing the cost of borrowing through lower policy rates, raising equity prices and in reducing the real value of debt through secular currency depreciation. For an historical example of an equity issuance replacing debt, see Grant (1996) on the “miracle cure” of the early 1990’s in which the Federal Reserve is described as a “helpmate” with low policy rates propping up stock prices and helping to reduce corporate-bond defaults and net corporate interest (pp.

244-245). The distortive and deceptive nature of this policy is noted; normally stock prices would be expected to reflect additional share issuance, but policy interventions can mask the market’s signal. Also, see the following section (Interest Rate Distortion) below regarding interest rate policy in recent years

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possibly contributing to a tendency towards overborrowing by firms.

2.Bailouts can take various forms, involving the purchase of company equity or debt, either by a government entity/agency or by central banks. These purchases (likely at inflated prices compared to the market) can have a distortive impact on yields because as securities are removed from the pool of available securities on the market, investors competing for, and bidding on, the remaining supply can lower yields below where they would settle in an undistorted market, sending the false signal of lowyield ‘safety’ and also pushing investors towards even higheryielding and riskier investments. This phenomenon may be prevalent in high-yield debt markets (where the underlying debt issuers are highly leveraged firms); contradictorily, HY debt may cease to be high-yielding because policy has essentially severed the link between heavy debt loads and high-yield spreads.

3. Banking System leniency includes perpetual forbearance and perennial renewal of defaulted and non-performing loans without initiating any special servicing or restructuring arrangements.

Working capital lines of credit that become unrepayable eventually stagnate on bank books as “permanent working capital lines of credit.” Problem loans also may be backstopped by government guarantees.

While at first banking system problems may fester quietly, they may eventually culminate in full-blown banking crises. (re: evergreening concept; see Kennedy (2015) Appendix 3 detailing the phenomenon of debt service and permanent non-amortizing debt in the banking system; also, Sigurjónsson, 2015 regarding systemic banking crises since 1970: 147 occurrences in 114 countries).

Monetary Policy Detail: Interest Rate Suppression? With early detection and pre-emptive action as summarized above, management may be able to stem or slow the growth of unsustainable debt accumulation. However, a system-wide issue remains that presents a challenge to limiting leverage: The absence of self-regulating market forces for the issuance of credit --until crisis hits. A more self-sustaining solution would be a longterm incremental adoption of flexible market rates of interest that 168

naturally regulate the quantity of credit granted (i.e. natural interest rates): Interest rates should rise as a function of greater loan demand relative to the supply of credit; moreover, less creditworthy borrowers should be disincentivized from taking on too much debt through higher (not lower) interest rates that reflect higher risk.

Charts 12-13. 2008-2018 Period. A crucial question is whether monetary policy might be encouraging more borrowing than would otherwise be the case. The charts below show the yearover-year change in the yields for 90-day Treasuries (left) and 10year U.S. Treasury bonds (right) from 2008 to 2018 (constant maturity).

During this period (2008-2018) central estimates (spatial) for the YoY change in the 90-day and 10-year yields are 0.95 and 0.93, respectively.

Charts 14-15. Comparison with 1963-2018 Rates. The estimates for the 2008-2018 period in the above Charts 12 and 13 appear to be substantially lower than those for the 55-year period from 1963 to 2018 shown in charts 14-15 below showing the distributions of the YoY change over 1963-2018 for the 90-day and 10-year bond yield. Their central estimates are 1.013 and 1.002, respectively, suggesting minimal change, particularly for the 10-year.

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Since the Great Financial Crisis (GFC) from 2008 to 2018, interest rate policy may have been somewhat more accommodative than the longer-term historical pattern suggests, both for short-term yields (90-day) and longer-term yields (10-year). Extended yield suppression, while intended to aid in the recovery, may have sowed the seeds of leverage for many firms, as a self-regulating rise of yields corresponding to increased borrowing was prevented from occurring.

Explanatory Notes 1 Definition, NFCF. NFCF is the primary source of payment for dividends and debt service (DDS), defined as Free Cash Flow (FCF) less acquisitions and is viewed as a more comprehensive measure for estimating debt service coverage than free cash flow (FCF). Also refer to the Appendix (Financing Obligations Coverage: Methodology), or the author’s Financial Distortion Dynamics (2018). NFCF is also referred to as equity income. For debt service coverage (DSC), the term primary source of repayment (PSOR) is used because of repayment of principal.

DSC is considered an essential indicator of over-indebtedness in addition to debt on the balance sheet.

2. Dividends. It is recognized that dividends are not considered to be an obligation in the same sense as debt, however, for dividendpaying companies, it is assumed that there is a reasonable expectation of dividend payments (and in many cases, also dividend growth) that constitute an implicit sense of obligation.

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3. Restructuring. Typically, restructuring refers to debt alone (i.e. debt restructuring).

In this framework, the concept of restructuring covers both dividends and debt.

4. Models. The theoretical curves selected are generally the most commonly occurring highest-ranking fitted distributions for each variable. For example, for NFCF/Revs, from FY 2009-FY 2018 the highest-ranking fitted distribution occurred in 7/10 or 70% of the cases and was also ranked highest in five of the six most recent fiscal years (FY 2013 to FY 2018); in the one exception, its ranking was #2. The shortcomings of theoretical models have been noted in previous posts and should be emphasized.

5.Closing Comment. In the section on Policy (in particular, monetary policy) a strategy of waiting for policy rates to decline further to lock in lower interest rates is implied, although the aim of preemptive debt restructuring is to avoid temporizing to make important and timely decisions.

3.2.2. Policy Reforms 3.2.2.1. Recognition of Taxpayer Ownership This topic is partly reproduced from the author’s Financial Distortion Dynamics (2018: Part III C “Financial Distortion in Equity Financing: Recognition of Taxpayer Ownership.”

Low-Cost Financing Source. A source of financial distortion that is often overlooked but that deserves mention concerns taxpayer funds that are transferred to firms in the form of either contracts or so-called corporate welfare including subsidies and bailouts.

This is a form of low-cost financing for businesses because the ownership rights of taxpayers are not recognized and there is no reimbursement to taxpayers for their infusion of capital into the companies. This issue was previously raised in Kennedy (2017), and the relevant policies discussed here are classified in the Subsystem 1 Policy Table as follows Granting/Awarding of Contracts (Sub 1.2a) and Corporate Welfare (Sub1.4).

Note: Other redistributive policy actions such as Transfer Payments (Sub1.1) and Employee Compensation: System 2 Employees (Sub1.3), are not considered here.

Recoupling 171

The concept of recoupling converts the flows of income-based fees (i.e. taxes) which are the derivative inflows to System 2 into assets with ownership rights preserved. In other words, the flow of funds is converted into a stock (an asset that preserves ownership rights), as shown in this circle and in the diagram further below:

Polarity Reversal This recoupling approach also reverses the polarity of the system such that the positive feedback loop structure is redirected and converted into a new type of counterbalancing feedback loop.

Flow-to-Asset Conversion Loop. Adding the recoupling link to the existing systems yields the following diagram:

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Source: Kennedy (2017) with flow-to-asset conversion loop added here.

Summary. Taxpayer funds are redistributed through the granting/awarding of contracts or corporate welfare. To recap from above, this benefits firms by giving them a much lower financing cost than would otherwise be the case. The recoupling approach proposed here rectifies this significant distortion of capital pricing for firms. Taxpayers retain legal ownership of their funds which are securitized and recorded as capital infusions to the firm. There might be the option to specify whether the funds are to be converted into (1) ownership/shares or (2) bonds/debt 173

capital or (3) a mix of the two. The process might be governed under a form of legal trusteeship under which share ownership can be transferred to designated pension accounts or held directly by the taxpayers themselves in their own accounts. Taxpayers can receive a return on their “investment” in the form of dividends and or interest (along with principal repayment). The accounting methodology is detailed below.

Note that additional equity issuance will dilute the shares of the recipient firms (Re: dilution). It is acknowledged that any attempts to alter the existing arrangement would not be likely to be implemented politically. However, at some point if taxpayer funds were integrated into smart contracts, the ownership of those funds and asset characteristics could be retained.

Recoupling Accounting Methodology Example: Contracts and Contract Revenues to Firms. A single example is shown here of a hypothetical government contract that represents revenues to a firm. First, the taxpayer ownership rights are recognized. Returns to the taxpayers (e.g. in the form of dividends or as debt service (i.e. interest + principal repayment) are paid to the taxpayers just as they would to other investors as a return on their investment. Simultaneous Recognition. Second, while recognizing taxpayer ownership, the firm recognizes the revenues simultaneously. How is this possible?

An unconventional set of accounting standards would be necessary although possibly unacceptable for GAAP; nevertheless, the following is a sketch of the accounting logic to preserve taxpayer ownership rights.

Journal Entries. Consider the journal entries (abbreviated JE) for Revenues when a government contract is legally granted to a firm (note that for simplicity, we use cash sales and a no percentage of completion contract): Journal Entry 1. Recognition of Contract Revenues Cash………………xx Revenues……………xx

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The following example assumes the issuance of common stock to the taxpayers to recognize their ownership while simultaneously recognizing the revenues earned from the contract: Journal Entry 2. Common Stock Issuance Here are journal entries for the issuance of common stock (ignoring par and excess-of-par value): Cash………………xx Common Stock………xx Modification. However, JE2 must be modified such that the debit is no longer cash (since cash has already been recognized in JE 1 above). Therefore, a new method and account category are proposed in the modified Journal Entry 2 below, which is a noncash transaction that replaces the cash in JE2.

Journal Entry 2 (Modified). Recognition of Taxpayer Ownership in Common Stock Recognition of Ownership Rights………….…xx Common Stock……………xx The recognition of ownership rights account represents an intangible asset that recognizes the taxpayer ownership and serves as the offset to the common stock issued to them.

It is acknowledged that such a practice would significantly dilute the stock of the entity and businesses and would be opposed by existing investors.

Amortization. The account may be amortized according to a formula derived from estimated future stock repurchases.

3.2.2.2. Addressing Monetary Asymmetry Ownership Recognition in Connection with Money Creation

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The following is an oversimplification of a complex process, briefly sketched as a preliminary exposition. Examples of journal entries as provided in the previous section are not shown here.

Problem. Debt monetization dilutes the currency which in turn reduces the purchasing power of savers/cash holders, creditors, wage earners and retirees – impoverishing them incrementally year after year. Meanwhile, those who received the income have benefited disproportionately by this money creation process.

Solution. To offset this injustice, the lost purchasing power is reestablished by crediting accounts of individual citizens by the per capita amount of fiat money creation (i.e. reserves expansion via debt monetization, and fractional reserve lending). Ownership rights are finally recognized for “citizen accountholders.”

The accounting for such an adjustment is summarized here and while unconventional, would help to rectify the existing monetary asymmetry and reverse the polarity from positive to counterbalancing.

On the asset side of the balance sheet a contra account exists to offset every asset acquired by the central bank by an equivalent change in the contra account. This results in a zero net increase in the balance sheet asset side. Why? Because “created” money is fundamentally not real money and although it is being used to acquire real assets the created funds were not involved in an economic value-creating process. On the liabilities + equity side of the balance sheet is an offset that represents an ownership interest by the citizens in the assets of the central bank. While liabilities in the form of commercial bank reserves have expanded due to the debt monetization, recognition of this ownership interest is in the form of “negative” equity, resulting in a zero net increase in the balance sheet liabilities + equity side of the central bank. The citizen “accountholders” now can count the central bank’s assets as part of their personal wealth. The mechanics of managing these accounts is not detailed here but could be subcontracted to trust administrators nationwide. The diagram in the previous section (ownership recognition and conversion to asset) also applies to this monetary dynamic, with the difference that citizens receive assets of the central bank rather than private corporations that benefited from taxpayer funds.

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Notes: 1.An estimate of growth in debt monetization is estimated at 5.6% per annum per capita since 1941. The history and derivation of the debt monetization estimates (1941-2018) were detailed in Part 1 (1.3.1).

2.On top of the monetized debt is commercial banking system money creation through fractional reserves deposit expansion of an estimated 1% for a total expansion of money creation estimated at approximately 6.7% per annum (corresponding to currency dilution).

3. Monetization need not be limited to debt; central banks may also change their policies to acquire equities, gold and other assets.

4. Debt monetization by foreign central banks should be credited to the citizens of the foreign country.

5. Gold purchases/holdings by the central bank would also be owned by the citizen accountholders just like the other assets.

6. Fractional Reserve Banking. To rectify the asymmetry resulting from fractional reserve banking money creation, for any “created money” (i.e. reserves) portion of loans/credit granted, the borrowers should receive a corresponding reduction of their total liability to the lending institution! The offset on the bank books would appear as some form of negative equity in the commercial bank balance sheets, with the net effect of created money on the bank assets being zero.

7. For further discussion on polarity reversal, see Part 3, Section C of Kennedy Financial Distortion Dynamics (2018).

3.2.2.3. Other Asymmetries and Choice Expansion Policy Legal monopolies essentially outlaw or severely curtail freedom of consumer choice, raising the risk of injustices including excessive costs and unfair judicial outcomes. Two key sectors to be considered for liberalization are community providers and adjudication services. Solutions proposed here are proximate and subject to revisions.

Community Provider Choice and Legal Monopolies Problem. Many localities (in U.S., for example, municipalities, counties, parishes and states) are legal monopolies immune from 177

competition from alternative community providers. Resident property owners have no opt-out clause or ability to change providers, other than out-migration to another locality which itself may also enjoy legal monopoly status. This asymmetric power legally creates "captive” residents that can be subjected to heavier costs through taxation (i.e. rising in excess of consumer prices and general cost of living) as well as poor customer service including reduced control over community safety. Property holdings may also be more difficult to transfer or sell to more capable management.

Solution. Allowing liberalization and competition in local area management ("community providers") gives property owners choices of providers. Payment, rather than by conventional means of taxation is replaced by monthly or annual community provider plans with opt-out clauses after an initial contract period, roughly analogous to internet service or phone providers. Competition can also bring some relief to help reduce costs charged to residents*.

In the U.S., currently municipal, county and state bonds provide a source of financing for these entities and serve as investment vehicles; liberalization would prove another source of financing for muni, county and state stock issuance as well as a new opportunity for investors. *Note: It is possible however that in an initial transition period costs for individual services that had been undercharged to residents (e.g. covered by debts, transfers from elsewhere, or other means) could rise, even if the sum of all service charges are lower than in the prior taxation-based system.

Adjudication Choice and Markets Problem. The current judicial system might be described as an adjudication monopoly that disallows free choice of competing judges (adjudication services). Consequently, the existing court system remains dominant and a market for adjudicators cannot develop properly; gravely, this asymmetric power status can perpetuate injustices because of the lack of competition in neutral adjudication.

Solution. Both sides are permitted to choose the judge(s) presiding over the case, allowing a market in adjudication services to develop in which judicial organizations/firms (employing 178

judges and attorneys) can compete on their ability to render judgement in a neutral manner (neutral adjudication).

Notes: To some extent partial solutions exist already in the form of out-of-court settlement or binding arbitration as an alternative to seeking justice through the court system. Just as attorneys and other professionals in various fields including Certified Public Accountants can be ranked according to various criteria, adjudication services can also be ranked according to their reputation for neutrality. Moreover, since there would likely be various court locations available to plaintiffs rather than a single court location, the problem of clogged courts and slow processing of court cases may also be diminished. Specialized judges with high competence in the relevant fields would also be a desirable outcome of such a market. Affordability remains a concern, although the existence of a market for personal injury lawyers often providing legal representation for low-income victims may serve as a reference for the development of new markets in the legal system. Source: Kennedy, Raoul., Economic Distortion Dynamics (2017).

APPENDICES A1. Firm Level Analysis: Sample Data Detail The analytical framework for the reports was applied to 29 global non-financial firms, classified by sector and industry with market capitalization totaling approximately US$5 trillion as of early 2020. The time period corresponds to the fiscal years of the firms, which ended either in 2018 or 2019. Note that “industry” may also be referred to as “subsector.”

A summary of the sector and industry groupings is provided below.

The five stress tests performed are noted with an asterisk after the industry name.

Basic Materials Sector (2 subsectors, 6 firms) Oil & gas production SIC 1311 Petroleum Refining SIC 2911 Consumer Goods Sector (3 subsectors, 8 firms) 179

Auto Makers SIC 3711 Beverages SIC 2080, 2086 Household & Personal Products SIC 2840* Healthcare & Industrial Sector (2 subsectors, 6 firms) Pharmaceutical SIC 2384* Diversified (Med/Ind) Equipment SIC 3821, 3841, 3861* Technology Sector (3 subsectors, 9 firms) Semiconductors SIC 3674, 3663* Software SIC 7371, 7372 Telecommunications equipment SIC 4813* Sample Selection. Non-financial firms were selected for this study. A primary objective was to diversify geographically and by sector and industry (subsector). Quality of financial information was also viewed as fundamental. Firms listed on either the NYSE or the NASDAQ have audited financial statements and filings with the U.S. Securities and Exchange Commission (SEC). These filings are predominantly 20-F and 10-K reports.

Note on Classification. Because some firms in the health care category have a significant industrial element, they can also reasonably be classified within the industrial goods sector. The solution was to consolidate healthcare and industrial into a single sector.

Distribution of Firms and Sectors Geographic. Firms are divided into four geographic regions: North America, Asia, Europe, and Other. North America consists of the United States, Canada and Mexico (15 firms or 52% of the total). Seven firms are classified in Asia (24% of total), five in Europe (17%). The “Other” category consists of one firm from South Africa (SSL) and one from Brasil (VIV). Note that TEVA Pharmaceuticals, has changed its legal domicile from Israel to the U.S.

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The “country” refers generally to the legal jurisdiction of incorporation, as reported to the SEC. It should be noted legal jurisdictions can change (e.g. TEVA). Because the firms are global in nature, their legal domicile and the scope of their business operations may differ considerably.

Market Capitalization. The sample is heavily weighted in favor of the technology sector which comprises 54% of the total market capitalization. Within the technology sector, a single technology firm comprises 27% of the total market capitalization, MSFT.

The Consumer Goods sector comprises 21% of total market capitalization, followed by Basic Material and Healthcare/Industrial at 14% and 11%, respectively.

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Financial Data Source. The data are sourced from the financial statements of the selected firms in either their filings with U.S. Securities and Exchange Commission (SEC), which are typically the 20-F or 10K Reports, or company annual reports that include the financial statements.

The financial statements are generally audited by one of the “Big 4” accounting firms (formerly known as the “Big 8”), Deloitte Touche, Pricewaterhouse Coopers, Ernst & Young, and KPMG, sometimes possibly in conjunction with a local accounting office, whether or not affiliated with one of the four firms.

For non-U.S. dollar currencies, the currency units used are local and are not converted into U.S. dollars.

Observation Period. The sample data are grouped into datasets of ten consecutive fiscal years, often referred to as the observation period (sample size of ten). The observation period includes the most recent fiscal year end for which financial statements are available at the time of analysis. For example, if the 2020 fiscal year end is 12/31/20, the dataset begins with fiscal year 2011, totaling ten observations on the data. Note that additional observations are required to compute the three-year shifts for the Financing Obligation Coverage analysis.

Fiscal Year. Most of the firms studied report their financial information on a calendar-year basis i.e. for one year ending on December 31 of that year. For firms that report their financial information on another basis, the fiscal year is generally advanced by one year. For example, if ABC Inc.’s fiscal year ends on December 31st, 2020, the fiscal year is calendar year basis and is referred to as fiscal year 2020 (or abbreviated as FY 2020). If XYZ, Inc.’s fiscal year ends June 30th, 2021, XYZ’s fiscal year is referred to as FY 2021. The remaining firms that do not report on a calendar year basis have fiscal years ending either in March, June, or September.

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Data Series. The fundamental series is net free cash flow (abbreviated NFCF) is constructed by subtracting cash outflows for capital expenditures and acquisitions from net cash from operations in each fiscal year (alternatively, NFCF is free cash flow (FCF) less acquisitions). As detailed in the Natural Rates framework section of Part I, this series is used to compute the ratio of NFCF to revenues in each fiscal year (NFCF/Revenues), as well as the natural rate of return (NRR) measure which is also referred to as rate of return on cost (RRC). The NRR (aka RRC) is computed by dividing NFCF by annual, synchronous cost, which is the total of the cash cost of sales, S, G &A, capital expenditures and acquisitions.

Financial Metrics. The question may arise as to why common metrics such as the dividend yield or dividend payout ratio are not examined in the financing obligations coverage analysis. First and foremost, analysis of dividend coverage alone is incomplete without consideration of debt service, since both compete for cash flow generated by the firm. Moreover, an appropriate value for net free cash flow (NFCF) must be determined; this is the subject of the methodological notes regarding theoretical models in the other sections of the Appendix. Second, if we ignore debt service (to repeat which is not viewed as appropriate), and focus solely on dividend coverage, the dividend yield cannot be viewed as indicative of the capacity of the firm to generate net free cash flow (NFCF) for investors. The dividend paid per share may not necessarily reflect the company’s underlying capacity to generate cash flow to pay dividends (and debt service); moreover, share prices can fluctuate considerably and the dividend paid per share can vary according to the number of shares outstanding which can be manipulated. Third, the dividend payout ratio is typically computed by dividing dividends by net income. However, net income is not considered suitable because net free cash flow NFCF is the primary source of payment for dividends (as well as debt service). The financing obligations coverage analysis in Part 2 can be viewed as a NFCF-based dividend payout ratio; also see the corresponding Appendix methodological discussion.

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However, to reiterate the above, dividends should be taken together with debt service for a more comprehensive measure of coverage.

Data Discrepancies. The terminology and composition of data may differ somewhat across firms. The data series are assumed to be reasonably uniform but not identical in all cases.

Although the financial information is of reasonably good quality, data discrepancies may be found that cannot be satisfactorily reconciled and have been noted in the reports as best as possible.

In certain cases, the composition of capital expenditures may not be fully clarified. Inclusion of capitalized interest, capitalized exploration expenditures or intangibles may depend on firm reporting practices. In some historical financial statements, figures for net cash from operations have been in a few cases inconsistently reported, and an arbitrary decision had to be made to select the most appropriate data for the sample.

Financial statements may be restated in subsequent years. The data used are subject to revisions and may be updated from time to time.

A2. Methodology Background Notes A brief clarification of certain terms begins this methodology Appendix.

Distortions. Typically, in the context of this research, distortions refer to yield, interest rate and asset price distortions (centering on debt and equities). In the natural rates framework employed here, these distortions arise from interest rates and yields departing from a measure of fundamental value defined as natural rates of return (NRR); these natural rates are termed net free cash flow (NFCF) after debt service. Spreads. A crucial aspect of distortion dynamics involves the widening of yield spreads by policy. When policy-influenced interest rates disconnect from natural rates of return, yield spreads widen, generating artificial returns that can encourage unsustainable debt growth and asset 184

inflation; together with debt monetization, interest rate manipulation ultimately self-cancels while fragilizing economic and financial systems to adverse shocks. Yield spread distortions are detailed in Part I (Natural Rates vs. Policy Rates) with actual firm financial data.

Yields and Rates. While Informally the term “yield” may often be used interchangeably with “interest rates” (often abbreviated as “rates”) they are distinct and briefly defined below. However, distortions in yields (e.g. yield-to-maturity on bonds) are viewed as having some relationship to distortions in interest rates on credit (e.g. bank loan rates); all are to some degree expected to be influenced by and related to policy rates set by monetary authorities. The yield-to-maturity (YTM) is the internal rate of return (IRR) for a stream of cash flow payments based on the initial cost of the bond.

Yield in an Equities Context. Dividend yields on equities are a common measure of yield (if dividends are paid); however, their relationship to fundamentals is considered specious because dividends paid do not necessarily reflect the capacity to pay them.

A superior measure may be the Net NFCF-Price Ratio, alternatively named the Net NFCF Yield or capitalization rate (abbreviated cap rate). This equity “cap rate” is viewed as a fundamental measure of yield and dividend-paying capacity because it derives from the net free cash flow of the firm, not from dividends paid. This yield measure should also account for debt service (therefore “net” as in “net of debt service.”). For details see this Appendix methodology of Asset Price distortion) Asset Price Distortion Debt Instruments. Although bonds are debt, they are also an asset. Because of an inverse relationship between yields and the price of debt instruments such as bonds, lower yields translate into higher bond values. Equities. In this framework, the yield distortion is marked by how the Net NFCF Yield (aka equity cap rate) noted above relate to the net natural rate of return (NRR), both net of debt service. For example, if the Net NFCF yield is in perfect alignment with the Net NRR, yield and stock price distortion are assumed to be zero. However, if the Net NFCF yield is lower than the Net NRR, then distortion is assumed to exist in 185

the form of an overpriced stock (with a yield that does not reflect the stronger underlying fundamentals).

Note: Another type of distortion indicator that is less the focus of this analysis concerns declining dividend yields and stock price rises associated with policy rates cuts. Moreover, a tight relationship between bond and dividend yields should not be categorically assumed, particularly given stock market disruptions where investors may flee to the purported safety of government bonds, leading to bond price rises while stock prices tumble.

Interest Rates (credit/loans). The stated interest rate of a bank loan is typically indicated on the loan document as a percentage of the amount lent. The bank loan rate is computed according to a bank formula to ensure bank profitability. The cost of funds to the bank is expected to relate relatively closely to monetary policy and policy interest rates.

Policy Interest rates (“official” rates). Policy rates are set typically by monetary authorities/central banks according to policy priorities (e.g. aid in price stability and economic/job growth). For example, in the U.S., the federal funds rate and the interest rate on excess reserves are set by policy. Monetary policy may also refer to their own estimates of the natural or neutral interest rate both as guides to setting policy rates, to be detailed further below. When policy interest rates are lowered, a downward impact on yields of various assets such as debt and equities is expected, in conjunction with an inverse relationship to the asset prices (upward).

Natural Interest Rates. The natural rates formulation in this research is fundamentally different from that pursued by monetary authorities and can be described as microeconomic and financial in nature. This concept was developed in the author’s Financial Distortion Dynamics (2018) and previous works, extending the financial element of the historical literature. Refer to the framework overview in Part I for details, as well as relevant methodological notes in the Appendix.

A2.1 Financing Obligations Coverage: Methodology Methodology 186

Data. The data is sourced directly from annual company financial statements (typically 10-K or 20-F).

Sample. Unless otherwise specified, the sample for each variable consists of ten observations corresponding to the company’s fiscal years (e.g. FY 2018-2009). Each dataset is a 10-year set of observations: For example, if FY 2018 is the most recent (current) fiscal year, then the current or latest period refers to the 10-year dataset comprised of the observations for FY 2018 back to FY 2009; the prior period refers to the 10-year dataset spanning FY 2017-2008 with the prior fiscal year being FY 2017. To perform all the analyses desired, a minimum of 13 fiscal years of observations is sought.

Company Selection. If more than one company is analyzed, they may be grouped either by 1. industry/SIC code, or 2. according to their proximity to the release of financial information (the latter causes firms from different industries to be reviewed together but is intended to keep the analysis as current as possible). Many of the firms are selected from the original database of firms surveyed in Equity Income Analytics (2014), an overview by the author of 32 global non-financial firms in 20 sub-sectors. The primary goal of sample selection was to diversify geographically and by subsector; audited financial information was also a key criterion.

Coverage Analysis. A common measure to gauge a firm’s underlying dividend capacity is dividend cover, which compares net income to dividends. An alternative is to use free cash flow in place of net income which is accrual-based. This dividend coverage report aims for a more expansive view of the dynamics underlying dividend coverage along with a more rigorous treatment of cash flow. The approach adopted here has the following main features: Net Free Cash Flow. Traditional free cash flow is reduced to account for various types of corporate acquisitions; this more conservative cash flow variable is referred to as net free cash flow NFCF (which may alternatively be referred to as equity income EI by the author). To estimate coverage, NFCF/Revenues is compared to a) dividends alone (the Coverage 1 category in the table in Section 1) or b) dividends and debt service combined DDS, also referred to as “financing obligations.”

This corresponds to the Coverage 2 category in the Section 1 table.

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Spatial Dimension. The behavior of the variables is expressed by the theoretical area or “space” that they occupy; the data are organized in terms of the relative frequency of their occurrence over the time period examined (typically 10 fiscal years); these results are then fitted to theoretical density curves that form the basis for an estimate of a central tendency of the historical data called “spatial centrality” or abbreviated centrality, detailed next.

(Spatial) Centrality is estimated for each 10-year dataset from the sample data and refers to an estimated value where the area occupied to both the left and the right of the point is exactly equal (50% each; re; symmetric stochasticization). Note that the exact location of estimate of centrality is not obvious from simple observation but may tend to be somewhere within the densest area of the curve. For the analysis of deviations (Charts 2 and 3), the term “prior” refers to the centrality estimate of the prior fiscal year based on the 10-year dataset up to and including that fiscal year.

It is recognized that other indicators of central tendency such as arithmetic averages could be used in analysis. However, this is not the approach adopted in these reports because an examination of the stochastic properties of the key variables suggests that in many cases there is no average. Even if an average does exist, limiting the study to averages may critically fail to capture the extent of risks, particularly where fat tails are concerned; see the section on Shortcomings and Qualifications for details.

Coverage with Spatial Centrality: Historical Example. The capacity of the firm to cover dividends and debt service DDS is illustrated here for an actual firm (KO) employing fitted distributions (also referred to as theoretical models/curves). This is a spatial representation for Coverage 2 from Section 1 above.

FY 2013 and FY 2014. A marked change in coverage capacity over two fiscal years is represented with fitted distributions of NFCF/Revenues (on the x-axis). The fiscal years FY 2013 and FY 2014 were selected to show examples of adequate and inadequate coverage. Recall that the underlying data for each distribution is 10 fiscal years of NFCF/Revenues up to and including the latest fiscal year of the chart.

The frame on the left is FY 2013 and suggests hypothetically adequate coverage that fiscal year (adequacy represented by green 188

color-coding). The frame on the right is FY 2014 and shows a marked shift to a tendency towards inadequate coverage (represented by red color-coding).

For further explanation of why, see the interpretation below.

Interpretation. Green and red color-coding between vertical lines signifies hypothetically adequate or inadequate coverage, respectively. The word ‘hypothetical’ is used because this type of format presents tendencies around a central estimate based on a theoretical model. Vertical Lines: Each curve features two vertical lines with a solid color in between them: 1)Spatial Centrality (NFCF/Revenues). One of the vertical lines represents a spatial centrality estimate of the NFCF/Revenues.

2)Financing Obligations DDS. A second vertical line represents the location of DDS/Revenues (i.e. the financing obligations) for the most recent fiscal year on the same chart. This vertical line can be anywhere around centrality: If DDS is to the left of centrality, the color coding is green, meaning hypothetically adequate because the historical centrality estimate is above the latest amount of financing obligations DDS. If DDS is to the right of centrality, the color coding is red, suggesting inadequate coverage because the historical centrality estimate is below the latest amount of financing obligations DDS. Shortcomings and Qualifications Relative Risk Profiles and Ranking. Ranking the firms according to measures of performance is generally avoided as 189

such an exercise may be of very limited value if not misleading.

Some general observations: 1. A greater number of combined negative factors from the table above could suggest greater risk.

For example, suppose that ABC Co. and XYZ Co. have identical estimates for coverage of debt service + dividends; weaker revenue growth for XYZ Co. may raise its risk profile relative to ABC Co. 2. Static vs. dynamic view: Assessing relative performance of firms may differ greatly between performance over a single period versus over many periods. 3. The best relative performer does not necessarily merit a positive evaluation in an absolute sense as all the firms may be faced with major challenges.

4. Any performance ranking also may depend critically on the weight placed on each factor which may be at least in part subjective. 5. This analysis overlooks intricacies of each firm and industry that can be gleaned from coverage by specialized analysts.

6. Comparisons between firms in different industries subject to distinct environmental factors and phases in business cycles may lead to inappropriate conclusions about relative performance; even companies in the same industry may not be entirely comparable.

Debt service is computed only for the upcoming fiscal year. Using the example of fiscal year 2018 financial statements, the debt service amount refers to the sum of: interest expense in the most current year’s income statement (i.e. FY 2018), and the current maturities due within the coming year as shown in the FY 2018 balance sheet. However, debt maturities typically extend out many years, and there may be a substantial amount of debt maturing in a fiscal year a few years hence that is not accounted for in the debt service figure used for this analysis, thus arguably understating debt service coverage capacity.

A principle of conservatism may be applied to decide on a best estimate of debt service figures. For example, if interest expense on the Income Statement consistently diverges significantly below the cash interest paid in the Cash Flow Statement, cash flow interest may be preferred.

Role of Political Economy. Interest costs may vary according to the firm’s loan origination location. Firms may be over-indebted due to a prior policy rate environment that incentivized them to borrow heavily. While inadequate debt service coverage may be 190

alarming, the firms’ recurring cash flow may still be strong and stable. Moreover, future political factors cannot be discounted: In recessions and amidst cascading loan defaults, political pressures can easily lead to an easing of the debt servicing burden through monetary policy and lower policy interest rates. In turn, higher inflation that nominally raises company revenues (nominally but not in real terms) can later develop. Insofar as debt repayment amounts are fixed, these higher nominal revenues coupled with cost-cutting can eventually mean improved debt service capacity over the longer term as the economy recovers.

Preemptive Debt Restructuring. While avoiding excessive debt should be a priority, if an over-borrowing situation can be identified early enough, management can undertake a preemptive debt restructuring which can include equity issuance to pay down debt, spin-offs and asset sales and renegotiation of loan terms: For additional details see Part 3 (Firm Level: Pre-emptive Debt Restructuring).

Theoretical models. The results of the studies are based on models that should be viewed as rough estimates and as a subjective approximation of the financial processes. Most of the highest-ranking fitted curves appear to be sub-exponential and devoid of moments, rather than normal (Gaussian). Crucially, many high or highest-ranking fits suggest a fat-tailed distribution FTD; moreover, the frequent occurrence of FTDs for the variables studied in these reports also points to the likelihood of fat tails.

Phantom Fat Tails. A source of confusion is how often wellfitted models are completely “invalidated” by subsequent values, and even by significant magnitudes: In the case of bounded curves (in one or both tails), out-of-sample values falling well beyond the bound and generating an “invalid” result is not unusual.

For unbounded curves (lower and upper), there is ample evidence that out-of-sample extreme values occur with far greater relative frequency than “should” occur according to the best-fitting model!

In conclusion, although an FTD may not be apparent from empirical testing, the possibility that one will be suddenly revealed cannot be discarded. In fact, in addition to this ghostlike quality, the presence of a (phantom) fat tail may be the appropriate default assumption for reasons explained further below.

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Cautionary “Tail.” If the “true” distribution is in fact fat-tailed, very high-impact extreme negative or positive events may be far more likely to occur. This poses a dilemma illustrated by the two side-by-side fitted curves below (thin tailed Normal at left and fattailed Cauchy at right). These two curves are different theorized versions of the same “reality”: A petroleum refiner’s NFCF/Revenues during its fiscal years from 2008 to 2017.

Suppose the Normal curve at left above is found to be the “best” fit according to certain testing procedures, and the Cauchy a lower-ranked alternative based on the same testing. Should the fattailed curve be disregarded as the “true” representation of reality?

We cannot be sure that the fat-tailed distribution should be categorically rejected in favor of the Normal. If we reject the assumption of FTD, a dangerous underestimation of the company’s risks is possible. For example, consider a potentially catastrophic event such as a cash flow loss worse than negative 25% of revenues: Under fat tails, this scenario occupies a nontrivial space of approximately 3% of the total area under the curve (located in the left tail). In contrast, the area under a Normal curve is estimated at nearly zero—i.e. virtually non-existent or impossible.

In conclusion, given considerable evidence for the presence fat tails (even if “phantom”) based on the author’s experience as detailed above, it is considered prudent to err on the side of caution by assuming a fat-tailed distribution as the default.

Concluding Remarks. Fat tails or not, even if the models accurately capture reality and sample sizes are adequate (they are usually not), much of real-world phenomena are expected to be distributed in ways that are essentially non-computable. Therefore, any predictive value for purposes of forecasting should not be 192

assumed. Accordingly, while security-level analysis is invaluable, it may be best supported by a strategy of optionality (e.g. buying equity puts). For an expert and better exposition of some of these points, see Cook, John D. (2018: “Six Sigma Events,” May 31; “Obesity Index” April 23, also citing Cooke, Roger, Fat-Tailed Distributions 2014), and studies by Taleb, Nassim, et al. (2020, 2012) to cite some examples.

A2.2 Stress Testing: Methodology Summary *This post provides introductory background information for the Cautionary Stress Testing analyses to avoid their repetition with each report. *Firm-level stress testing may offer insights into firm vulnerabilities to specified shocks, default exposure, recession, and policy actions.

Introduction Cautionary stress testing aims to provide a quick analytical resource of firm-level vulnerability to shocks, i.e. the extent to which an adverse shock could in theory become a reality. The process involves specifying scenarios of varying severity and performing simulations to generate estimates corresponding to levels of vulnerability or criticality for the firm.

Basic Scenarios. There are four basic scenarios grouped into two types of adverse shock: 1. Cash Flow Shocks; and 2. Revenue Growth Shocks; each type of shock consists of 2 scenarios of different severity levels. Each scenario occurs within a single fiscal year. Recall that “cash flow” refers to net free cash flow NFCF. Additional diverse scenarios may also be constructed as needed.

Critical Zones. The simulations generate an estimated “critical zone” (area) for each scenario: In theory, the larger this value, the greater the vulnerability and cause for concern. The range of critical zone values is zero to one with values in between shown as proportions (e.g. 0.015, 0.30, 0.56). The variables in the scenarios are NFCF/Revenues (also which may be abbreviated as NFCF) and annual year-over-year revenue growth REVG. Based on these critical zone estimates, qualitative approximations of the 193

firm’s vulnerability or criticality levels are assigned: e.g. high, moderate, low, etc., describing in words the extent to which a severe outcome could in theory become a reality. In the extreme case of maximum criticality, the critical zone estimate has the value 1; this means that the specified scenario corresponds to all possible outcomes (i.e. 100% or 1) and therefore in theory describes a scenario closest to an actual outcome. In contrast, a critical zone value of zero corresponds to no possible outcome (in theory an impossibility).

Qualifications. The estimates are referred to as “in theory” because they are based on theoretical models constructed by distribution-fitting. Consequently, the estimates or criticality levels should not be equated with predictions or forecasting. At best they can be viewed as imprecise indicators (also see the commentary on default model assumptions below).

Applicability. This firm-level analysis may also serve to assess vulnerabilities within financial institutions (business loans) and capital markets in addition to investment portfolios (securities).

Moreover, criticality levels may give insights as to the prospects for economic recession and policy countermeasures. Shocks experienced by individual firms may exert impacts on capital markets, financial institution credit portfolios and economic output as a function of various measures of their relative size (e.g. revenues, assets, market capitalization, debt outstanding).

Also refer to the Appendix (Financing Obligations Coverage: Methodology).

Theoretical Models. The stress test simulations are run on theoretical models constructed by distribution fitting. However, a default assumption of fat tails is generally made in favor of higherranking distributions for reasons detailed in the methodology and briefly reviewed next. Cautionary “Tail.” Consider the two curves shown below: A petroleum refiner’s NFCF/Revenues during its fiscal years from 2008 to 2017.

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These curves are different theorized versions of the same “reality”: Normal (at left) and Cauchy (at right). If the Normal curve is found to be the “best” fit versus a lower-ranked alternative such as the Cauchy based on the same testing, should a fat-tailed distribution be categorically rejected in favor of the Normal? If it turns out that the “true” distribution is in fact fat-tailed, the firm may be vulnerable to very high-impact extreme events which rejection will miss. Therefore, to err on the side of caution, a fattailed distribution is assumed as the default. Also, it should be noted that ignoring the presence of fat tails is made difficult due to substantial evidence for their presence in empirical work by the author.

For authoritative references and expert exposition of underlying concepts, see for example: Cook, John D. (2018: “Six Sigma Events,” May 31; “Obesity Index” April 23); Cooke, Roger, FatTailed Distributions 2014; Taleb, Nassim, Statistical Consequences of Fat Tails (2020) as well as Chapter 12 and Appendix II of Antifragile (2012) and other studies (also re: Pascual Cirillo, Rafael Douady). For additional analysis of the companies, also see the companion Financing Obligations Coverage report. Research by reliable equity and industry analysts should also be considered for further depth and perspective.

A2.3. Yield and Asset Price Distortion This appendix is in two parts: (1) definitions and methodological notes and (2) possible phases within distortion dynamics.

A2.3.1 Definitions and Methodological Notes

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Variables and abbreviations. This framework uses specific definitions and terms may be abbreviated within the framework context for conciseness. Net NRR is the firm’s estimated comprehensive cash flow-based natural rate of return after debt service, and is treated as a fundamental value indicator; Net NRR is also abbreviated “natural (rates of) return” or simply “return;” it may also be referred to as “underlying return” to emphasize its fundamental firm-specific nature. Net NFCF is estimated stabilized net free cash flow after debt service, sometimes abbreviated simply “cash flow.” The Price-Net NFCF ratio is an estimate of the firm’s valuation and may be abbreviated cash flow multiple; the reciprocal of the preceding ratio is the Net NFCFPrice ratio, a measure of the firm’s comprehensive cash flow yield based on its share price and which may be abbreviated NFCF yield, simply “yield,” or cap rate (itself an abbreviation for capitalization rate). “Net” indicates after debt service.

Chart A1.1. Review of Natural Rates of Return Natural Returns. The firm’s natural rate of return NRR is an estimate of the core net free cash flow NFCF divided by the firm’s cost (NRR=NFCF/Cost). Natural rates of return NRRs are as diverse as the entities which generate them and are estimated for 10-year periods in order to correspond to a hypothetical fully amortizing 10-year term loan. Net NRR. Any debt service requirements are deducted from the NRR to arrive at Net NRR (“net” referring to after debt service). Annual debt service is the sum of principal and interest paid throughout the year. Net NRR is assumed to be a fundamental source of a firm’s underlying value.

An example of the estimated Net NRR is shown in the chart below for five firms. The rate on the y-axis is percent (i.e. 0.10=10%) per annum.

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Of the five firms, only MSFT and QCOM appear to have a positive firm-specific net natural rates of return (NRR after debt service). Source: SEC filings and author computations.

Notes: 1. Cost is synchronous to NFCF (i.e. matched to the same fiscal year) and consists of the firm’s estimated total cash outflows.

For further detail on the components of NFCF, see section 1.1.1.

Natural Rates: Microeconomic-Financial Framework or the author’s Financial Distortion Dynamics (2018).

2. Estimates of variables are made from theoretical models based on 10 years of observations to identify a “core” natural rate of return for the firm (re: spatial centrality, location parameter). A stabilized value for net free cash flow NFCF can also be estimated based on the firm’s core NFCF and its revenue growth, from which debt service is deducted for the most recent fiscal year.

Also refer to the Appendix (Financing Obligations Coverage: Methodology and Stress Testing: Methodology).

Chart Details Details for Chart 1: Excess Stock Price Growth Excess growth is defined as natural returns Net NRR divided by the annual stock price growth in the latest fiscal year. The debt service amount is typically that of the latest fiscal year; however, depending on the magnitude of future debt maturities, the debt service figure may be adjusted to provide a more representative figure.

Details for Chart 2. Valuations 197

This chart presents an alternative to the conventional P-E ratio but deducts debt service. This alternative, termed the Price-Net NFCF Ratio (or multiple), is net of (after) debt service. This multiple is computed as follows: If Net NFCF is $1/share and the share price is $100, the multiple is $100/$1= 100x.

Chart A1.2. Pre-Debt Service. For a comparison of both ratios before debt service, the chart below displays the conventional Price-Earnings ratio next to the Price-NFCF (before debt service). The standard P-E ratios are trailing twelve months TTM as of this writing.

The conventional P-E ratio exceeds the P-NFCF ratio for PG and QCOM. Yields Detail for Chart 3. Yield Measure Alternative: Net NFCF Yield (aka Cap Rate) Taking the inverse of the Price-Net NFCF Ratio from above and seen in Chart 2 (Valuations) gives us an alternative measure of yield on equities based on the actual share price: The Net NFCFPrice Ratio. This ratio is alternatively named the Net NFCF Yield or equity capitalization rate (abbreviated equity cap rate or most often cap rate). The concept of cap rates is based on an income capitalization approach using the classic perpetuity valuation formula rc=π/P. In this framework this yield or cap rate’s numerator π is an estimate of stabilized per-share net free 198

cash flow NFCF after debt service and P is the actual share price.

Example: If the stabilized Net NFCF is estimated at $1/share and the actual share price is $100, the Net NFCF yield (equity cap rate) is $1/$100=0.01 or 1%, the proportion of the cash flow to the share price.

Note: In the conventional price-earnings ratio (P-E ratio) framework the inverse of the P-E ratio is the earnings-to-price ratio EPR, or earnings yield; however, the earnings are not reduced by debt service.

Dividend yield is the annualized dividend payment divided by the current stock price. However, the dividend yield is not necessarily indicative of the underlying capacity of the firm to pay those dividends. Net NFCF is the estimated underlying net free cash flow generated by the firm to support dividends as detailed above.

Also refer to the Appendix (Financing Obligations Coverage: Methodology and Stress Testing: Methodology). Although not the focus of this analysis, simplistically stated a dividend yield that exceeds the NFCF yield (equity cap rate) may suggest that the underlying cash flow of the firm offers weak support for the dividends being paid; investors may be compensated by the higher dividend yield for an additional risk of capital loss going forward.

Conversely, a dividend yield below the Net NFCF yield (equity cap rate) could suggest relative strength of the firm’s underlying cash flow with more room to grow dividends, thereby supplying future benefits to investors.

Details for Chart 4. Yield Distortion Definition. Recall that this framework hypothesizes that the Net NRR (net natural return after debt service) is a fundamental measure of a firm’s underlying value and return. Zero yield distortion means that the firm’s underlying return Net NRR is equal to the Net NFCF yield (equity cap rate) at the current share price. A yield distortion arises from a (positive or negative) difference between the underlying return and yield; both variables are “net,” meaning after debt service. For example, if Net NFCF is $1/share and the share price is $100, the Net NFCF yield (cap rate) is $1/$100=0.01 or 1%. Distortion Examples: Assume that the Net NFCF yield (cap rate) is 1% and Net NRR is 3%, then the yield distortion is simply the difference between them: 1% - 3%= negative 2% (=negative yield distortion). Assuming that the Net 199

NFCF yield is 1% and Net NRR is negative 3%, then the difference is 1% - (-3%) = positive 4% (=positive yield distortion).

Details for Chart 5. Capitalized Values and Asset Mispricing.

Yield distortions indicate a disconnect from fundamental returns that may explain asset mispricing. These distortions and capitalized values are computed next to show the extend of mispricing in the form of under(over) valuation. (Absence of mispricing=1; undervaluation <1; and overvaluation>1 or negative).

Undervaluation Positive Distortion depends on two conditions: The underlying return Net NRR < yield (cap rate) and Net NRR>0. For example, Net NFCF is $1/share and the share price is $50, the Net NFCF yield (cap rate) is =$1/$50=0.02 or 2%. If Net NRR of the firm is 1%, then: The yield distortion is this yield (cap rate) minus the return or 2%(1%) =1%.

The capitalized value per share (which equals the actual share price at the cap rate): $1/ (0.02) = $50.

The capitalized value per share based on the Net NRR is $1/ (0.01) =$100 (hypothetical share price).

Asset mispricing is $50/$100= 0.5 or undervaluation The undervaluation estimate suggests that based on the firm’s underlying return, more can be paid for the stock than it is currently being traded for. This is because the current share price is being discounted (downwards) by a distorted yield (cap rate) that is being pushed higher than the firm’s underlying return.

Overvaluation Negative Distortion Case. The underlying return Net NRR > yield (cap rate).

For example, Net NFCF is $1/share and the actual share price is $100. The Net NFCF yield (aka cap rate) is 0.01 or a yield of 1%.

If the Net NRR is 3%, then: The yield distortion is this yield (cap rate) minus the return: 1%3%=-2%.

The capitalized value per share (which equals the actual share price at the cap rate): $1/ (0.01) = $100.

The capitalized value per share based on the Net NRR is $1/ (0.03) =$33 (hypothetical share price) 200

Asset mispricing is $100/$33= 3.3x overvalued.

Positive Distortion Case is based on two conditions: The underlying return Net NRR < yield (cap rate) and Net NRR<0 (i.e. a negative net return).

Net NFCF is $1/share and the actual share price is $100, the Net NFCF yield (aka cap rate) is 0.01 or a yield of 1%. If the Net NRR is -3%: The yield distortion is this yield (cap rate) minus the return or 1%(-3%) =+4%.

The capitalized value per share (which equals the actual share price at the cap rate): $1/ (0.01) = $100.

The capitalized value per share based on the Net NRR is $1/ (0.03) =-$33 (hypothetical share price) In this case, the hypothetical share price is negative, therefore asset mispricing is negative and is treated as overvaluation since actual share prices are zero or positive.

The overvaluation estimates suggest that based on the firm’s underlying return, less should be paid for the stock than it is currently being traded for. This is because the current share price is being discounted (upwards) by a distorted yield (cap rate) that is being pushed below the firm’s underlying return.

Note: Recall that the yield here refers not to the dividend yield (from actual dividends received), but to the Net NFCF yield based on the firm’s stabilized net free cash flow after debt service.

Causality. Because firms are diverse, they are not expected to have uniform natural rates of return. Speculative fervor, faulty perceptions, and so-called “castles in the sky” (Malkiel 2016) all may help explain asset mispricing in the form of overvaluation.

Crossover Effect. Centrally planned monetary policy may exert a certain “gravitational pull” on yields of equities, roughly aligning them with policy-influenced rates on debt instruments.

Accordingly, individual characteristics of the firms may tend to be masked by this external, potential overriding force. Of course, it is recognized that the reality is complex and significant differences between debt instruments and equities may arise due to various factors including relative perceptions of risk of the security, maturities of debt instruments; and bond yields of different maturities to the extent influenced by monetary policy.

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Chart A1.3. A crossover effect from yields on debt to equities as noted above may not be easily identifiable. To test this thesis, the following chart compares the NFCF yields before debt service for four firms to 10-year Treasury Yields as of early 2019 (at a time when 10-year Treasury yields were approximately 2.5%):

Apart from F with an arguably higher risk profile, three firms (KO, MMM, XOM) tend to be in a rough range of conformity with the 10-year Treasury. Note: The NFCF yield is termed “nominal cap rate” in the chart.

A2.3.2 Distortion Dynamics: Transitions and Trajectory Borrowing Spreads: Policy vs. Natural COF. This section provides a brief overview of the transitional process in which the incentives to borrow and lend tend based on cash flow would be expected to diminish. Recall that the cost of funds COF influenced by monetary policy is referred to as the policy COF. In a natural rates environment (i.e. a hypothetical undistorted free market for loanable funds), the natural COF is another name for natural rates of interest NRI which are firm-specific and diverse (i.e. nonuniform across firms); the NRI are assumed to be half of the NRR.

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Both the policy COF and natural COF borrowing spreads are shown in the charts below employing the financial data for the firms. A positive borrowing spread, defined in this framework at the Net NRR less the cost of funds (whether policy or natural COF is assumed), is expected to offer some incentive for firms to borrow more.

Chart A2.1. Borrowing Spreads at Policy COF. Cutting policy rates can lower the cost of funds and provide additional impetus to borrow. For simplicity, assuming the 10-year Treasury yield to be a rough approximation to a policy cost of funds for a loan with a 10-year amortization, the following borrowing spread chart can be constructed:

Three of the firms (DEO, HMC and PG) evidently no longer have a positive borrowing spread based on the approximate current policy rates (as of this writing). To induce them to borrow further, policy rates could be reduced further by an expansionary monetary policy, but the magnitude of the rate cut required may be difficult to achieve.

Chart A2.2. Borrowing Spreads at Natural Cost of Funds.

Based on the natural rates framework, the borrowing spread is shown as follows.

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In the natural COF case, now only MSFT has a significant borrowing spread. While QCOM is still slightly positive, it is close enough to zero such that the benefit of additional debt accumulation is questionable.

Note: In the above chart, the natural COF is labelled Natural Rate of Interest NRI and is composed of 1. the policy COF (assumed to be the 10-year U.S. Treasury yield in the example) and 2. the interest rate gap which is the difference between the NRI and the policy COF. Lowered policy COFs help artificially widen the borrowing spread, allowing corporate borrowers to benefit from a greater spread on their borrowings than otherwise possible and facilitating a transfer of income from savers and lenders to these borrowers.

Collateral Lending. Once cash flow lending is no longer a viable alternative, collateral lending may be relied upon for further borrowings. An advantage to borrowing/lending on collateral is the payments may essentially become interest only, easing debt service coverage but only provided that the collateralized debt can consistently be rolled over. A challenge for borrowers is that their cash flow may be not be enough to accumulate liquid assets that can be adequately collateralized. In such a case, financing in other forms may be sought: Treasury stock can be pledged as collateral; rising equity prices can encourage further lending but depends on channel of debt monetization. Evergreening: Banks 204

can indefinitely roll over permanent working capital lines of credit and other bank loans. (Monetary) Inflation can provide a type of “inflation return” subsidy to firms by depreciating the real value of their debt and inflating their earnings (revenues, cash flow) over longer periods of time. Collateral value and Liquidity.

Monetary authorities may intervene by purchasing bonds to suppress yields thereby keeping bond prices stable against various factors including: Oversupply relative to demand at the current price (yield), underlying weakness of corporate borrowers, or significant inflationary expectations. Even sovereign bonds may face challenges in the face of rising inflationary expectations.

However, a counterbalancing force against bond market weakness is the so-called “flight to safety” in Treasuries when equity markets falter; the reassurance of bond stability can support their value as collateral to a point until liquidity demands become too great.

Policy Risks and Possible Outcomes. As noted, the integrity of a post cash-flow financing phase requires that the collateral value remain relatively intact and that the debt be continuously rolled over or refinanced. However, a sustained policy of this nature may involve a corporate “zombification” state marked by a slowing of innovation, economization and efficiencies coupled with creeping financial distress. While there may be a crossover benefit in terms of deflationary characteristics in consumer and producer prices,* eventually, impairment of collateral values is likely to be unavoidable, either through outright default or bankruptcy (corporate debt) or resurgence of consumer price inflation (affecting longer-dated sovereign debt). Initially, monetary authorities may purchase impaired assets outright from the banking system and other entities as part of large-scale asset purchases (re: LSAPs). Such policies may help the financial system weather insolvencies and remain in compliance with Basel III liquidity coverage ratio and other requirements for some time.

Case-by-case, policymakers may also act ahead of bankruptcy to bail out bondholders in the form of selective takeovers or provisional nationalizations. An inflationary recession marked by tempered consumer price growth, corporate and wage stagnation relative to asset price rises could be sustained for some time.

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However, if pre-emptive debt restructuring efforts are not undertaken, eventually this scenario may transform into some form of unstable inflationary-deflationary recession/depression affecting both asset prices (alternating bubbles and crashes) * and consumer prices (inflationary tendency). Over the longer term, unsustainable debt loads may result in a clustering of defaults and bankruptcies that trigger equity market and supply shocks* coupled with further declines in tax revenues and associated rising budget deficits. Assuming no Black Swan events, even a standard trajectory may lead to supply interruptions, which when coupled with debt monetization from accelerating deficit financing, may spark markedly higher inflationary pressures into consumer prices.* Bond values and yields may be adversely impacted by inflation expectations, leading to rising borrowing costs that at some point cannot be fully suppressed by further monetary interventions, triggering a further cascading of defaults and declining bond values including sovereigns. As the systemic debt bias unwinds, a semblance of natural rates may eventually reemerge if permitted.

Additional Commentary Clarification of points made in the previous paragraph that were marked with an asterisk (*) are provided here: 1. Re: “…deflationary characteristics in consumer and producer prices…” This is not intended to mean deflation in the sense of falling prices, or disinflation (a slowing in the rate of price inflation), but continued price growth; however, this price growth is expected to be less than would have otherwise been the case (unobserved).

2. Re: “higher inflationary pressures into consumer prices…”

Monetary inflation that sparks and sustains general price inflation provides a "return" or subsidy for indebted firms in terms of nominally rising revenues and cash flows and the depreciation of the real value of their debts. Also, an inflation- weakened currency (subject to currency pairs, external financing conditions and overseas inflation rates) would also be expected to put upward pressure on import prices, feeding into overall consumer price inflation.

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3. Re: “alternating bubbles & crashes” “equity market and supply shocks”: Drivers of instability may arise from opposing sources, i.e. weakened earnings and defaults and possible price-earnings compression versus monetary expansion into asset prices (priceearnings expansion) via central bank debt monetization, outright purchases of equities and other means.

Sources of secular price declines that may act counter to central bank efforts to reflate economies can include technological progress that improve the efficiency of energy and other input use (re: McAfee 2019).

Rickards (2017) points to the depressive impact of uncertainty about policy (e.g. taxes, regulation) on consumer and business investment activity. Moreover, policy measures that involve debt, subsidies or grants to increase economic activity initially can have opposite effects later: For example, student loans (linked to university systems and the surrounding communities) or the rapid construction of “ghost cities” and other government-induced development projects. These cash infusions may be associated with price rises and increased activity for some time, but when the economics and debt service capacity are not sustainable, a reversal deflationary impact can be expected.

4. “Supply shocks” refer to scenarios of sporadic or sustained reductions in the supply of goods: e.g. in which defaulted or bankrupt firms cease or severely curtail production, also the impact of a weakening currency that results in the slowing or cessation of imports.

To counteract further debt accumulation and policy circularity concerns highlighted in the previous section, rather than lowering policy rates to further accommodate debt service, timely debt restructuring could offer a less destabilizing solution; for further details and examples see Part 3 (Firm Level: Pre-emptive Debt Restructuring).

A3. Physical Yields and Returns: Agronomy Field Study This section aims to highlight the link between financial sector activity and the actual physical production processes underlying economic output, yields and returns. This last section presents a

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study in agronomic science conducted by the author at the University of California, Davis.

Interactions among Input Levels, Management Practices and Yield of Transplanted Leaf Lettuce Introduction California is the largest production center of crisphead lettuce (Lactuca Sativa) in the world. Most of the U.S. supply during the winter originates in the Imperial Valley and the SalinasWatsonville area provides the bulk of California’s summer shipments. In California’s Imperial Valley lettuce is planted from September to November and harvested from mid-December to mid-March (Note 1). Lettuce is primarily grown on fertile, welldrained soils. Another major consideration is temperature. Lettuce growth in midwinter is slowed by cold weather as nutrient uptake is reduced and thus head size is reduced (UCAE 1978: 6). Furrow irrigation is the most common method of irrigating lettuce.

Lettuce is highly responsive to nitrogen and commercial lettuce growers in California use high fertilizer application rates. Early lettuce growers in California produced organically grown lettuce crops, using animal manures, fishmeal, tankage, dried blood and cottonseed meal as nitrogen and phosphorous sources. Green manure legume crops such as cowpeas, vetch and alfalfa were common (UCAE 1978: 22). Land preparation in the Imperial Valley is done with discs and landplanes, and lettuce beds are made with double-faced listing shovels.

The objective of the investigations reported in this paper was to evaluate the effect of two levels of fertility management, organic manure and chemical fertilizer (N, P and K), and two levels of tillage, manual and mechanical, on the growth and yield of transplanted leaf lettuce, and on labor use and distribution. The experiment was conducted from April 6 to May 11, 1985 and field study results were submitted June 11.

Materials and Methods 208

The experiment site was located at the University of California, Davis at approximately 38 N latitude and 122 W longitude. The experiment was performed on Yolo Loam, a deep, well-drained soil, neutral in reaction, and well supplied with all essential nutrients except nitrogen (re: Williams 1966). Maximum and minimum soil temperatures at 10 centimeters were 78o F (5/1-5/5) and 61o F (4/22) during the period of experiment, respectively (re: Appendix climatology data). At 20 centimeters, maximum and minimum temperatures were 72o F (5/2-5/6) and 61o F (4/22), respectively. Precipitation during the experiment was 0.36 inches, and total evaporation was 12 inches. Maximum and minimum air temperatures at two meters were 90o F (4/14) and 37o F (4/22), respectively.

The experiment used a 2 x 2 factorial design with three replications. Plot size was 12.5 X 50 feet, and total net area was 150 X 50 feet. The experiment used two different levels of tillage, manual and mechanical, and two levels of resource inputs, organic manure and chemical fertilizer (N, P and K). Manual tillage consisted of two passes with a rototiller in second gear in land preparation, and manual hoeing in seedbed preparation.

Mechanical tillage consisted of land preparation with a tractordrawn harrow-disc (two passes), and seedbed preparation with a tractor-drawn lister (Note 2). Organic manure treatments consisted of application of composted manure, which was broadcast, raked and incorporated with the rototilling operation in the manual tillage, and with the discing operation in the mechanical treatments. On April 23, fish emulsion (5% N content) was applied at a rate of 250 ml per plot. Chemical fertilizer treatments consisted of preplant NPK application by broadcasting and incorporation with the rototilling or discing operation.

Application of N (as NH4NO3) was 1.3064 kg/plot of elemental nitrogen, phosphorus, as CaCH2PO4 was 0.5806 kg/plot and potassium as K2SO4 was 0.5806 kg/plot (both quantities were elemental as well). On May 1, a side dressing of NH4NO3 containing 0.6532 kg/plot of elemental nitrogen was applied.

209

The lettuce, Black seeded Simpson, was sown on March 1 in seedling trays, grown in the greenhouse and transplanted on April 6 at an attempted intra-plant spacing of 20.3cm. Furrow irrigation began on this date, with irrigation twice per week during the experiment. Bird nets were used, and the experiment site was fenced in with chicken wire, with soil moved to cover the fence base against intrusion by small animals. On May 1, manual weeding took place. Irrigation ended on May 7, and the lettuce was harvested on May 11. No insecticides were used in the experiment.

Sampling was done in the two outside rows of each treatment plot.

At weekly intervals (Dates: 4/12, 4/19, 4/26, 5/3, 5/10) ten plants were taken from each of the sample rows for dry matter weight measurement. The two center rows were left intact for harvest. At harvest, 30 feet of the most uniform section of each of the two center harvest rows (a harvest area of 1.524m X 9.144m) was selected for measurement of fresh weight yields, which was done on platform scales in the field. Harvest sample plants were trimmed before weighing, while dry matter sample plants were not trimmed. Dry matter weight was measured after oven-drying of the untrimmed sample plants. Common management components of the experiment were weeding, irrigation, transplanting, and harvest.

Results and Discussion Agronomic aspects. The tests showed the chemical fertilizer significantly improved the rate of plant growth in terms of dry matter weight (gm/plant;) (Chart 1: Dry Matter Accumulation; Chart 2: Least significant difference values at LSD 0.05) and significantly increased fresh weight yield (kg/ha) (Table 1). The dry weight per plant and the fresh weight yield were both significantly higher at harvest for the chemical fertilizer lettuce than for the organic manure lettuce. There was no significance between tillage treatments, thus the only significant effect in terms of growth was due to fertility management. A large observable difference was noted between the organic manual and organic 210

mechanical fresh weight yields, however. This observable difference might be worth further investigation because of possible interaction effects between bed size and organic matter.

The wider manual beds might have provided a greater volume for roots to search for nutrient sources than the narrower mechanical beds.

A post-harvest bitterness test was conducted and showed that chemical lettuce was generally considered more bitter. Increased stress on lettuce can increase bitterness. The organic plots were observed to have more soil moisture due to the greater content of soil organic matter. The chemical fertilizer plots, with less organic matter content, were found to be dryer and harder from lack of soil moisture. Since nitrogen was not limiting, this suggests that the chemical fertilizer lettuce had water stress which might have caused plant bitterness (Note 3).

The nitrogen content of the organic manure was 1.5% of the manure dry matter weight. Yields might have been greater for the organic lettuce if better quality organic manure had been selected for the experiment.

Chart 1. Dry Matter Accumulation

211

Note: Dates listed are sample dates Chart 2. Least Significant Difference Values

It is likely that the manure had lost nitrogen from leaching and volatization in storage in the field, and the relatively higher carbon to nitrogen ratio might have tied up nitrogen in the organic plots.

Plant population a harvest was highest for the chemical fertilizer mechanical treatment and lowest for the chemical fertilizer 212

manual treatment (Table 1). Attempted interplant spacing at transplanting was 20.3 centimeters, but later measurement showed that chemical fertilizer mechanical interplant spacing was the least, 21 cm, while chemical fertilizer manual was the greatest, 22.2 cm.

This suggests that the variation in interplant distance between these two treatments affected the plant population at harvest and thereby the total fresh weight yield per hectare in favor of the chemical fertilizer-mechanical treatment. Further investigation should be conducted to determine yield per hectare of these treatments with identical interplant distance. The mean fresh weight (Table 1) showed lower values for organic lettuce, suggesting nutrient stress due to lack of nitrogen.

Table 1. Plant Weight, Population and Yield at Harvest

Treatment OM/MAN OM/MECH CF/MAN CF/MECH

Plant Weight, Population and Yield at Harvest Fresh Weight Plant Population Fresh Weight Yield 187 61069 11395.5 151 61355 9257 219 59130 12952.7 216 62503 13513.4

Detail Fresh Weight: Plant Population: Fresh Weight Yield:

mean plant weight (grams/plant) per hectare kg/hectare

(LSD 0.05)

Air temperature does not appear to have had adverse effects on lettuce growth. According to Gardner and Pew (1979), when mean air temperature falls below 13o C (39oF) for a week or longer, lettuce growth and nitrogen uptake are reduced. But at no point during the experiment did this occur (re: Appendix climatology data).

Several pests were observed during the experiment. Ground squirrels (Sytellus Beechyii) penetrated the fence, but no significant damage occurred. No bird damage was observed.

Cabbage loopers (Trichopolus Ni) were evenly distributed on all plots, with a ratio of one per 20 plants, approximately. Few cucumber beetles were observed. Aphids infested the plots, but to 213

a greater degree in the chemical fertilizer plots (8-10/leaf) than in the organic manure plots possibly due to the greater nutrient content. Ladybugs from the surrounding area were observed as well and fed on the aphid population. A high population of pin nematodes was observed, but they are not known to attack lettuce.

Many other species of nematodes were observed but none attacked the lettuce roots. At transplanting, several tomato plants, Early Pack 707, were planted in order to observe root knot nematode population, a nematode known to attack lettuce. Although tomatoes are more susceptible to root knot nematode infestation, none were observed, and no lesions were found on the plant roots, which were in healthy condition. In sum, no significant yield damage occurred due to pests, but the high aphid population would have reduced the selling quality of the chemical fertilizer lettuce.

Economic Aspects. Data showing inputs, labor and other costs, as well as yield, gross return and net return for each of the treatments are presented in the partial budgets for Yolo County and a hypothetical experiment in Egypt (Table 2, Table 3). Total gross return is equivalent to the yield multiplied by an assumed $0.75/kg fresh weight price for lettuce in Yolo and $0.25/kg for Egypt (with lower assumed labor costs as well: manual labor at $0.50/hour for Egypt, $4.50/hour for Yolo, and tractor driver costs at $2.00/hour for Egypt and $12.00/hour for Yolo). Gross returns between chemical fertilizer plots and organic manure plots were significantly different. Chemical fertilizer-mechanical treatments had the greatest gross return and organic-mechanical the least in both Egypt and Yolo. Average gross return of chemical fertilizer plots was 28% higher than that of organic plots in both Yolo and Egypt. Average net return (Note 4) for chemical fertilizer plots was $1903.85/ha, while organic manure plots had a negative average net return of -215.9/ha in Yolo. In Egypt, organic manure plots did not produce negative average net returns, but chemical fertilizer plots gave 124% higher average net returns. In both Egypt and Yolo, the chemical fertilizer treatments yielded the greatest net returns, at $1341.35/ha and $2073.6/ha, respectively, 214

while organic manure-mechanical performed the worst of all treatments in terms of net returns for both Yolo and Egypt.

Table 2. Partial Budget, Yolo Partial Budget for Transplanted Leaf Lettuce Experiment in Yolo County: Per Hectare Basis Operation/Input Treatment OM/Man OM/Mech CF/Man CF/Mech Labor Cost Labor Cost Labor Cost Labor Cost (hrs) ($) (hrs) ($) (hrs) ($) (hrs) ($) Land/Seedbed Prep Manual bedding 71.7 322 0 71.7 322.8 0 0 Rototilling 27.3 122.85 0 27.3 122.85 0 0 Discing 0 1.05 12.6 0 1.05 12.6 Listing 0 1.2 14.4 0 1.2 14.4 SUBTOTAL 99 445.73 2.25 27 99 445.73 2.25 27 Transplanting Labor Transplants SUBTOTAL

774.9 774.9

Fertility Mgt Inputs: Manure (133.9 T/ha) N (?kg/ha) P (?kg/ha) K (?kg/ha)

3487 320 3807

3858.75 320 4178.75

774.9 774.9

3487 320 3807

857.5 857.5

86

103.3 20.1 5.7 0 0 129.1

464.85 90.45 25.65

Weed Control Labor

464.85 90.45 25.65

934.85

12.9

58.05

50

225 100 383.05

Harvest Plants/ha Seconds/plant Labor/ha SUBTOTAL

110 110

495 495

TOTAL (Labor/TVC)

1175.9

6065.63

61069 6.5

11396 8547 -2136.75 6410.25 344.62

168.7 40 20

86

103.3 20.1 5.7 0 0 129.1

62.9

3858.75 320 4178.75

267.9 168.7 40 20

Labor Manure hauling Manure spreading Spraying Fish emuls.

Broadcast NPK Band N topdr.

SUBTOTAL

Yield (kg/ha) Gross Return -25% Share crop Net Gross Return Net Return

857.5

267.9

Fish Emulsion N (?kg/ha)

Irrigation Labor Pumping cost SUBTOTAL

857.5

934.85

0 0 0 8.6 29.3 37.9

12.9

58.05

50

225 100 383.05

62.9

61355 6 102.2 459.9 102.2 459.9 1154

5983.5

9257 6942.75 -1735.69 5207.06 -776.9

215

38.7 131.85 399.29

0 0 0 8.6 29.3 37.9

38.7 131.85 399.29

12.9

58.05

12.9

58.05

50

225 100 383.05

50

225 100 383.05

62.9

59130 7 115 517.5 115 517.5 1089.7

5552.5

12953 9714.75 -2428.69 1286.06 1733.5

62.9

62503 6.9 119.8 539.1 119.8 539.1 1080.35

5527.2

13512.7 10134.5 -2533.6 7600.8 2073.6

Table 3. Partial Budget, Egypt Partial Budget for Transplanted Leaf Lettuce Experiment in Egypt: Per Hectare Basis Operation/Input Treatment OM/Man OM/Mech CF/Man CF/Mech Labor Cost Labor Cost Labor Cost Labor Cost (hrs) ($) (hrs) ($) (hrs) ($) (hrs) ($) Land/Seedbed Prep Manual bedding 71.7 35.87 0 71.7 35.87 0 Rototilling 27.3 13.65 0 27.3 13.65 0 Discing 0 1.05 2.1 0 1.05 2.1 Listing 0 1.2 2.4 0 1.2 2.4 SUBTOTAL 99 49.52 2.25 4.5 99 49.52 2.25 4.5 Transplanting Labor Transplants SUBTOTAL

774.9 774.9

Fertility Mgt Inputs: Manure (133.9 T/ha) N (?kg/ha) P (?kg/ha) K (?kg/ha)

387.45 320 707.45

428.75 320 748.75

774.9 774.9

387.45 320 707.45

857.5 857.5

86

103.3 20.1 5.7 0 0 129.1

Weed Control Labor

418.45

12.9

6.45

50

25 100 131.45

Harvest Plants/ha Seconds/plant Labor/ha SUBTOTAL

110 110

55 55

TOTAL (Labor/TVC)

1175.9

1361.87

61069 6.5

11396 2849 -712.25 2136.75 774.88

168.7 40 20

86

103.3 20.1 5.7 0 0 129.1

62.9

51.65 10.05 2.85

428.75 320 748.75

267.9 168.7 40 20

Labor Manure hauling Manure spreading Spraying Fish emuls.

Broadcast NPK Band N topdr.

SUBTOTAL

Yield (kg/ha) Gross Return -25% Share crop Net Gross Return Net Return

857.5

267.9

Fish Emulsion N (?kg/ha)

Irrigation Labor Pumping cost SUBTOTAL

857.5

418.45

0 0 0 8.6 29.3 37.9

12.9

6.45

50

25 100 131.45

62.9

51.65 10.05 2.85

61335 6 102.2 56.1 102.2 56.1 1154

1359.25

9257 2314.25 -578.56 1735.69 376.44

216

4.3 14.65 247.7

0 0 0 8.6 29.3 37.9

4.3 14.65 247.7

12.9

6.45

12.9

6.45

50

25 100 131.45

50

25 100 131.45

62.9

59130 7 115 115

57.5 57.5

1089.7

1193.6

12953 3238.25 -809.56 2428.69 1235.07

62.9

62503 6.9 119.8 59.9 119.8 59.9 1080.35

1192.3

13512.7 3378.2 -844.55 2533.65 1341.35

Partitioning of total variable cost (TVC) of each treatment by field operations shows that variable cost as a proportion of TVC for weeding, irrigation and harvest were similar across treatments.

(Chart 3) However, land preparation variable costs were only 0.40.5% of TVC in the mechanical tillage plots (due to rounding, these values are shown as 0% and 1% in the table below) compared to 7-8% in manual tillage plots. Fertility management was only 7% of TVC in the chemical plots compared to 15-16% in the organic manure plots. Transplanting was by far the costliest operation for all treatments, representing from 63% to 76% of TVC. As a percentage of TVC, transplanting variable costs were somewhat lower for manual tillage (63-68%) than for mechanized tillage (70-76% of TVC). Refer to Chart 3 next.

217

Chart 3. Field Operation Cost (% of TVC by Treatment)

Labor cost ranged between 91% to 100% of variable cost of each operation for all but two operations: Irrigation (between 58-59% for all treatments) and fertility management (62% for organic manure and 42% for chemical fertilizer treatments, respectively); labor cost data are reported here without a corresponding table.

Labor use and distribution data (Table 4) show that transplanting was the greatest labor sink for all treatments, absorbing from 66% to 74% of total labor used. The organic plots absorbed more labor overall in terms of person-hours than the chemical fertilizer plots.

218

A similar percentage of total labor was used in weed control, irrigation and harvest for all treatments. Mechanical tillage plots absorbed less labor (0.2%) for land seedbed preparation than manual plots (8-9%) and chemical fertilizer plots absorbed less labor (3.5%) than organic manure plots (11%) in fertility management.

Table 4. Labor and Labor Distribution

TREATMENT

Labor and Labor Distribution by Treatment and Operation OM/MAN OM/MECH CF/MAN ph/ha %TL ph/ha %TL ph/ha %TL

CF/MECH ph/ha %TL

FIELD OPERATION Land-Seedbed Prep 99 8.4 2.25 0.2 99 9 2.25 0.2 Transplanting 774.9 66 857.5 74.3 774.9 71 857.5 79.4 Fertility Mgt 129.1 11 129.1 11 37.9 3.5 37.9 3.5 Weed Control 12.9 1 12.9 1 12.9 1 12.9 1 Irrigation 50 4 50 4.3 50 4.6 50 4.6 Harvest 110 9 102.2 8.9 115 10.5 119.8 11 Totals 1176 99 1154 100 1090 100 1080 100 Notes: ph/ha and %TL refer to "person-hours per hectare" and "percentage of total labor," respectively.

Total of percentages may not add up to 100% due to rounding.

The data presented here would suggest that alternative methods of transplanting to improve labor use efficiency should be researched, including the possibility of direct seeding instead of transplanting.

Labor scarcity has been noted as a problem in Egyptian agriculture, and more efficient methods of transplanting, or direct seeding, would be advisable to prevent labor bottlenecks in lettuceproducing rural areas. As well, reducing labor use and costs would make lettuce growing a more profitable enterprise in both Yolo and Egypt since it is the greatest labor sink. For example, if labor costs per hectare could be reduced by 50% for the transplanting operation, Yolo chemical fertilizer-manual farmers could double their net returns per hectare (Note 5). Egyptian chemical fertilizer219

manual farmers would gain less, only 15% over previous net return per hectare. (both values exclude increased cost for the alternative planting method and therefore should only be used as a rough estimate).

Because fertility management was determined to be the significant factor in plant growth, more consideration should be given to the value of chemical fertilizers. Comparison of relative investments for fertility management inputs shows that N-P-K chemical fertilizer costs less, $228.00/ha, than organic applications of manure and fish emulsion, $353.00/ha yet produced significantly higher yields. As well, the reduced labor use and labor costs per hectare associated with chemical fertilizer use suggests the advantage of chemical fertilizers in fertility management. As previously noted, chemical fertilizer application reduced fertility management variable costs to only 7% of TVC in both chemical fertilizer treatments compared to 15% of TVC in the organic manure treatments (Chart 3). Thus, all these factors should be considered in making recommendations on fertility management for low income Egyptian farmers.

Tillage was not found to be significant in explaining plant growth, but some comments on method of tillage can be made. The poor yields and net return of the organic manure-mechanical treatment compared to the organic manure-manual treatment for the reasons already discussed suggest that mechanical tillage should not be adopted in organic manure farming systems until further research is done to explain the large observable yield differences. Although the chemical-fertilizer-mechanical treatment had greater fresh weight yields per hectare than the chemical fertilizer-manual treatment, further investigation should determine how yields would differ if interplant spacing of both treatments were the same.

In sum, it would not be advisable to favor mechanical tillage over manual tillage until further research is conducted. Without mechanization, Egyptian farmers could still benefit considerably for chemical fertilizer applications. Farmers might then choose independently to invest profits from their chemical fertilizer 220

systems into machinery for mechanized tillage operations due to labor scarcity and rising labor costs in agriculture. For farmers already considering or using mechanical tillage, perhaps increasing bed width and reducing clod size and numbers through more discing would help increase yields, although such benefits have yet to be determined by experimentation. Another important consideration concerning mechanization in Egypt is legal: Land must be divided up for certain family members. If the average Egyptian farm size is presently one acre, benefits from mechanization, which depends on land size for success, will decrease over time.

The gross returns/variable costs ratio is a useful measure of relative profitability of a farming system where, where values above one indicates positive returns to farmers. Values have been calculated for each treatment in Yolo and Egypt; see Chart 4 next.

Chart 4. Gross Returns to Variable Cost Ratio

Given the assumptions for Egyptian and Yolo prices and labor costs, each of the treatments are relatively more profitable in Egypt than in Yolo in terms of the gross returns/variable cost ratio.

Ratios of 2 and above in the Egypt case may be considered above average (Note 6).

221

In closing, the research conducted in this experiment had been particularly useful in determining the value of chemical fertilizers in fertility management and in comparing the four treatments of the experiment. As an extension of the investigations reported in this paper, other topics of future research might include: interactions between bed size and organic manure, lettuce plant yields using higher quality organic manure, organic manure contribution to soil moisture in hot climates, and long-term effects of chemical fertilizer and organic manure use on soils.

Notes 1. Information on lettuce production in California and the Imperial Valley was provided by V. Rubatsky, Vegetable Crops Cooperative Extension, UC Davis.

2. A D4 Caterpillar tractor was used for the discing and a wheeled John Deere for the listing.

3. It should be noted that there were no apparent effects of such water stress on yield.

4. Net return = gross return net of total variable costs (TVC) and 25% share crop.

5. Proper analysis requires accounting for the cost of any additional machinery.

6. See Cassman, et al., (1984:19) for values in Egyptian soybean farming.

Appendices. Climatological Data for Davis, California

222

Table 5. Climatological Data April 1985

Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

max 78 81 85 85 83 84 77 77 84 78 74 75 87 90 89 70 68 66 70 67 61 64 72 79 77 70 79 83 78 82

min 44 46 50 55 51 46 43 47 45 44 45 46 48 52 56 48 46 42 43 46 49 37 43 51 54 56 59 48 48 47

max 77 82 85 84 82 83 77 78 83 77 73 75 86 88 88 68 69 66 68 68 61 61 72 77 75 69 79 83 76 82

min 50 52 54 63 57 49 45 53 59 49 50 52 53 58 54 50 49 46 48 50 50 42 48 55 53 54 63 51 51 51

Climatological Data for Davis, California: April 1985 Soil Temperature Prec Evap 10cm 20cm 50cm max min max min mean totals 62 54 55 52 55 0 0.3 66 47 58 56 57 0 0.16 68 61 61 58 59 0 0.22 70 63 63 60 61 0 0.39 70 65 64 62 62 0 0.59 73 65 65 63 64 0 0.25 72 65 65 63 64 0 0.24 70 64 64 62 64 0 0.2 70 64 66 65 64 0 0.22 74 67 67 64 66 0 0.28 71 66 66 64 65 0 0.18 72 64 66 64 65 0 0.22 73 66 67 65 66 0 0.31 76 68 68 66 67 0 0.26 78 70 70 68 68 0 0.36 75 69 70 68 69 0 0.31 73 64 68 64 66 0.34 0.19 68 62 65 63 66 0 0.14 69 62 64 62 65 0 0.24 69 63 65 63 66 0 0.29 67 64 64 63 65 0 0.17 65 61 63 61 64 0 0.11 68 61 64 62 64 0 0.26 71 64 66 63 65 0 0.4 71 66 66 65 66 0 0.59 69 64 66 64 66 0 0.66 70 64 66 64 66 0 0.57 75 67 67 65 66 0 0.6 75 68 68 66 67 0 0.36 75 68 69 67 68 0 0.25

High Low Mean Total

90 61 77

59 37 48

88 61 76

63 42 52

78 62 71

Air Temperature 2m

15m

70 47 64

70 55 65

68 52 63

223

69 55 65

0.34 0 0.01 0.34

0.66 0.11 0.31 9.32

0.5m 104 27 51 150 250 72 72 42 36 111 78 51 68 28 96 128 77 59 103 76 80 87 64 151 245 348 230 118 106 62 348 27 106

Wind 2m 10m 2m passage direction 149 195 CALM 48 76 NW 78 108 NW 175 256 NW 353 424 N 106 134 WNW 107 144 N 65 84 NW 64 90 SW 152 200 W 123 170 W 79 100 N 98 0 N 49 0 NE 139 0 SW 185 243 W 114 144 SW 91 119 CALM 153 193 N 109 85 W 114 113 SE 130 174 NW 91 83 CALM 203 224 N 321 389 NW 452 567 NW 299 357 N 164 112 S 147 135 W 87 47 CALM 452 48 148

567 0 184

Table 6. Climatological Data May 1985

Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

max 86 88 83 77 82 84 74 76 78 77 69 70 83 88 85 86 75 74 81 87 85 93 93 88 84 73 68 67 64 72 80

min 45 51 48 52 43 52 44 42 41 47 42 43 45 47 56 40 45 42 46 48 49 52 57 49 49 50 50 53 40 45 52

max 86 86 81 77 80 82 71 74 76 77 66 75 81 86 82 84 72 71 80 85 83 91 91 86 83 73 68 66 66 74 80

min 50 54 51 57 49 50 47 47 46 51 46 46 51 52 65 56 52 47 50 52 53 56 60 53 54 53 53 52 47 52 55

Climatological Data for Davis, California: May 1985 Soil Temperature Prec Evap 10cm 20cm 50cm max min max min mean totals 78 70 70 68 69 0 0.31 78 71 71 69 72 0 0.5 78 72 72 70 70 0 0.36 78 71 72 70 71 0 0.41 77 71 72 69 71 0 0.5 78 70 72 69 71 0 0.46 76 69 70 68 69 0 0.41 76 69 70 68 70 0 0.39 77 68 70 68 70 0 0.41 76 69 70 68 70 0 0.37 72 66 70 66 70 0.02 0.22 72 66 68 66 69 0 0.29 76 69 70 68 72 0 0.45 79 70 72 70 70 0 0.38 78 73 73 71 72 0 0.68 79 72 73 71 72 0 0.6 78 72 72 70 72 0 0.41 78 71 72 70 72 0 0.35 79 71 73 71 72 0 0.33 81 72 74 71 72 0 0.44 82 74 74 73 74 0 0.39 83 75 76 74 74 0 0.43 84 76 79 74 75 0 0.4 85 78 78 75 76 0 0.47 85 78 78 76 77 0 0.48 82 74 77 74 76 0 0.42 76 72 74 72 74 0 0.22 78 72 73 72 74 0 0.41 75 68 72 69 74 0.04 0.17 76 68 71 70 72 0 0.29 79 71 72 70 72 0 0.49

High Low Mean Total

93 64 80

57 40 47

91 66 78

65 46 52

85 72 78

Air Temperature 2m

15m

78 66 71

79 68 73

76 66 70

224

77 69 72

0.04 0 0 0.06

0.68 0.17 0.4 12.44

0.5m 51 88 126 121 127 195 199 120 103 146 77 93 109 98 240 195 109 83 89 117 86 100 111 123 132 310 35 204 87 63 158 310 35 126

Wind 2m 10m 2m passage direction 74 32 CALM 116 71 S 180 165 SW 161 109 N 173 158 N 266 266 SW 257 258 S 162 106 S 146 83 CALM 207 184 S 111 49 N 124 46 N 146 109 N 127 52 NW 311 352 NW 271 302 W 171 140 N 127 48 NW 119 39 N 152 70 CALM 116 41 CALM 133 43 CALM 138 78 S 163 105 CALM 175 111 SW 259 215 W 203 171 WSW 269 252 SW 134 87 CALM 94 38 NW 208 179 S 311 74 171

352 32 128

Table 7. Climatological Data: Detail Climatological Detail Statistical Category Growing degree days (base 50 F) Heating degree days (base 65 F) Cooling degree days (base 65 F) Days 90 F or above Days 32 F or below

April 374 106 30 1 0

May 417 92 44 2 0

Observations for 24 hr period ending at 8am; year 1985.

Temperature in Fahrenheit; precipitation and evaporation in inches Wind passage in statute miles; wind direction at 8am Data Source: NOAA Reference Climatological Station maintained by the Department of Land, Air and Water Resources, UC Davis.

Acknowledgements and Additional Comments Special thanks to: Kenneth Cassman, Ph.D., (Agronomy and Soil Science) at the University of California, Davis for comments and direction on this field study.

Vincent Rubatsky, Cooperative Extension Vegetable Crops, UC Davis for information on California lettuce production.

The Department of Land, Air and Water Resources at the University of California, Davis for climatological data.

Fertilizers. It may be pointed out that inferior yields from organic manure may have resulted from insufficient time for the material to break down, preventing the release of most of its nitrogen. The environmental impact and toxicity of chemical fertilizers versus organic manure also should be taken into consideration.

In materials and methods, metric units should have been used for the plot size and NPK application was reported per plot which should have been converted to per ha basis.

225

References Cassman, K.G., et al., “An Economic Analysis of 1982 Soybean Production in Minya Governorate,” Food and Agriculture Organization, 1984.

Gardner, B.R., and Pew, W.D., “Comparison of Various Nitrogen Sources for the Fertilization of Winter-grown Head Lettuce,” Journal American Society of Horticultural Sciences 104 (4): 534536, 1979.

NOAA Reference Climatological Station maintained by the Department of Land, Air and Water Resources, University of California, Davis UCAE (University of California Agricultural Extension), “Lettuce Production in the Imperial Valley,” Circular 135, 1978.

Williams, William A., “Management of Nonleguminous Green Manures to Improve the Infiltration Rate of an Irrigated Soil,” Soil Science Society of America Proceedings, Vol. 30, No. 5, September-October 1966, pp. 631-634.

***

226

ADDENDUM Conventions attempted in the book are mentioned here, although it is recognized that these conventions may not always be consistently applied. Partitioning of Sections (multilevel listing): For example, “Part 1, section 1, subsection 1” reads as 1.1.1; “Part 2, section 2, subsection 1” would read as: 2.2.1. Appendices are listed as A1, A2, with subsections such as A1.1, A1.2, etc. The numbering may be truncated at a certain point for simplicity over clarity of organization.

Parentheses. When a term appears in parentheses after another term [such as creditors (lenders)], the meaning of the two terms is taken to be very similar but possibly used in different contexts.

The use of “i.e.” within parentheses is designed to emphasize the term. “E.g.” within parentheses is an abbreviation for “example” as in “for example.” Also, “aka” is an abbreviation for “also known as”). Occasionally parentheses will be used in front of or behind another to mean that the word is optional but can help clarify the meaning. “RE:” or “Re:” is a general reference to an additional source of information related to the topic. Re: is often a concept or terminology but can also be a work or author that may or may not appear in the References section and that should be searchable online. Slashes. Like parentheses, a slash (for example, debt/borrowings) means that both terms are nearly identical in meaning in that context and therefore “joined” together by a slash.

Italics are added because of its deemed importance and often to indicate a technical or other terminology used in the field that could be researched elsewhere for background information.

Boldface is intended usually to indicate a subtopic. Currency Units are at times stated (such as Dollars, Yuan, Euros, Yen) when necessary, but when a neutral example is provided that involves currency, the term “currency units” may also at times be used.

Quotation marks are used either for emphatic effect, for a popularized term, informal jargon or common expression used in a field, industry, or context. References are from a wide variety of sources, academic, practitioner-oriented, and journalistic. It is recognized that using non-academic sources may appear to reduce the quality of the research, but this approach is considered 227

preferable to identify variations in viewpoints--practitioners can offer valuable insights from their first-hand experiences in industry. Quality journalistic references can provide readers with more current information. References and citations do not necessarily imply agreement with, or endorsement of, the authors, whether individuals or related to a service or organization (e.g. a fund manager, group, political party, etc.). It can be difficult to cover certain topics without referring to possibly objectionable and unpopular research, ideas or individuals that might have fallen out of the mainstream; however, despite these concerns, it is hoped that drawing from diverse sources can help shed light on economic distortions and sustainable, just systems. If the same work is cited in succession, the same work may be indicated only by enclosing the page number in parentheses; it is assumed that the same work is being cited. Some citations are not direct and are sourced from another researcher. It is recognized that a citation may not be representative of, or fully convey, the entirety of a body of research and therefore can be misleading if not qualified.

Citing a work doesn’t imply approval of, or agreement with, the citation or the cited work or author(s). It is not unusual for some historical writings be confusing or inconsistent. It is understood that some educated guesswork may be necessary at times to ascertain what an author most likely intended to say, as even scholars in the field may be unable to fully agree on the meaning of certain passages.

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Many of the references may appear irrelevant to the subject matter of this book; however, when considering the interactions of economic and financial systems with politics, law, judicial systems, media, society and morality, the relevance may become

228

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Additional Notes, Resources, and Acknowledgements Some references may not have been cited in the book but may have some tangential relevance to the topics covered or the other references, particularly historical documents. Interest rate data were sourced from the Board of Governors of the Federal Reserve System (US) and Federal Reserve Bank of St. Louis (FRED) online databases. Annual financial statements of firms are sourced from the filings with the U.S. Securities and Exchange Commission (SEC) or from company annual reports containing financial statements. Other resources include the NASDAQ, Wikipedia, Wikimedia Commons, MathWave, Wikinvest, Econ Library (econlib.org; David R. Henderson), Investing dot com, Seeking Alpha and contributors focused on dividend investing and Yahoo Finance. Special thanks to the Taipei Institute of Banking and Finance, the University of California, Davis Cooperative Extension and College of Agricultural and Environmental Sciences.

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