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Cautionary Tails Framework and Applications: Dividend Coverage and Debt Management Raoul Kennedy Introduction This Cautionary Tails Framework and Applications is a draft document consisting of Part 2 and modified Appendices of the published works Dividend Coverage Analytics and Debt Management Analytics (2021), intended to provide prospective readers with a free preview of the methodology. This document is divided into cautionary stress-testing (section 2.1) and ranking system methodologies for coverage capacity (section 2.2). The stress testing methodology describes the key variables and definitions, the five major metrics, the theoretical modeling and assumptions. This includes a modification to the coverage analysis in the author’s Yield Distortion Dynamics (2020) that extends beyond centrality (metric 3) to encompass maximum acceptable exposure (MAE) 0.05 and 0.10 (metrics 4 and 5, respectively). The reports using this methodology are typically titled Cautionary Stress Testing Dividend Coverage for individual firms, and Dividend Coverage Roundup [industry/grouping] for more than one firm. The ranking system of section 2.2 is founded on the cautionary stress-testing methodology and divides firms into three tiers (1, 2 and 3) with the remainder in Tier 4. The five metrics are modeled and assigned to the tiers. Theoretical and actual results from the 59-firm sample are contrasted. The initial aggregate results for each tier are termed “unfiltered” and are detailed in three separate reports by tier. In a fourth report, these filters are applied to these “unfiltered” tiers to narrow the list of dividend contenders to the top ten Dividend Coverage Roundup: Capacity Tiers Finalists. The fifth and last report titled Dividend Coverage Roundup: Capacity Finalist Yields compares these finalists to their respective yields. Collectively these reports are part of the Dividend Coverage Roundup series with a subheading of Capacity Tier(s) or alternatively, Capacity Rankings.

2 Appendices. Appendix 1 provides detail on the sample data, results and further detail on the methodology employed as part of the cautionary tails framework. Appendix 2 covers the specifics of financial data requirements, and data sources. Appendix 3 addresses the broader context of financial analysis within an accounting framework. Appendix 4 is a recap of key principles of credit analysis originally introduced in the author’s Interest Rate Analytics (2015). Appendix 5 presents a study on Coca Cola (KO) and a report on Barrick Gold (GOLD). The KO study technically falls into both the individual firm and comparative analysis categories and compares each of the three decades alongside the entire 30-year period. The Barrick Gold report shows how financing obligations can be aligned with estimated centrality. Appendix 6 includes non-English versions of the Dividend Coverage Roundup report on petroleum refiners in French, Japanese and Spanish. References are divided into two sections, general and specialized, the former centering on financial/economics and the latter on a separate epidemiology and economy study. Significance of Research. A key feature of the methodology is that it allows us to evaluate dividend coverage capacity without the need for data on the historical dividend-paying track record of firms. Moreover, once we have a better notion of the theoretical limits of dividend and debt service coverage capacity, there is greater hope that a more sustainable business and economic environment can be achieved. This research may also reinforce some of the findings of the author’s Yield Distortion Dynamics* (2020) which pointed to a significant problem of debt service capacity. This precarious state 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. This cautionary framework also can be applied to other fields. A case in point is a year-long epidemiology and economy country study of Japan (Re: Kennedy 2021 in Section 2 of the references),

3 *Note: In Yield Distortion Dynamics, the methodologies and application of an extension of natural interest rate theory are worth noting and are relevant to the historical root causes of overindebtedness. Debt service Opacity. Correct annual debt service figures are not considered categorically reliable, and therefore in many cases estimates must be made. Ascertaining debt service amounts should 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, as further detailed in the Credit Analysis discussion of Appendix 4. In sum, the reliability of typical financing obligations coverage (i.e. dividends and debt service combined) results and their comparability across firms may be in question; therefore a cautionary approach to debt service is adopted as explained in Part 2. Time Period of Observations on Data. Uniform time periods of financial data are preferred but not all the firms have identical fiscal year ends. Most data tend to be concentrated by calendar year (1/1-12/31), but 16 of the 70 firms (23%) have non-calendar fiscal year ends (e.g. 3/31, 6/30 and end of September). Moreover, for interim period analyses, data collection may contribute to the non-uniformity of comparisons. For example, consider two firms with identical fiscal year ends of 12/31; however, the data collected for firm #1 cover the interim period to its Q3 (quarter 3), while the firm #2 data was only available to Q2. 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

4 distributions to err on the side of caution. However, in some cases, the most conservative estimate is not based on the fat tailed distribution, but the higher-ranking one. Therefore, to improve on the cautionary approach, resources permitting, an additional step may be taken to compare both (fat tailed and higher ranking) fitted distributions to determine the most conservative estimate, which is generally selected for use. Note that the highest-ranking distribution itself being fat tailed was estimated at 19% of the total distributions of the sample as discussed in Appendix 1. 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 the analysis can be useful, 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 the References (e.g. Cook, John D. (2018), Cooke (2014), and studies by Taleb, Nassim, et al. (2020). Expertise. Exploring solutions to complex problems that straddle several disciplines calls for deferring to top authorities on each of the subjects. 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 improving our understanding of underlying debt service capacity of firms, towards better assessing risk of investments and companies to avoid potentially devastating longterm consequences not only for those that depend on the income, but also for economic vitality. Debt and Policy. A common finding is excessive indebtedness contributing to weak capacity to cover financing obligations including dividends. In addition to various forms of “soft default”, and restructuring, policy actions may move to counter any

5 accumulation of debt through inflationary* means in an approach that increases monetary velocity while monetizing corporate debt. *Note: In this sense, “inflationary” refers to the creation of currency or reserves to debase the currency such that the principal amount of any debt becomes proportionally less of the overall currency in circulation. Policy countermeasures were detailed in Yield Distortion Dynamics (2020) and are not the focus here. As stated there, in replicating natural rates via policy was viewed as an impossible task; a preferable approach would be the removal of policy interventions to allow undistorted credit/loanable funds market to function. However, any policy-guided transition towards undistorted markets, management of the existing/outstanding debts would be a major undertaking involving some form of largescale and long-term 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 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 are found in an Addendum before the References. The individual reports and findings have also been shared online as a public service; if helpful, readers who contribute to charitable causes are encouraged to pay it forward by adding the imputed value of this service to a reputable charity, preferably with priority given to the most vulnerable, including defenseless animals. Organizational reputations may be researched through sites such as charitynavigator.org. The author may also hold positions in securities of companies, including through ETFs, that are covered. The discussion and any visuals may contain significant errors, are subject to revisions and are provided 'as is' solely for informational purposes, not for

6 trading or investment advice. This preliminary analysis is exploratory; 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 or other events. Part 2. Cautionary Tails Framework and Methodology Part 2 is divided into two main methodological sections, 2.1 Stress Testing and 2.2 Coverage Capacity. See Appendix 1 for the listing of firms covered, including the sample.

2.1 Stress Testing Methodology 2.1.1. Background This section covers the cautionary tails methodology for the reports in Part 1 of the author’s Dividend Coverage Analytics and Debt Management Analytics (2021). Because these reports also refer to the methodology, there may be some redundancy. Much of the content in this section was originally part of the report titled Cautionary Stress Testing, Dividends: Methodology Background Notes, and has been modified here to update and better integrate it into this book. Summary *Can dividend stocks cover their dividends and debt service? This post provides methodological details for Cautionary Stress Testing: Dividends reports and Cautionary Financial Analysis videos. *Heavy indebtedness may impair the capacity of firms to cover their dividend payments. *Firm-level stress testing can offer insights into exposure to various scenarios and default. Introduction Dividend growers and high dividend stocks are popular additions to investment and retirement portfolios. Well-known listings of high-performing dividend payers include the Dividend Aristocrats (S&P), Dividend Achievers (Nasdaq), and the Dividend Champions list founded by the late David Fish and

7 currently maintained by Justin Kan (re: Drip Investing Resource Center). Coverage. While the above are valuable sources of information on stocks with a historical track record of dividend payments, growth or high dividend yields, analysis of the underlying capacity of firms to cover and grow their dividends is essential in making more informed investing decisions for these types of stocks. 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. The cautionary framework aims for a more expansive view of the dynamics underlying dividend coverage along with a more rigorous treatment of cash flow. Reports Employing the Methodology. Cautionary Stress Testing, Dividends refers to individual firm dividend coverage capacity analyses. Dividend Coverage Roundup compares two or more firms for which cautionary stress testing was performed on each of the firms in the grouping. These reports or video versions of the reports may also be referred to as “comps.” Applicability of Analysis. Firm-level stress testing may also serve to assess vulnerabilities within financial institutions (e.g., business loans) and capital markets in addition to investment portfolios (securities). Moreover, individual firm or aggregate industry exposure levels may give insights as to the prospects for economic recession and policy countermeasures. Adverse shocks may have ripple effects in capital markets, financial institution credit portfolios and economic output, as well. 2.1.2. Variables and Definitions Financing Obligations (abbreviated FO) are defined as the sum of dividends and debt service payments. Dividends are combined with debt service because the source of cash to cover debt service and dividend is the same (see Net Free Cash Flow NFCF). Debt service is defined as principal repayments plus interest for the current 12-month period. This definition extends beyond just current maturities (aka current portion of long-term debt

8 abbreviated CPLTD) to include all repayments of principal. A cautionary approach to this definition aims to capture the debt (typically short-term) that is being rolled over rather than being termed out as part of an “evergreening” process. Future Debt Maturities. Debt service (i.e., interest + principal) is normally computed only for the current 12-month period. This ignores the amount of debt that must be serviced in coming years. It is not unusual for debt maturities in a future fiscal year to greatly exceed the amount of current maturities. If data a readily available on future debt maturities, an extended assessment of risk accounts for the future amounts of debt coming due. Interest Expense sourced from the income statement is generally used if cash flow interest data not readily available. The preliminary results may be subsequently modified to incorporate updated information. 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. Cash Flow is defined as Net Free Cash Flow abbreviated NFCF. NFCF is traditional free cash flow (FCF) less various types of acquisitions. This version of cash flow is s more conservative cash flow variable. NFCF has alternatively been referred to as equity income, abbreviated EI by the author. NFCF is treated as the primary source of payment SOR for coverage of financing obligations. Secondary sources of payment may include additional borrowings or equity issuance, for example. As noted above in the Financing Obligations (FO), to estimate coverage, the NFCF/Revenues variable is compared to FO (i.e., dividends and debt service combined, also referred to as DDS for “dividends+debt service”). Note: In earlier versions of the Cautionary Stress Testing, financing obligations referred to the latter of two coverage categories: a) Coverage of dividends alone was called Coverage 1 and b) Financing Obligations which are the sum of debt service and dividends and called Coverage 2. The methodology has been updated to eliminate the former dividends-only category because both dividends and debt compete for the same source of cash.

9 Revenues. The “top line” figure, also referred to as Total Revenues, Gross Sales, Turnover; this figure is typically accrual based. 2.1.3 Financial Information Data may be sourced in part from interim unaudited financial information; results are preliminary as the data and default assumptions are subject to modification to reflect updated information. Trailing twelve-month data may also be employed which may result in a partial overlap with the prior fiscal year data. Audited company annual financial statements filed with the SEC are preferred but may not be available for some months after the fiscal year end. For further detail, see Appendix 2 on financial data. 2.1.4. Metrics There are five basic metrics as follows: 1. Coverage 2. Negative Cash Flow Exposure 3. Centrality 4. Maximum Acceptable Exposure (MAE) 0.05 and 5. MAE 0.10. These metrics are estimates based on the models. Metrics are estimates. For metrics 1 and 2, the range is 0-1 (or 0% to 100%); for metrics 3, 4 and 5, the estimates refer to a cash flow measure (net free cash flow) as a percentage of revenues (e.g., 0.07 =7% of revenues). Cautionary approach note: The addition of metrics 4 and 5 is a major modification to the approach that was introduced in Yield Distortion Dynamics (2020) of limiting the coverage analysis to centrality (metric 3). Results are generally considered preliminary because the default assumptions are subject to modification to incorporate updated information. Metric 1, coverage is an estimate of the capacity to cover financing obligations i.e., the sum of actual debt service and dividends for the current 12-month period; debt service is defined as all repayments of principal plus interest. Weak coverage estimates may suggest the presence of permanent working capital as part of an evergreening process, in which accumulating debt is rolled over via short-term refinancing arrangements rather than being termed out.

10 Metric 2 is negative cash flow exposure for which the estimate is the proportion of total possible outcomes ranging from zero to one; for example, a value of zero suggests negative cash flow is a theoretical impossibility (i.e., no possible outcomes); a value of one corresponds in theory to all possible outcomes (i.e., approaching reality; and an unfavorable indicator). Recall that cash flow is defined as NFCF (see section Variables and Definitions above). Metric 3 is centrality, an estimate of the point at which 50% of outcomes are in theory contained to right, and the other 50% to the left. Note that the approach as detailed in Yield Distortion Dynamics (2020) limited the analysis of coverage to centrality; the cautionary methodology adds metrics 4 and 5 (MAE 0.05, MAE 0.10). Metrics 4 and 5 are estimates of a theoretically acceptable level of financing obligations (as a percentage of revenues) based on maximum acceptable exposure MAE criteria of 0.05 and 0.10, respectively. For example, if the criterion of MAE 0.05 is specified (stricter than MAE 0.10), one should not accept an amount of financing obligations that exceed the cash flow/revenues the lie above MAE 0.05. Negative Values are explained in part by the distribution of the cash flows in which a non-trivial portion fall further into the left tail; notably, negative MAE values suggest that the firm may in theory be unable to cover any amount of financing obligations given the specified MAE criteria. For example, in the following curve, negative cash flows comprise an estimated 0.25 (or 25%) of the possible outcomes; this estimate of 0.25 greatly exceeds the MAE 0.10 or MAE 0.05 criteria, meaning that creditors would need to adjust their risk assessment to account for this difference.

2.1.4.1. Tiers and Rankings (Part 2.2 Prelude) The metrics are used to develop a ranking system in three tiers. The methodology for the estimation of the rankings is detailed in Part 2. Briefly stated here, the tiers are 1, 2, and 3, with rankings from 1.0 to 3.99 (rounded). An estimate of 1.0 suggests the strongest possible performance; estimates of 4.0 and greater fall below the criteria for the top three tiers and are labelled as “unranked” or “remainder.” 2.1.5. Modeling and Assumptions The cautionary stress test simulations are run on theoretical models constructed by distribution fitting. The behavior of the variables is expressed by where they land within a theoretical “space” of possible outcomes; the observations are organized in terms of the relative frequency of their occurrence over the period examined (five or up to ten twelve-month periods is most common); these results are then fitted to theoretical density curves. Spatial Centrality is a certain type of estimate of a central tendency of the historical data, often abbreviated centrality, detailed next. Centrality is estimated for each x-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. It is recognized that other indicators of central tendency such as arithmetic averages could be used in analysis. However, this is not

12 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 as to be discussed. Assumptions A conservative approach adopted in the analysis is founded on a principle of erring on the side of caution. Two major assumptions are made: 1) A default assumption of fat tails for the theoretical models is generally made over higher-ranking distributions, which may be thinner tailed. Substantial evidence for the presence of fat tails in financial data has been identified in prior empirical work by the author (re: Financial Distortion Dynamics 2018). 2) In accordance with a principle of conservatism there still may be circumstances in which higher-ranked fits with thinner tails may be preferred if the estimates tend to reflect greater exposure, in accordance with a conservative approach. 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. Well-fitted models can be misleading and therefore should be treated with caution as they can be completely “invalidated” by subsequent values, 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-ofsample extreme values occur with far greater relative frequency than “should” occur according to the best-fitting model! A fattailed distribution may not always be apparent from empirical testing, the possibility that one will be suddenly revealed cannot be discarded. In fact, for reasons explained below, in addition to

13 this ghostlike quality, assuming the presence of a (phantom) fat tail may be the appropriate default assumption. Cautionary “Tails.” 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, left frame and fat-tailed Cauchy, right frame). These two curves are different theorized versions of the same financial process or “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 non-trivial 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 they are not obvious and therefore have a “phantom” quality to them) based on the author’s experience as detailed above, the cautionary approach initially assumes a fat-tailed distribution as the default. That said, the methodology allows for

14 higher-ranked fits with thinner tails if the estimates tend to reflect greater exposure. For authoritative sources and expert exposition of underlying concepts, see the References [For example, re: Cook, John D. (2018: “Six Sigma Events,” May 31; “Obesity Index” April 23); Cooke, Roger, Fat-Tailed Distributions 2014; Taleb, Nassim, Statistical Consequences of Fat Tails (2020) and other studies (also re: Pascual Cirillo, Rafael Douady)]. 2.1.6. Qualifications and Shortcomings Estimates are not to be used in isolation and are not forecasts; multiple resources should be consulted* for decision-making; estimates are at best rough indicators of what may be possible based on models employed. Due to the limited number of observations for each firm, small sample sizes may be used in fitting the distributions, a shortcoming of the analysis. Data may primarily be sourced from interim unaudited financial information; results are preliminary as the data and default assumptions are subject to modification to reflect updated information. Poor apparent results for a firm may not indicate imminent default for reasons explained in the section below on Additional Commentary. *Note: For example, research by reliable equity and industry analysts should also be considered for further depth and perspective. The estimates are referred to as “in theory” because they are based on theoretical models constructed by distribution-fitting. Consequently, the estimates 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). 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).

2.1.7. Evaluating Findings While estimates of inadequate debt service coverage may be alarming, there may be reasons to remain optimistic. Conversely, a high ranking in terms of debt service coverage capacity does not imply a guarantee that financing obligations can be covered and that default will be avoided. Both scenarios are examined in further detail next. 2.1.7.1. Mitigating Factors. Poor results are not necessarily indicative of an imminent default, especially for large firms with access to global capital markets and credit. The analysis itself is preliminary, with default assumptions that are subject to modification and inherent shortcomings in the data. Negative cash flow does not preclude the possibility of borrowing under credit lines and offering additional collateral for borrowings, although it is recognized that such actions may not be possible indefinitely. Many overindebted firms may still have strong and stable underlying recurring cash flow. Credit lines do not typically amortize and accordingly may not be included in the computation of debt service. Moreover, the credit lines may have a "clean-up" requirement that the company cannot, or chooses not to comply with, turning a short-term credit accommodation into potentially a longer-term one. Preemptive Debt Restructuring. While avoiding excessive debt should be a priority, if a potential over-borrowing situation can be identified early enough, management may undertake a preemptive debt restructuring which can include equity issuance* to pay down debt, spin-offs, asset sales and renegotiation of loan terms. *Note: While equity issuance may dilute the stock and can potentially increase cash outflows for future dividends, the net cash outflow may still be reduced by such actions. Policy and Inflation. In recessions and amidst cascading loan defaults, political pressures can easily lead to an easing of the debt servicing burden through corporate debt (or equity) monetization 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

16 revenues coupled with cost-cutting can in theory result in improved debt service capacity over the longer term. The mechanisms are complex, but the inflation rate depends on various factors including deficit financing, debt monetization, monetary policy accommodation, and fundamental changes in the velocity of money itself, to cite just a few examples. Policy changes also can play a role in easing the financing burden of distressed firms: for example, the Federal Reserve (U.S.) policy to acquire debt through its Secondary Market Corporate Credit Facility, which may be extended to a Primary Market facility as well. 2.1.7.2. Unforeseen Risks In contrast, an apparently satisfactory finding does not necessarily guarantee that the firm is safe from imminent default or other financial distress; the estimates and rankings should be treated as approximations of possible outcomes based on the theoretical models. The analysis may not account for other possibilities such as fraudulent activity and misrepresentation of financial information, sudden regulatory policy changes adversely impacting the company business model, unanticipated dividend hikes and accumulation of borrowings. 2.2. Ranking System Methodology: Coverage Capacity Introduction Part 2.2 walks through the steps that were taken to derive the estimates of the coverage capacity tiers and rankings. The rankings are founded on the five metrics. The list of the 59 firms and their metrics employed in the sample is found in Appendix 1, both alphabetically as well as by SIC Code. 2.2.1 Modeling the Metrics Distributions were fitted based on the individual observations for each of the 5 metrics of the 59 firms studied. Generally, the highest-ranking fit was chosen to represent each process. For example, the fitted distribution for the Coverage metric (metric 1) corresponded most closely to the Pareto, while that of the Negative Cash Flow metric (metric 2) was the Fréchet.

2.2.2. Fitted Distributions by Metric

Metric 1. Coverage Probability Density Function 18 16 14

f(x)

12 10 8 6 4 2 0 0

0.1

0.2

0.3

0.4

0.5

x Pareto 2 (0.77563; 0.04332)

Metric 2. Negative Cash Flow Probability Density Function 8.8 8 7.2 6.4

f(x)

5.6 4.8 4 3.2 2.4 1.6 0.8 0 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

x Frechet (1.2512; 0.06923; -0.01623)

Metric 3. Centrality Probability Density Function 5.2 4.8 4.4 4 3.6

f(x)

3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0 0.8

0.6

0.4

0.2

-0.2

x Burr (0.68699; 8502.3; 371.7; -371.62)

Metric 4. MAE 0.05 Probability Density Function 4.8 4.4 4 3.6

f(x)

3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0 -0.5

0.5

1.5

x Wakeby (3.6424; 7.5136; 0.0863; -0.03442; -0.56988)

Metric 5. MAE 0.10

f(x)

Probability Density Function 5.6 5.2 4.8 4.4 4 3.6 3.2 2.8 2.4 2 1.6 1.2 0.8 0.4 0 0

0.5

1.5

x Wakeby (3.0406; 9.0923; 0.08569; -0.04318; -0.37184)

2.2.3. Tiers by Individual Metric 2.2.3.1. Tier Cutoff Values by Individual Metric Assumptions Decisions were made on the tier cutoff values for each of the five metrics based on patterns in the sample observations. It is recognized that such decisions have an arbitrary element to them. Alternative methodologies may choose to adjust the tier cutoffs to either include or exclude more estimates according to specific priorities. This Cautionary methodology applies a cutoff “rules of thumb” by making an assumption as to an “acceptable” proportion of estimated outcomes by tier based on patterns observed in the data. These assumed proportions are as follows: For the Coverage and Maximum acceptable exposure (MAE) metrics (Metrics 1, 4 and 5), it was assumed that estimates in the top 20% of the estimates should be included in the top three tiers; for the Negative Cash Flow and Centrality Metrics (metrics 2 and 3), the assumption was that top half of the estimates would be included in the top three tiers. Any estimates that fall below the Tier 3 cutoffs are labelled as Tier 4 or “remainder.” Chart 2.2.3.1. The following chart shows the cutoff values that were established to achieve these proportion of outcomes by metric 1 through 5. In the chart below, for example, the Coverage metric (metric 1 at left) shows that Tier 1 comprises all coverage estimates that are greater than or equal to 0.85 (scale 0 to 1). Estimates that fall below 0.5 would be categorized as “remainder” or Tier 4 (not shown in the chart).

2.2.4. Theoretical Proportions within Tiers by Metric Based on the cutoffs established as detailed above, the fitted distributions are used to compute the proportions that in theory should result from those cutoffs. As seen in the next chart, these proportions can deviate somewhat from the assumed proportions, particularly in the case of MAE 0.10 (metric 5) which is 0.35 compared to the assumption of 0.20.

2.2.5. Actual Proportions: Estimates within Tiers by Metric In contrast to the theoretical proportions based on the fitted distributions as seen in the previous section, here we see the proportions of the actual estimates for each tier and metric, shown in relative frequency distributions. For example, in the Coverage chart below, the proportion of actual estimates that fall into the Tier 1 category is 13% of the total, Tier 2 is 7%, and Tier 3 is 5% for a total of 25% for the top 3 tiers. Note that this proportion of 25% deviates from the assumed proportion of 0.20 (or 20%). Lastly, the “remainder” Tier 4 category comprises 0.75 (or 75%) of the estimates, compared to the assumed proportion of 0.80 (80%).

Metric 1. Coverage

Metric 2. Negative Cash Flow Exposure

Metric 3. Centrality

Metric 4. MAE 0.05

Metric 5. MAE 0.10

2.2.6. Theoretical vs. Actual Proportions: Comparisons by Tier The following metrics charts compare the differences between the theoretical proportions computed and the actual proportions by tier. Note that for the Coverage metric (metric 1), the assumed proportion was 0.20 (or 20%) for the top three tiers and 0.80 (or 80%) for the remainder (Tier 4). As can be seen in the Coverage chart, for Tier 4 there is a wide discrepancy between the theoretical (0.86) and the actual (0.75), with the assumed proportion (0.80) falling in the middle of the two.

Metric 1. Coverage

Metric 2. Negative Cash Flow (see below)

Metric 3. Centrality

Metric 4. MAE 0.05

Metric 5. MAE 0.10

2.2.7. Aggregate Tiers: Unfiltered Tiers and Rankings The process constructing aggregate tiers for the companies begins by simplistically summing the individual tiers for each of the five metrics (coverage, negative cash flow, centrality, MAE 0.05, MAE 0.10), then dividing by the total number of metrics. For example, if the values of the tiers for a given firm are 2, 2, 2, 4, 2, the sum of these values which is 12 is divided by 5 for an unfiltered general ranking of 2.4 (2.4 being within Tier 2).

27 Sample. Based on this ranking process, 42 of the 60* companies that constituted the sample (70%) fell within the three tiers: Tier 3 (28 firms or 47% of total), Tier 2 (9 firms or 15% of total) and Tier 1 (5 firms or 8%). (*) Note that there are 59 firms in the sample, but one firm is represented twice, resulting in a sample size of 60 firms; for further details on this sample criteria, see Appendix 1. Table 2.2.7.1. The next table shows the aggregate unfiltered results by tier, with the individual rankings for each company.

UNFILTERED RESULTS TIER 2

TIER 3 AMZN 5961 LMT 3760

3 3

GOOGL 7370 ABBV 2834

TIER 1

2 2

MSFT 7372 AAPL 3571

1 1.2

TSM 3674 MDT 3845

3.2 3.2

ADP 7374 PG 2840

2.2 2.4

FB 7370 PM 2111

1.4 1.6

INTC 3674 T 4813 CLX 2842 PNR 3550 NOC 3812 PFE 2834 AON 6411 F 3711 MO 2111 INGR 2040 QCOM 3663 VIV 4813 KO 2080 CVX 2911 CL 2844 MDLZ 2000 PPG 2851 VFC 2320 KHC 2030 UN 2840 TMO 3829 EMR 3600 CHL 4813

3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.6 3.6 3.6 3.6 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8

DOV 3530 MCD 5812 DEO 2080 ITW 3560 JNJ 2834

2.4 2.6 2.6 2.8 2.8

INFY 7371

1.6

SAP 7372

3.8

2.2.8. Theoretical Distribution: Unfiltered Aggregate Tiers. In the next chart, the fitted distribution shown is the Johnson SB (Rank 2, 1, 22) for this unfiltered list of companies by aggregate tier (tiers shown on the x axis).

Probability Density Function 4 3.6 3.2 2.8

f(x)

2.4 2 1.6 1.2 0.8 0.4 0 0.8

1.2

1.6

2.4

2.8

3.2

3.6

x Johnson SB (-1.0632; 0.4947; 3.3644; 0.67837)

2.2.9 Theoretical vs. Actual Proportions by Tier The following table shows the theoretical and actual proportions that fall into each tier. The theoretical distribution results are based on the Johnson S-B from the previous section. The “actual” refers to the proportions in each tier based on the aggregate unfiltered results. Proportions Tier Theoretical Actual Tier 1 8.0% 8.3% Tier 2 15.0% 15.0% Tier 3 58.5% 46.7% % of Total 81.5% 70.0% Notes: Tiers are from .00 to .99; Tier 3 to 3.95 is 51% of total; theoretical is Johnson SB.

The “remainder” (Tier 4) is not shown, but from the table can be assumed to constitute very roughly between 20 to 30 percent of the results (i.e., Theoretical 1-0.815, Actual 1-0.70). It is recognized that there is a considerable discrepancy between the two Tier 3 proportions (58.5% vs, 46.7%). However as stated in the notes to this table that refers to the theoretical proportions column: 51% of the total results lie between 3.0 to 3.95. But because 58.5% of the total results are in Tier 3, this suggests that a relatively large proportion of the Tier 3 results (7.5%) lie in

30 between a relatively small sliver of values between 3.95 and 3.99 (or 0.04). This may account for some of the wide discrepancy. 2.2.9.1. Comparison with Alternative Theoretical Distribution. The next table compares the proportions estimated from two theoretical distributions, the Johnson S-B (abbreviated JSB in the column) discussed just above, and an alternative distribution, the General Pareto (Gen Par). Johnson SB ranks slightly higher in terms of goodness of fit than the General Pareto, but both are found to generate similar estimates. The column labelled “P” at left indicates the proportion of total possible outcomes based on the theoretical models (or P as probability). The estimates in the JSB and Gen Par columns are the tiers that correspond to those proportions. Centrality (0.50). The bottom row labelled 0.5 indicates that in the Johnson S-B case, about half of the values are both above and below the ranking 3.69, while the corresponding General Pareto estimate is 3.74; the remaining proportion beyond 0.50 (up to 3.99) is not shown in the table. To explain the results in the table for the JSB column, the top 25% of theoretical outcomes fall just below Tier 3 at 2.99; the top 10% fall below Tier 2 at 2.0 and both the top 5% and 1% fall within Tier 1: below 1.47 and 0.92, respectively. Note that although 0.92 is below 1.0, this is an estimate that is assumed to lie in Tier 1. Table. The proportions for both distributions are summarized here: Unfiltered Tiers: Theoretical JSB Gen Par P x(P) x(P) 0.01 0.92 1.09 0.05 1.47 1.5 0.1 2 1.94 0.25 2.99 2.93 0.5 3.69 3.74

2.2.10. Aggregate Results: Reports by Tier Tier 3 Aggregate, Unfiltered Dividend Coverage Roundup: Capacity Tier 3, Interim 2020 Summary *Can companies with strong dividend-paying track records continue growing their dividends and cover their debt service? *This series on Dividend Coverage Capacity groups companies into three tiers of coverage capacity by modeling underlying factors outside of dividend-paying history. *This report presents the results for the third tier of the companies to date in 2020. Background Common methods of evaluating dividend coverage look at the historical record of dividend-paying performance. Unfortunately, although standard financial analysis may reveal warning signs, dividend cuts, suspensions or growth slowdowns can come unexpectedly. Dividend coverage capacity is based on underlying factors summarized in five key metrics that center on financing obligations and cash flow generation; ; this recent video report from Cautionary Global provides an overview Cautionary Global Financial Analysis: Comps for Dividend Champions, Interim 2020. Estimates are derived from theoretical models of the companies derived from a sample consisting of 59 companies analyzed in the Dividend Coverage Roundup and/or Cautionary Stress Testing reports to date in 2020. Preliminary Results The Tier 3 companies from the sample are listed below together with their SIC code and followed by the global estimate that combines the estimates for each of the company’s five metrics. While a tier ranges from 0 to 0.99 (e.g., 3.0 to 3.99) it should be emphasized that due to non-linearities the qualitative differences between companies can be significant within the same tier.

32 AMZN 5961 LMT 3760

3 3

TSM 3674 MDT 3845

3.2 3.2

3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.6 3.6 3.6 3.6 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8

SAP 7372

3.8

33 based on existing debt service estimates), but this can be disproportionately offset by higher scores for one or more of the other metrics related to cash flow profiles such as negative cash flow exposure (metric 2) or centrality (metric 3) and the others. No adjustment is made here, but this potential for the role of financing obligations to be underweighted for these companies is recognized. For example, concerns with financing obligations have been noted elsewhere for F, and KHC (food industry) and MO may also warrant further attention. As noted above because the estimates are subject to non-linearities within a given tier the differences in terms of underlying capacity is potentially significant. Recall that estimates may be derived from interim, unaudited historical data for non-uniform time periods, and are subject to other shortcomings detailed elsewhere in Part 2 and the Appendices. The results are not predictions or forecasts. Moreover, a low estimate does not necessarily imply the company’s imminent default as the role of external sources of financing cannot be underestimated; nor does a highly-ranked company face a non-trivial risk of serious setbacks. Comparisons with Dividend Performers. Various sources track companies with a consistent history of dividend growth, including S&P 500 Dividend Aristocrats, the Nasdaq Dividend Achievers, and the Dividend Champions list (see References). The Dividend Champions (abbreviated here DC) list divides the companies into three categories according to duration of uninterrupted dividend growth (1=Champions 2=Contenders 3=Challengers). A brief comparison is made to see how closely dividend-paying histories match this lists' estimates.* Of the 28 Tier 3 companies listed above, a total of four companies (14%) matched with the DC “Challengers” (category 3) list: AON, INGR, INTC, and MDLZ. Fifty-four percent (54%) of the firms on this list were ranked higher on the Dividend Champions list. Almost one-third (32%) of the companies on the list above were not found in any category of the DC list: AMZN, CHL, F, KHC, SAP, TMO, TSM, UN and VIV. *Where feasible, a similar-period DC list is used for comparisons.

Tier 2 Aggregate, Unfiltered Dividend Coverage Roundup: Capacity Tier 2, Interim 2020 Summary *Can companies with strong dividend-paying track records continue growing their dividends and cover their debt service? *This series on Dividend Coverage Capacity groups companies into three tiers of coverage capacity by modeling underlying factors outside of dividend-paying history. *This report presents the results for the second tier of the companies to date in 2020. Background Common methods of evaluating dividend coverage look at the historical record of dividend-paying performance. Unfortunately, although standard financial analysis may reveal warning signs, dividend cuts, suspensions or growth slowdowns can come unexpectedly. Dividend coverage capacity is based on underlying factors summarized in five key metrics that center on financing obligations (which includes debt service) and cash flow generation rather than dividend-paying history; this means that companies that pay little or no dividends may figure on the list. This recent video report from Cautionary Global provides an overview of the metrics for select dividend payers: Cautionary Global Financial Analysis: Comps for Dividend Champions, Interim 2020. Estimates are derived from theoretical models of the companies derived from a sample consisting of 59 companies analyzed in the Dividend Coverage Roundup and/or Cautionary Stress Testing reports to date in 2020. The Tier 1 results are presented in the next article. The following screen cap is of a video on Capacity Tiers that also presents the overall results (Tier 1, 2 and 3) at https://youtu.be/6RyEk-ZZhS8.

Preliminary Results The Tier 2 companies from the sample are listed below together with their SIC code and followed by the global estimate that combines the estimates for each of the company’s five metrics. While a tier ranges from 0 to 0.99 (e.g., 2.0 to 2.99) it should be emphasized that due to non-linearities the qualitative differences between companies can be significant within the same tier. GOOGL 7370 ABBV 2834

2 2

ADP 7374 PG 2840

2.2 2.4

DOV 3530 MCD 5812 DEO 2080 ITW 3560 JNJ 2834

2.4 2.6 2.6 2.8 2.8

36 cases, the company may score at the bottom on metric 1 (coverage based on existing debt service estimates), but this can be disproportionately offset by higher scores for one or more of the other metrics related to cash flow profiles such as negative cash flow exposure (metric 2) or centrality (metric 3) and the others. No adjustment is made here, but this potential for the role of financing obligations to be underweighted for these companies is recognized. For example, concerns with financing obligations have been noted elsewhere for ABBV (re: pharmaceuticals) and DEO (beverages). As noted above because the estimates are subject to non-linearities within a given tier the differences in terms of underlying capacity is potentially significant. Recall that estimates may be derived from interim, unaudited historical data for non-uniform time periods, and are subject to other shortcomings for which references are found elsewhere in this Part 2 and the Appendices. The results are not predictions or forecasts. Moreover, a low estimate does not necessarily imply the company’s imminent default as the role of external sources of financing cannot be underestimated; nor does a highly-ranked company face a non-trivial risk of serious setbacks. Comparisons with Dividend Performers. Various sources track companies with a consistent history of dividend growth, including S&P 500 Dividend Aristocrats, the Nasdaq Dividend Achievers, and the Dividend Champions list (see References). The Dividend Champions (abbreviated here DC) list divides the companies into three categories according to duration of uninterrupted dividend growth (1=Champions 2=Contenders 3=Challengers). A brief comparison is made to see how closely categories based on dividend-paying histories match this lists' tiers. * Of the nine Tier 2 companies listed above, none matched with the DC “Contenders” (category 2) list. Six firms (67% of the total) on this list were ranked higher on the Dividend Champions list: ADP, DOV, ITW, JNJ, MCD, and PG. Two of the companies on this list were not found in any category of the DC list: DEO and GOOGL. *Where feasible, a similar-period DC list is used for comparisons.

Tier 1 Aggregate, Unfiltered Dividend Coverage Roundup: Capacity Tier 1, Interim 2020 Summary *Can companies with strong dividend-paying track records continue growing their dividends and cover their debt service? *This series on Dividend Coverage Capacity groups companies into three tiers of coverage capacity by modeling underlying factors outside of dividend-paying history. *This report presents the results for the top tier of the companies to date in 2020. Background Common methods of evaluating dividend coverage look at the historical record of dividend-paying performance. Unfortunately, although standard financial analysis may reveal warning signs, dividend cuts, suspensions or growth slowdowns can come unexpectedly. Dividend coverage capacity is based on underlying factors summarized in five key metrics that center on financing obligations and cash flow generation rather than dividend-paying history; this means that companies that pay little or no dividends may figure on the list. This video report from Cautionary Global provides an overview of the metrics for dividend payers: Cautionary Global Financial Analysis: Comps for Dividend Champions, Interim 2020. Estimates are derived from theoretical models of the companies derived from a sample consisting of 59 companies analyzed in the Dividend Coverage Roundup and/or Cautionary Stress Testing reports to date in 2020. The following screen cap is of a video on Capacity Tiers that also presents the overall results (Tier 1, 2 and 3) at https://youtu.be/6RyEk-ZZhS8.

Preliminary Results The Tier 1 companies from the sample are listed below together with their SIC code and followed by the global estimate that combines the estimates for each of the company’s five metrics. While a tier ranges from 0 to 0.99 (e.g., 1.0 to 1.99) it should be emphasized that due to non-linearities the qualitative differences between companies can be significant within the same tier. MSFT 7372 AAPL 3571

1 1.2

FB 7370 PM 2111

1.4 1.6

INFY 7371

1.6

Qualifications. Note that some results may appear to be inappropriate for classification in this tier. This can occur, for example, when firms with relatively strong cash flow have excessive financing obligations (debt and/or dividends); in such cases, the company may score at the bottom on metric 1 (coverage based on existing debt service estimates), but this can be disproportionately offset by higher scores for one or more of the other metrics related to cash flow profiles such as negative cash

39 flow exposure (metric 2) or centrality (metric 3) and the others. No adjustment is made here, but this potential for the role of financing obligations to be underweighted for these companies is recognized. For example, concerns with financing obligations have been noted elsewhere for PM. As noted above because the estimates are subject to non-linearities within a given tier the differences in terms of underlying capacity is potentially significant. Recall that estimates may be derived from interim, unaudited historical data for non-uniform time periods, and are subject to other shortcomings noted elsewhere in this Part 2 and the Appendices. The results are not predictions or forecasts. Moreover, a low estimate does not necessarily imply the company’s imminent default as the role of external sources of financing cannot be underestimated; nor does a highly-ranked company face a non-trivial risk of serious setbacks. Comparisons with Dividend Performers. Various sources track companies with a consistent history of dividend growth, including S&P 500 Dividend Aristocrats, the Nasdaq Dividend Achievers, and the Dividend Champions list (see References). The Dividend Champions (abbreviated here DC) list divides the companies into three categories according to duration of uninterrupted dividend growth (1=Champions 2=Contenders 3=Challengers). A brief comparison is made to see how closely categories based on dividend-paying histories match this lists' tiers. * Of the five Tier 1 companies listed above, none matched with the DC “Contenders” (category 1) list. Three firms on this list were ranked lower on the Dividend Champions list: AAPL, MSFT and PM. Two of the companies on this list were not found in any category of the DC list: FB and INFY. *Where feasible, a similar-period DC list is used for comparisons. 2.2.11. Applying Filters In this section we apply filters to the aggregate unfiltered tiers from above. The notion of applying filters is intended to allow for adjustments due to possible biases or inconsistencies that can become apparent in the aggregate tiers results. The filters are

40 applied on a metric-by-metric basis (Metrics 1 through 5: Coverage, Negative Cash Flow, Centrality, MAE 0.05, and MAE 0.10). A case in point is Ford Motors, which obtained an unfiltered aggregate ranking of 3.4, but when applying a filter to Coverage (metric 1) due to the company’s debt service issues, the company is ranked as “remainder” or 4.0 as the estimate falls below the cutoff. Note that rankings may indicate both the “unfiltered” and “filtered” results for comparison purposes. For example, two values shown as 2.6–4.0 for MCD indicate both the filtered and unfiltered rankings for the firm, respectively. The filtered value, is generally considered to be the final ranking and often indicates a MAE 0.05 below the Tier 3 cutoff as noted next. Guidelines for Filtering The following shaded area summarizes simple heuristics for the application of filters. Recall that the filtering process involves established cutoffs; refer back to the cutoff values chart in the previous section 2.2.3.1. However, subjective judgement is also allowed, and based on subsequent information, adjustments may be warranted. Coverage: 4.0 is generally subject to filtering out Negative Cash Flow: 4.0 is generally subject to filtering out Centrality: Within a 2 percentage points below centrality cutoff, case-by-case evaluation. MAE 0.05 Difficult hurdle for many firms. Cutoff for Tier 3 is 0.05 (i.e. 5% of revenues), but estimates to 0.04 may be acceptable on a case-by-case basis. MAE 0.10: Any value of 4.0 is generally subject to filtering out, case-by-case evaluation. For example, assume that an aggregate unfiltered result for a firm is 2.7. If there is an individual estimate for the Negative Cash Flow metric (metric 2); of 4.0, then firm’s filtered rank drops to 4.0. Summary of Filtered Results. The filtering process that leads to the ten finalists yields in the following number of firms and proportions out of the sample of 60 firms; the unfiltered results are also noted:

41 Tier 1, Filtered: 4 firms (7%, rounded), down from 5 unfiltered (8% of sample) Tier 2, Filtered: 6 firms (10%), down from 9 unfiltered (15% of sample) Tier 3, Filtered: 3 firms remain (5%) after applying the Coverage filter; 0 remain after applying the negative cash flow filter. This tier experienced the greatest loss from filtering, in which the unfiltered number of firms was 28, or 47% of the sample. 2.2.12. Filtering Towards the Finalists This section walks through the process of a metric-by-metric filtering process to obtain a preliminary list of “Finalists,” followed by a final “top ten” list. Dividend Coverage Roundup: Capacity Tiers Finalists, Interim 2020 Summary *Can companies with strong dividend-paying track records continue growing their dividends and cover their debt service? *This series on Dividend Coverage Capacity groups companies into three tiers of coverage capacity by modeling underlying factors outside of dividend-paying history. * This report applies filters to all tiers of the companies to date in 2020 to identify sensitivity levels and the finalists. Background Common methods of evaluating dividend coverage look at the historical record of dividend-paying performance. Unfortunately, although standard financial analysis may reveal warning signs, dividend cuts, suspensions or growth slowdowns can come unexpectedly. Dividend coverage capacity is based on underlying factors summarized in five key metrics that center on financing obligations (which includes debt service) and cash flow generation rather than dividend-paying history; this means that companies that pay little or no dividends may figure on the list. This video report from Cautionary Global provides an overview

42 of the metrics for dividend payers: Cautionary Global Financial Analysis: Comps for Dividend Champions, Interim 2020. Estimates are derived from theoretical models of the companies derived from a sample consisting of 59 companies analyzed in the Dividend Coverage Roundup and/or Cautionary Stress Testing reports to date in 2020. The following screen cap is of a video on Capacity Tiers that also presents the overall results (Tier 1, 2 and 3) at https://youtu.be/6RyEk-ZZhS8. Filtered Results In the previous Coverage Capacity reports, the three tiers consisted of 28 firms in Tier 3, nine in Tier 2 and five in Tier 1. However, a potential issue was highlighted to be addressed here. Some preliminary results appeared to be inappropriate for classification in certain tiers. This may be explained by firms with relatively strong cash flow with excessive financing obligations (debt and/or dividends). A poor ranking for metric 1 (coverage based on existing debt service estimates) can be disproportionately offset by higher scores for one or more of the other metrics related to cash flow profiles such as negative cash flow exposure (metric 2) or centrality (metric 3) and the others. No adjustment was made in the prior reports but the potential for the role of financing obligations to be underweighted for these companies was recognized. Coverage Filter. To correct for this possible bias, a filter using the coverage metric was applied to eliminate all firms that fell below Tier 3 for the coverage metric: i.e., estimated capacity to cover financing obligations is less than half of possible outcomes. With this filter applied, Tier 1’s members decline from 5 to 4 firms, Tier 2’s from 9 to 6 firms and Tier 3’s from 28 to 5 firms. The recast list is as follows (recall the ticker symbol is followed by the SIC code, then the estimated rank:

43 ALL TIERS MSFT 7372 1 AAPL 3571 1.2 FB 7370 1.4 INFY 7371 1.6 GOOGL 7370 2 ADP 7374 2.2 PG 2840 2.4 DOV 3530 2.4 ITW 3560 2.8 JNJ 2834 2.8 AMZN 5961 3 LMT 3760 3 INTC 3674 3.4 CLX 2842 3.4 NOC 3812 3.4

Additional Filters: Negative Cash Flow. A second filter was also applied to account for any firms in this coverage-filtered list that had ranked below Tier 3 for the negative cash flow metric. In this case, a single firm drops out, INTC. MAE 0.10. When a filter for the less stringent maximum acceptable exposure level of 10% is applied, the entire Tier 3 drops from the list, leaving the top 10. ALL TIERS MSFT 7372 1 AAPL 3571 1.2 FB 7370 1.4 INFY 7371 1.6 GOOGL 7370 2 ADP 7374 2.2 PG 2840 2.4 DOV 3530 2.4 ITW 3560 2.8 JNJ 2834 2.8

44 MAE 0.05. The maximum acceptable exposure level of 5% is a difficult hurdle; applying this filter to the above list, only the following firms remain: MSFT, AAPL, FB, INFY, GOOGL, ADP and DOV. The MAE 0.05 estimate for ITW of 4% of revenues was deemed just close enough for the top ten list. However, while JNJ is added to allow for the construction of a list of ten firms, the estimate of 1% falls short. Qualifications. While a tier ranges from 0 to 0.99 (e.g., 1.0 to 1.99) it should be emphasized that due to non-linearities the qualitative differences between companies can be significant within the same tier. A filter for the third metric, centrality, was also examined and adversely affected DOV, AMZN and NOC; in this case, DOV would drop out. However, centrality is left unfiltered here because the three firms’ centrality estimates were deemed within a reasonably acceptable range of the hurdle of 9.8%: Net free cash flow was 7.7% for DOV, 6.7% for AMZN and 8.2% for NOC. Moreover, although centrality cannot be ignored, an overriding concern is what may not always be captured– behavior in the tails, such as for negatively-skewed distributions. Estimates may be derived from interim, unaudited historical data for non-uniform time periods, and are subject to other shortcomings noted elsewhere in this Part 2 and in the Appendices. The results are not predictions or forecasts. Moreover, a low estimate does not necessarily imply the company’s imminent default as the role of external sources of financing cannot be underestimated; nor does a highly-ranked company face a non-trivial risk of serious setbacks. 2.2.13. Top Ten Finalists and Dividend Yields In this section, the top ten finalists from the previous section are compared in terms of their yields. The aim is to find companies with similar rankings that offer higher yields to narrow down the field of “value” candidates for investment. Dividend Coverage Roundup: Capacity Finalist Yields, Interim 2020 Summary

45 *The last report in the Dividend Coverage Capacity series applied filters to reveal the capacity finalists. * In this brief follow-up we look at what these top ten firms are currently yielding to see which may offer extra value. Background Common methods of evaluating dividend coverage look at the historical record of dividend-paying performance. Unfortunately, although standard financial analysis may reveal warning signs, dividend cuts, suspensions or growth slowdowns can come unexpectedly. Dividend coverage capacity is based on underlying factors summarized in five key metrics that center on financing obligations (which includes debt service) and cash flow generation rather than dividend-paying history; this means that companies that pay little or no dividends may figure on the list. This video report from Cautionary Global provides an overview of the metrics for dividend payers: Cautionary Global Financial Analysis: Comps for Dividend Champions, Interim 2020. Estimates are derived from theoretical models of the companies derived from a sample consisting of 59 companies analyzed in the Dividend Coverage Roundup and/or Cautionary Stress Testing reports to date in 2020. The following screen cap is of a video on Capacity Tiers that also presents the overall results (Tier 1, 2 and 3) at https://youtu.be/6RyEk-ZZhS8. Overview of Filtered Results In the last Coverage Capacity report, several filters were applied to arrive at a top 10 list. After the MAE 0.10 filter, the following list emerged:

46 ALL TIERS MSFT 7372 1 AAPL 3571 1.2 FB 7370 1.4 INFY 7371 1.6 GOOGL 7370 2 ADP 7374 2.2 PG 2840 2.4 DOV 3530 2.4 ITW 3560 2.8 JNJ 2834 2.8

An additional step was taken to apply a filter for the maximum acceptable exposure level of 5% (MAE 0.05) that yielded a further pared-down list of seven firms: MSFT, AAPL, FB, INFY, GOOGL, ADP and DOV. Note that DOV technically “failed” the centrality filter but the company remains on the top ten list for reasons explained in Qualifications. These top ten appear are shown dividend yield section next. Dividend Yields Now we line up the dividend yields of each of the ten finalists. The company ticker symbol is followed by its SIC code and approximate recent dividend yield in the rank (% is omitted in the table). In the Tier column, the ranks range from MSFT leading at 1.0 to ITW in Tier 2 at 2.8. in the Yield column, JNJ has the highest dividend yield at 2.66%; FB and GOOGL pay no dividend.

47 Symbol/SIC MSFT 7372 AAPL 3571 FB 7370 INFY 7371 GOOGL 7370 ADP 7374 PG 2840 DOV 3530 JNJ 2834 ITW 3560

Tier

Yield 1 1.2 1.4 1.6 2 2.2 2.4 2.4 2.8 2.8

1.04 0.66 0 1.63 0 2.43 2.21 1.76 2.66 2.24

Qualifications. While a tier ranges from 0 to 0.99 (e.g., 1.0 to 1.99) it should be emphasized that due to non-linearities the qualitative differences between companies can be significant within the same tier. A filter for the third metric, centrality, was also examined and adversely affected DOV, AMZN and NOC; in this case, DOV would drop out of the ten finalists list above. However, a centrality filter was not applied because the three firms’ centrality estimates were deemed to be in an acceptable range of the hurdle of 9.8%: The estimate for central net free cash flow was 7.7% for DOV, 6.7% for AMZN and 8.2% for NOC. Moreover, although centrality cannot be ignored, an overriding concern is what may not always be captured– behavior in the tails, such as for negatively-skewed distributions. Estimates may be derived from interim, unaudited historical data for non-uniform time periods, and are subject to other shortcomings noted elsewhere in this Part 2 and the Appendices. The results are not predictions or forecasts. Moreover, a low estimate does not necessarily imply the company’s imminent default as the role of external sources of financing cannot be underestimated; nor does a highly-ranked company face a non-trivial risk of serious setbacks.

48 Post-Filtering Recap. The filtering process to arrive at the ten finalists resulted in the following number of firms and proportions out of the sample of 60 firms: Tier 1: 4 firms (7%, rounded). Tier 2: 6 firms (10%); Tier 3: 3 firms (5%) after applying the Coverage filter; 0 after applying the negative cash flow filter.

APPENDICES A1. The Firms and Sample Data This section supplements the cautionary stress testing and coverage capacity discussion of Part 2 by listing all 70 firms covered. The term “sample” refers to the 59 firms selected for the derivation of the capacity tiers and ranking system. Note that ten additional firms in two industries that are not included in the sample are also shown in the tables below. Sample Selection. Non-financial international firms were selected, “international” either in the sense of global operations or non-U.S. based firms. A primary objective was to diversify 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. Many of the firms were 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. Firms and Industries. The tables below list a total of 70 global non-financial firms in 16 industries, classified by SIC code. The sample however excludes 10 of these firms in the gold mining and the chemical industry (marked with the letter “N” in the first column of the first table), leaving 14 industries and 59 firms in the sample. There are three groupings that overlap with the industries:

49 FAANG/Tech, Dividend Growth stocks, and Dividend Champions. Discrepancy. While the number of firms in the sample is 59, the methodology included one firm twice for a total number of 60 datasets used for the sample: The company Briggs and Stratton (BGG) was duplicated to artificially increase representation of a specific type of company situation: One that is nearing bankruptcy filing, which occurred to BGG in July 2020. Market Capitalization. Total market cap as of early 2021 is estimated at US$13.3T for all 70 firms, and US$12.9T for the 59 firms in the sample. The sample is heavily weighted in favor of the technology sector which comprises 60% of the total market capitalization. Within the technology sector, a single technology firm comprises 15% of the total market capitalization, AAPL, and 12% for MSFT. Geographic Distribution. Many firms are global in scope, although their country of origin refers generally to the legal jurisdiction of incorporation, as reported to the SEC. It should be noted that legal jurisdictions can change (e.g., TEVA Pharmaceuticals, which has changed its legal domicile from Israel to the U.S.). Financial Data Overview General information regarding financial data is noted here, for more on the financial data, see Appendix 2 (detail) and Appendix 3 (broader context). Data Sources. Primary 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 10-K Reports, company annual reports that include the financial statements, or from online data sources that aggregate the data from these primary sources. 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,

50 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 from each firm are grouped into datasets of between five and up to thirty fiscal years depending on available data; distributions are fitted based on these datasets. 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). Metrics. For the individual metrics (1 through 5), the data series are the estimates obtained for each metric: Coverage, Negative Cash Flow Exposure, Centrality, MAE 0.05, and MAE 0.10. These are listed in the table. Fiscal Year. Any non-calendar year fiscal year ends are noted in the last column. 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 March 31st, 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. Interim typically refers to the twelve-month period that ends differently from the fiscal year end. 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.

51 Although the financial information is deemed to be of reasonably good quality, data discrepancies may be found that cannot be satisfactorily reconciled. 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. TABLE A1.1 Alphabetical Listing of Firms (see below)

# 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

Sample

N N

Symbol SIC Code Coverage AAPL ABBV ADP AEM AMZN AON APD AU BGG CAH CAJ CHL CL CLX CNQ CVX DD DEO DOV DOW EMN EMR EQNR F FB FMX FNV GOLD GOOGL HMC IFF INFY INGR INTC ITW JNJ KHC KO LMT MCD MDLZ MDT MMM MO MSFT NEM NFLX NOC PFE PG PM PNR PPG PTR QCOM RTX SAP SSL SYK T TEVA TMO TSLA TSM TTM UN VFC VIV WPM XOM

3571 2834 7374 1040 5961 6411 2810 1040 3510 5122 3861 4813 2844 2842 1311 2911 2821 2080 3530 2821 2821 3600 2911 3711 7370 2086 1040 1040 7370 3711 2860 7371 2040 3674 3560 2834 2030 2080 3760 5812 2000 3845 3841 2111 7372 1040 7841 3812 2834 2840 2111 3550 2851 1311 3663 3724 7372 1311 3841 4813 3834 3829 3711 3674 3711 2840 2320 4813 1040 2911

0.99 0.11 0.88 0.04 0.88 0.02 0.04 0.00 0.05 0.21 0.33 0.02 0.03 0.61 0.49 0.03 0.01 0.06 0.93 0.00 0.16 0.36 0.03 0.00 0.97 0.02 0.01 0.50 0.97 0.00 0.19 0.97 0.01 0.53 0.59 0.78 0.03 0.03 0.82 0.05 0.00 0.28 0.05 0.08 0.99 0.06 0.02 0.83 0.00 0.76 0.01 0.09 0.03 0.00 0.06 0.02 0.06 0.00 0.23 0.03 0.06 0.08 0.04 0.00 0.04 0.03 0.01 0.03 0.20 0.04

Neg CF 0.01 0.02 0.02 0.51 0.03 0.06 0.07 0.10 0.67 0.40 0.11 0.08 0.09 0.06 0.37 0.07 0.36 0.03 0.02 0.56 0.03 0.20 0.13 0.02 0.03 0.24 0.56 0.11 0.03 0.11 0.14 0.02 0.03 0.25 0.04 0.05 0.11 0.08 0.04 0.03 0.05 0.04 0.16 0.14 0.01 0.05 0.98 0.06 0.17 0.04 0.01 0.06 0.07 0.15 0.09 0.13 0.14 0.97 0.45 0.06 0.17 0.38 0.62 0.04 0.70 0.06 0.07 0.05 0.22 0.22

NonCentrality MAE 0.05 MAE 0.10 Calendar FYE 0.24 0.21 0.22 9/29 0.36 0.18 0.26 0.13 0.07 0.10 6/29 0.01 -0.45 -0.27 0.07 0.02 0.04 0.13 -0.03 0.05 0.07 -0.03 0.02 9/29 0.10 -0.09 0.01 0.00 -0.10 -0.06 6/30 0.01 -0.04 -0.03 6/29 0.05 -0.06 -0.01 0.07 -0.04 0.02 0.13 -0.11 0.01 0.12 -0.01 0.03 6/29 0.10 -0.60 -0.30 0.10 -0.04 0.02 0.02 -0.19 -0.09 0.20 0.09 0.13 6/29 0.08 0.05 0.06 0.04 -0.43 -0.23 0.11 0.05 0.08 0.11 -0.17 -0.08 9/29 0.06 -0.07 -0.01 0.07 0.04 0.05 0.30 0.11 0.22 0.02 -0.05 -0.03 0.05 -1.75 -0.83 0.12 -0.15 -0.01 0.17 0.08 0.13 0.02 -0.02 0.00 3/30 0.07 -0.13 -0.03 0.17 0.11 0.14 0.05 0.02 0.04 0.20 -0.24 -0.15 0.17 0.04 0.11 0.18 0.01 0.10 0.11 -0.15 -0.02 0.14 -0.10 0.02 0.10 0.04 0.07 0.19 0.06 0.12 0.08 0.00 0.04 0.15 0.03 0.09 4/29 0.09 -0.03 0.00 0.22 -0.45 -0.11 0.28 0.24 0.26 6/30 0.20 0.02 0.10 -0.18 -0.27 -0.22 0.08 -0.02 0.03 0.25 -0.27 -0.12 0.17 0.05 0.11 6/30 0.26 0.24 0.25 0.14 -0.01 0.05 0.06 0.03 0.04 0.03 -0.07 -0.02 0.19 -0.15 0.02 9/29 0.04 -0.08 -0.02 0.14 -0.17 -0.05 -0.12 -0.33 -0.22 6/29 0.02 -0.32 -0.22 0.12 -0.02 0.05 0.07 -0.21 -0.06 0.11 -0.33 -0.23 0.01 -0.36 -0.30 0.23 0.03 0.10 -0.02 -0.14 -0.08 3/31 0.06 -0.01 0.03 0.06 -0.02 0.02 3/31 0.09 0.01 0.05 0.62 -1.19 -0.53 0.02 -0.08 -0.03

53 TABLE A1.2 Firms by SIC Code with Industry/Category (see below)

Firm #

Industry/Category

Symbol SIC Code Coverage

1 AEM 2 AU 3 Gold and Silver Ores FNV 4 GOLD 5 NEM 6 WPM 7 SSL Oil & Gas 8 CNQ 9 PTR 10 MDLZ Food Products 11 KHC 12 INGR 13 KO Beverages 14 DEO 15 FMX 16 PM Tobacco 17 MO 18 Dividend Champions VFC 19 APD 20 DD 21 Chemicals DOW 22 EMN 23 PPG 24 IFF 25 JNJ 26 ABBV Pharmaceuticals 27 PFE 28 TEVA 29 PG 30 UN Household & Personal Products 31 CLX 32 CL 33 CVX Petroleum Refiners 34 XOM 35 EQNR 36 BGG 37 DOV Machinery/Equipment Makers 38 PNR 39 ITW 40 FAANG/Tech AAPL 41 Dividend Growth EMR 42 QCOM Semiconductors 43 INTC 44 TSM 45 TSLA 46 F Auto Makers 47 HMC 48 TTM 49 RTX Aerospace & Defense 50 LMT 51 NOC 52 TMO 53 MMM Diversified Instruments 54 SYK 55 MDT 56 CAJ 57 T Telecommunications 58 CHL 59 VIV 60 Dividend Growth CAH 61 Dividend Champions MCD 62 FAANG/Tech AMZN 63 Dividend Growth AON 64 FAANG/Tech GOOGL 65 FAANG/Tech FB 66 INFY 67 MSFT Software/Programming 68 SAP 69 ADP 70 FAANG/Tech NFLX

1040 1040 1040 1040 1040 1040 1311 1311 1311 2000 2030 2040 2080 2080 2086 2111 2111 2320 2810 2821 2821 2821 2851 2860 2834 2834 2834 2834 2840 2840 2842 2844 2911 2911 2911 3510 3530 3550 3560 3571 3600 3663 3674 3674 3711 3711 3711 3711 3724 3760 3812 3829 3841 3841 3845 3861 4813 4813 4813 5122 5812 5961 6411 7370 7370 7371 7372 7372 7374 7841

0.04 0.00 0.01 0.50 0.06 0.20 0.00 0.49 0.00 0.00 0.03 0.01 0.03 0.06 0.02 0.01 0.08 0.01 0.04 0.01 0.00 0.16 0.03 0.185 0.78 0.11 0.00 0.06 0.76 0.03 0.61 0.03 0.03 0.04 0.03 0.05 0.93 0.09 0.59 0.99 0.36 0.06 0.53 0.00 0.04 0.00 0.00 0.04 0.02 0.82 0.83 0.08 0.05 0.23 0.28 0.33 0.03 0.02 0.03 0.21 0.05 0.88 0.02 0.97 0.97 0.97 0.99 0.06 0.88 0.02

NonNeg CF Centrality MAE 0.05 MAE 0.10 Calendar FYE 0.51 0.01 -0.45 -0.27 0.10 0.10 -0.09 0.01 0.56 0.05 -1.75 -0.83 0.11 0.12 -0.15 -0.01 0.05 0.20 0.02 0.10 0.22 0.62 -1.19 -0.53 0.97 -0.12 -0.33 -0.22 6/29 0.37 0.10 -0.60 -0.30 0.15 0.03 -0.07 -0.02 0.05 0.08 0.00 0.04 0.11 0.11 -0.15 -0.02 0.03 0.05 0.02 0.04 0.08 0.14 -0.10 0.02 0.03 0.20 0.09 0.13 6/29 0.24 0.02 -0.05 -0.03 0.01 0.26 0.24 0.25 0.14 0.22 -0.45 -0.11 0.07 0.06 -0.02 0.02 3/31 0.07 0.07 -0.03 0.02 9/29 0.36 0.02 -0.19 -0.09 0.56 0.04 -0.43 -0.23 0.03 0.11 0.05 0.08 0.07 0.06 0.03 0.04 0.138 0.068 -0.13 -0.029 0.05 0.18 0.01 0.10 0.02 0.36 0.18 0.26 0.17 0.25 -0.27 -0.12 0.17 0.07 -0.21 -0.06 0.04 0.17 0.05 0.11 6/30 0.06 0.06 -0.01 0.03 0.06 0.12 -0.01 0.03 6/29 0.09 0.13 -0.11 0.01 0.07 0.10 -0.04 0.02 0.22 0.02 -0.08 -0.03 0.13 0.06 -0.07 -0.01 0.67 0.00 -0.10 -0.06 6/30 0.02 0.08 0.05 0.06 0.06 0.14 -0.01 0.05 0.04 0.17 0.04 0.11 0.01 0.24 0.21 0.22 9/29 0.20 0.11 -0.17 -0.08 9/29 0.09 0.19 -0.15 0.02 9/29 0.25 0.20 -0.24 -0.15 0.04 0.23 0.03 0.10 0.62 0.01 -0.36 -0.30 0.02 0.07 0.04 0.05 0.11 0.02 -0.02 0.00 3/31 0.70 -0.02 -0.14 -0.08 3/31 0.13 0.04 -0.08 -0.02 0.04 0.10 0.04 0.07 0.06 0.08 -0.02 0.03 0.38 0.11 -0.33 -0.23 0.16 0.09 -0.03 0.00 0.45 0.02 -0.32 -0.22 0.04 0.15 0.03 0.09 4/29 0.11 0.05 -0.06 -0.01 0.06 0.12 -0.02 0.05 0.08 0.07 -0.04 0.02 0.05 0.09 0.01 0.05 0.40 0.01 -0.04 -0.03 6/29 0.03 0.19 0.06 0.12 0.03 0.07 0.02 0.04 0.06 0.13 -0.03 0.05 0.03 0.17 0.08 0.13 0.03 0.30 0.11 0.22 0.02 0.17 0.11 0.14 0.01 0.28 0.24 0.26 6/30 0.14 0.14 -0.17 -0.05 0.02 0.13 0.07 0.10 6/29 0.98 -0.18 -0.27 -0.22

Overlap

Div Champs

Div Champs Div Growth

Div Growth Div Champs

Div Champs Div Champs

Soft/Progr Soft/Progr FAANG/Tech Div Champs

55 Top-Ranked Fitted Distributions: Breakdown Chart A1.1. The following pie chart shows the breakdown of the top-ranked fitted distributions for the net free cash flows of each of the 59 firms in the sample.

As seen in the chart, five types of distributions account for close to three-quarters of the top-ranked fits, with the top two, Generalized Extreme Value (20%) and Cauchy (19%) accounting for 39% of all fitted distributions of the sample. Note that these top-ranked distributions are not necessarily the same as those that were selected based on the cautionary methodology. Negative Estimates. A negative estimate for either centrality or the maximum acceptable exposure (MAE) metrics (MAE 0.05 and MAE 0.10) suggests that the company cannot support any level of financing obligations given those assumed parameters; negative centrality is equivalent to an MAE 0.50, which suggests that in half of possible outcomes the company cannot cover any financing obligations. To recap, the 70 firms of this study are broken down into the sample used for the ranking system (59 firms) and the remaining firms outside of the sample which consists of 5 firms in the chemical industry (SIC 2810-2860) and 6 firms in gold and silver ores (SIC 1040).

56 The results from the distributions selected based on the cautionary methodology are also compared with the estimates generated from the highest-ranking distributions. Sample. The proportion of estimates from the sample of 59 firms that were negative for MAE 0.05 and MAE 0.10 were 63% and 37%, respectively. Negative centrality occurred in 7% of the estimates (4 firms of the 59). The corresponding estimates for the highest-ranking distributions were 36% for MAE 0.05 and 29% for MAE 0.10 and 8% for centrality. For the remaining eleven firms that did not figure in the sample, the corresponding proportions were 82% for MAE 0.05 and 64% for MAE 0.10. Negative centrality was not found in any of the selected distributions, but the highest-ranking distributions generated negative centrality estimates in three of the eleven firms (27%); two were in gold and silver ores (FNV and AEM), one in the chemical industry (DOW). Invalid Results. Among the top-ranking fitted distributions shown in the section above, a certain number were found to generate invalid results. This section calls attention to the invalid results obtained using the highest-ranking fitted distributions of the sample 59 firms. Of interest was to identify the proportion of fitted distributions that ranked #1 in terms of goodness-of-fit testing but that generated invalid results for specific metrics. The findings are as follows (percentages rounded): Metric 1. Coverage capacity, survival function P(X>X1), where X1= financing obligations; 20 invalid results of 59 total (34%). Metric 2. Negative Cash Flow Exposure, cumulative density P(X<X1), X1=0, where X1 is negative net free cash flow; 7 invalid results of 59 total (12%). It was hypothesized that despite the high rankings these distributions did not sufficiently reflect the processes, prompting the use of lower-ranked fat-tailed distributions that did not produce invalid results using the observed values and estimates. This should not imply that the fat tail distributions categorically represent the “correct” model, but that at minimum these appear to capture the observed results.

57 A2. Financial Data Detail Cautionary Audit Introduction. This appendix reproduces a short course on financial data using a cautionary framework for specialized audit purposes, expanding on the discussion of the main text. This appendix begins with general steps for classifying and obtaining the financial data needed for a preliminary audit. This cautionary audit focuses on whether companies can cover existing dividends and debt service, as well as estimating their potential level of financing obligations coverage capacity. Company Selection: Audits offered through this course, if approved, are currently limited to publicly traded companies with a ticker symbol for which data is accessible online. “Publicly traded” refers to companies whose shares are traded on an exchange (e.g., the New York Stock Exchange, or Nasdaq). There are many companies traded on exchanges and information should be available online. Examples of various types of business entities, including publicly-traded companies, are shown at the end of this section. Required Information. The following blank Required Information form contains sections in yellow that need to be completed. There are two sections: 1. Basic Information and 2. Input Table. Each section will be discussed next using an example of an actual publicly-traded company. Note that that for conciseness the example given here is limited to a minimum number of data points; a larger sample is advisable where possible.

58 REQUIRED INFORMATION *Yellow cells indicate where information/data are input BASIC INFORMATION Company Name (Ticker Symbol): SIC Code: Fiscal Year End: INPUT TABLE Period Income Statement FY or TTM Revenues Interest

FCF

Cash Flow Statement Acquisitions Debt Rpmt Divs Paid

Reminders: A minimum of the most recent 5 fiscal years of financial data is needed. TTM (trailing 12 months) data may not be available and is not required. Figures should be input as they are displayed on the financial statements or screen. Red variables (Acquisitions, Debt Repmt, Dividends Paid) are input as negatives. Refer to document titled "Financial Data" and accompanying videos for further details.

#1. Basic Information Section An example using the publicly-trade pharmaceutical company Pfizer is used here. Further explanations are provided below this list. 1. Company Name and Ticker Symbol: Pfizer (PFE)* 2. SIC Code: 2834 3. Fiscal Year End: 12/31 (*) Important Note. If you have chosen a publicly-traded company that is not traded on a major exchange (e.g., New York Stock Exchange, Nasdaq) indicate not only the name of the company, its ticker symbol, but also add the name of the exchange on which it trades under that ticker symbol. SIC Code is the Standard Industrial Classification code (SIC code for short). A good source for SIC codes for publicly traded companies is the SEC website which is introduced in the last section of this document. If the SIC Code is not available there some additional research may be necessary to identify the SIC

59 code that would best match the businesses activities. Online sources under “Standard Industrial Classification Codes” (e.g., Bureau of Labor Statistics) may also help. Fiscal Year End (abbreviated FYE) is the date that the company closes out twelve months of its financial history. Fiscal year ends can be calendar year basis (e.g., January 1 to December 31) or non-calendar year basis (e.g., March 31 to March 31) For example, the fiscal year 2020 financial statement for a calendar year statement would end as of December 31, 2020; for a noncalendar year statement, fiscal year 2020 could end as of March 31, 2020, or September 28, 2020. Most companies are calendar year basis. Fiscal year may be abbreviated “FY.” #2. Input Table See the example next of a completed input table. The cells in yellow highlight are the datapoints needed to be filled in. In the example below, this is data for Pfizer (PFE). Requirements: Time Period. A minimum of the most recent five years (i.e., five twelve-month periods) of financial data is needed. Recall from above that the example given here is limited to a minimum number of data points; a larger sample is desirable if sufficient data are available. Inputting Data. It is recommended to input the data exactly as they are displayed on the financial statement or screen you are using. If you are using multiple sources of financial information to complete the data, be sure that what you input is consistent.

60 Input Table (rows and columns numbered). 1 1 Period 2 FY or TTM 3 TTM 4 FY 2019 5 FY 2018 6 FY 2017 7 FY 2016

2 3 Income Statement Revenues Interest 49,197,000 1,586,000 51,750,000 1,574,000 53,647,000 1,316,000 52,546,000 1,270,000 52,824,000 1,186,000

4 FCF 12,604,000 9,994,000 13,631,000 14,253,000 13,902,000

5 6 Cash Flow Statement Acquisitions Debt Rpmt 0 -22,523,000 -10,861,000 -12,633,000 0 -9,620,000 -1,000,000 -14,743,000 -18,368,000 -15,875,000

7 Divs Paid -8,212,000 -8,043,000 -7,978,000 -7,659,000 -7,317,000

Columns and Rows. The columns are labelled for reference, from left to right, 1 through 7. The rows are labelled from top to bottom, 1 through 7. Period (Column 1, Row 1) indicates the period which the data covers. There are two types: Fiscal Year (abbreviated FY) and “trailing 12 months” abbreviated TTM. “TTM” is “trailing twelve months’ and incorporates the most recent twelve months of financial data for the firm; this data is not always available and is not required. The advantage of TTM, if available, is that it can bring the financial data more current. The disadvantage is the quality of information may be less reliable, and there is some overlap with a part of the prior fiscal year. If TTM data is not available, since it is not required, you would input the most recent 5 fiscal years. Fiscal Year (FY). The year indicates the fiscal year of the company; recall “FY” is an abbreviation for “fiscal year.” A fiscal year is a twelve-month period that ends on the fiscal year end date such as December 31 for a calendar year, and (for example) June 30 for a non-calendar year. Income Statement, Cash Flow Statement (Row 1) This indicates the financial statement from which the data beginning in Row 3 and below are usually obtained. Revenues and Interest are typically found on the income statement. Free cash flow (FCF), acquisitions, debt repayment and dividends paid data are typically found on the cash flow statement.

61 Variables (Row 2): Revenues, Interest Expense, Free Cash Flow (FCF), Acquisitions, Debt Repayment and Dividends Paid. The red font for the three variables is to emphasize that they are negative values. Revenues and Interest Expense are input as positive values. Free Cash Flow (FCF) may be positive or negative, depending on the company performance. Terminology. The terms to describe the data can slightly differ from place to place. Some common alternatives are noted along with the name used here. Column 2 and 3 (Income Statement) Revenues, Net (or "Net Sales", "Net Turnover") is normally found near or at the top of the Income Statement. Interest is normally found somewhere in the middle of the Income Statement, typically titled “Interest Expense” and which should be interest on debt, not capitalized interest. Tip. Occasionally, interest expense is hard to find on the income statement and searching for the figures in the Notes to the financial statements can be time-consuming. The best alternative is to look for a line item “Interest or “Cash Interest Paid” (or similar wording) at the end of the Cash Flow Statement. This interest figure is the cash paid for interest, not interest expense. While there are likely to be differences between the figures, if interest expense figures are not readily available, cash interest paid is an alternative. Column 4. Free Cash Flow (abbreviated FCF) FCF requires three variables not seen on the Input Table and which must be computed separately. They are listed next: Net Cash from Operations (NCO) Capital Expenditures (may be abbreviated Capex) Intangible assets Formula. To compute free cash flow from these variables:

62 NCO minus Capex minus Intangibles = Free Cash Flow Tips FCF. Some sources of data may report a Free Cash Flow figure which is convenient; however, you should confirm that it is being computed in the same way as shown in the formula above. NCO. Net Cash from Operations may also be called "Net Cash Flow-Operating Activities" or "Cash Flow from Continuing Operations." Capital expenditures may also be called “capex” as noted above, but on the financial statements more commonly may appear as "purchases of property, plant and equipment" (abbreviated P, P & E). Intangible assets may also be indicated on the financial statement as an "Acquisition” or “Purchase” of Intangible Assets. Column 5. Acquisitions (of Businesses) Input as a negative; may also be labelled "net assets from acquisitions"; this can include several types of acquisition activities including purchase of businesses or business assets, merger activity, technology acquisitions and joint ventures. Warning. Acquisitions does not include purchases of short-term investments in marketable securities. This occurs when companies may “park” excess cash in as part of their cash management and is not an acquisition of a business or related activity. Column 6. Debt Repayment (Reductions) Input as a negative. This includes repayment of both short-term debt and long-term debt; “repayment” may also be called "reductions.” Warning. Be careful that the debt repayment figures shown are not “net”, meaning that the debt repayment amount is offset

63 against amounts borrowed. When reported as “net”, this potentially understates the amount of debt that is being repaid. Column 7 Cash Dividends Paid. Input as a negative; these may also be called "cash paid, common dividends". Important Note: Dashes. Data will occasionally include a dash (-) in the statement or on the screen. There are likely two main reasons: 1) zero or “amount not meaningful.” For example, in the following column of figures for a variable such as acquisitions, if a dash appears it can usually safely be replaced by a zero. 2020 2019 2018 2017 2016 2015

48,000 2,000 358,000 74,100 9,429

2) If most of a row or column contains dashes, then the full dataset is likely not available yet. In this case, an alternative source of data may be needed; see the next section on Data Sources. Data Sources (Examples) Summary The major sources of financial information presented were: 1. The company financial statements, which can be found a) on the company website either the ANNUAL REPORT or FINANCIAL REPORT or b) on the SEC website inside the SEC FILING documents or uploaded as a separate document from the filings.

2. Business websites. Examples include but are not limited to CNBC, Yahoo! Finance or Bloomberg business. A reminder of the information needed: Basic Information Company Name and Ticker Symbol: Pfizer (PFE)* SIC Code: 2834 Fiscal Year End: 12/31 Input Table

Next, we go through the steps to locate these same data points shown in the yellow part of the table: a) directly from the company financial statement, and b) from a business website. This section centers on sources of financial data that should be accessible online. Videos also complement this section. Updates/Changes. There are frequent changes to the presentation on websites; therefore, the description below and video could differ from what you see on the websites in the future. Examples provided here apply to publicly-traded companies for which financial information is readily available in most cases. The sources listed here are some examples and this is not a comprehensive list.

65 Source #1. The Company Website The company Annual Report is typically found on a page on the company’s website and contain the financial report/financial statements. There may be an option select the Annual Financial Report of just the financial statements rather than the annual report which may be considerably longer. The financial report for any given fiscal year typically covers three fiscal years on the income statement and cash flow statement (not the balance sheet). This because you obtain three fiscal years of data at once. For example: in the Financial Report or Annual Report for Fiscal Year 2019, the financial statements will cover 2019, 2018, and 2017. You want to source your data from Consolidated financial statements; fortunately, these are generally the main type of statement presented in the Annual Reports. However, occasionally “non-consolidated” or “combined” financial statements may be reported; these should be avoided. The highest quality of financial information – an Audited financial statement -- is preferred. Fortunately, publicly-traded companies on exchanges should have audited fiscal year-end financial statements; note that quarterly statements may often be unaudited. Steps. In the search engine, type in the company name and keywords such as "company annual information" or "financials" or "annual reports" This should take you to the company's investor relations or similar page. for example, for the company Pfizer the URL appeared in the top results (https is removed here): //investors.pfizer.com/financials/annual-reports/default.aspx There you can select what is offered, either the Annual Report or Financial Report by selecting the fiscal year of that report (FY 2020, 2019, etc.).

66 Source #2. Financial Website Example: CNBC CNBC generally provides four years of fiscal year end historical data. To obtain a fifth year, it will be necessary to go to the company site (#1 above) or to the SEC website (#5 below) or other reliable sources. Steps. In the search box, type in the company name or symbol. Select Financials, then SEC FILINGS (HTML Format, PDF FORMAT) Warning. As noted above the Repayment of Debt data should be gross, not net. At this time, CNBC's site appears, at least for some companies, may indicate debt repayment as “net”, which means that the debt repayments are offset by the company’s borrowings. This form of presenting the data can understate the amount of cash the company needs to repay debt. Therefore, for the repayment of debt data point, other sources are recommended. Also see the notes to Yahoo Finance on this topic which currently does not appear to net out the borrowings. Source #3. Financial Website Example: Yahoo Finance Steps. On the Yahoo! Finance website, type the company symbol into “QUOTE LOOKUP” field. It may take a moment for the symbol to be recognized. Select the "Financials" tab. The first financial statement that appears should be the Income Statement. Yahoo provides 4 years of financial data plus adds a 5th year (12month period, called TTM for "trailing twelve months" prior to the fiscal year-end). See Tip #2 below, however because sometimes the most recent 12-month period of data is repeated. Tips 1.Arrows. On the Yahoo! Finance site, the data can be broken down into considerable detail. You may need to click on the

67 “down” arrows to expand the selection to find what you are looking for. 2. Repetition of Data. If the most recent fiscal year end data is already in their system, they may report the same data for the last two years. In this case, to obtain the minimum 5 years of data you will need to get the earlier financial statements (see the other sources listed here #1. Company Website or #5. SEC website). 3.Repayment of Debt. Yahoo Finance currently provides a more conservative and preferred figure for debt repayment. This is because at this time debt repayment is not netted against borrowings as the CNBC’s website may do (refer to this issue in Source #2: CNBC website). Source #4. Financial Website: Bloomberg Bloomberg's site may offer financial data for subscribers but at this time detailed financial data does not appear to be readily available for non-subscribers. Source #5. Securities & Exchange Commission (SEC) Website On the SEC website, type the symbol in the COMPANY SEARCH field. A moment may be necessary for the symbol to be recognized. Tip: Occasionally a symbol may resemble the symbol of other companies and special care must be taken not to confuse the companies. Steps. SIC Code. The SIC Code is currently found at the top left of the FILINGS information page. Select 10-K for the annual report filing for the fiscal year end; select the fiscal year you need. Tip. A financial report typically covers three fiscal years on the income statement and cash flow statement (not the balance sheet).

68 Therefore, by selecting the annual report’s latest fiscal year, you obtain three fiscal years’ data (for example: selecting 10-K for Fiscal Year 2019, the financial statements will cover 2019, 2018, and 2017. Tip. If you want to see a partial year statement filing, select 10-Q (Q for “quarterly”) Type of Data. The website gives a choice of type of data: DOCUMENTS, or INTERACTIVE DATA: *The DOCUMENTS option gives you a copy of the original 10K SEC filing. This is the true source document. *INTERACTIVE DATA: If you select this, you have 2 options: Excel or HTML document format. View Excel Document. The statements are found in the worksheets of the document; look for tabs to obtain the data: Consolidated Statements of Income Consolidated Statements of Cash flows Html. If you prefer this option, click on the navigation bar at left to get to the financial statement listed. Just as with the Excel option, look for the link to Consolidated Statements of Income and Consolidated Statement of Cash flows. Prior Fiscal Years. If you select the Interactive Data and Excel document, normally there is 3 Years of Data. To obtain previous years, you must go back into the filings/documents to find those fiscal years. Filing Date. Note that reports and filings take a while to complete, and the filing may be dated a few months after the fiscal year end, so the filing date for the Fiscal year 2019 filing may say “March 9, 2020.” Notes on Business Entity Types 1. Publicly traded companies on an exchange (e.g., traded on the New York Stock Exchange, or Nasdaq). There are many companies traded on exchanges and information should be available online.

69 2. A privately-held company whose stock is not traded on an exchange. This can include a small business that has several years of operating history and financial data, but the information is generally available only to owners, insiders, and financiers. 3. Real estate. Income real estate that generates cash flow can also be considered a type of business activity. Sometimes the real estate is owned by a business so that #1 (publicly-traded) or #2 (privately-held) in this list also apply. 4. A start-up business. A “start-up” is defined here as a business that has either zero or minimal financial history. For a company with no history, proforma financial statements may be constructed as part of a proposed business and/or financing plan. These data are not real as they are projections. For a start-up with a few years of history, the data can be combination of the actual historical data with projections. A3. Financial Analysis: The Broader Context Introduction. The following text is the approximate transcription of an introductory video presenting the broader context of financial analysis as it relates to the methodology that is employed here. The video is titled Introduction to Cautionary Global Financial Analysis, with the term global referencing firms operating on an international scale. This video is also included as part of a course titled Cautionary Stress Testing on the Thinkific platform. Link: https://youtu.be/xQw7AnWHByY

70 TABLE: Accounting Framework

Whether you are a business owner, an investor trying to invest for income or retirement, or even a credit or portfolio manager, this analysis focuses on some key questions: 1.Can businesses pay back their debts? 2.Are dividend-paying companies capable of covering their dividends and debt service as well? We begin with the accounting framework of a firm, from local businesses in different countries to firms that operate on a global scale. The analysis applies mostly to non-financial businesses, whether privately held, or public companies that issue stock on publiclytraded stock markets worldwide. The focus is on private sector businesses and their underlying securities; therefore, government and agency debts, derivatives such as futures and options, are excluded. The approach is to err on the side of caution to evaluate businesses, thus a "cautionary" analysis. This topic is so broad to be oversimplified in parts, with inaccuracies. Some terminology may differ by custom or country. The terms "business", "firm" or "company", and even "corporation" may be used interchangeably.

71 Accounting Framework Close-up How do firms get financing? We start with the balance sheet here. There are two basic ways, shown on the right-hand side of the balance sheet: 1. Financing with Debt (in pink) 2. Financing with Equity (in green) The Debt part means borrowing (with bank loans or debt instruments like commercial paper or bonds. Note that because this is debt the investors in these are also lenders. The Equity part is by raising money from owners (such as by issuing stock to stockholders or equity holders). For simplicity, these sources of financing are referred to as "financing instruments." How do investors provide financing for these firms? The two come together in the capital markets: For example, financing instruments (such as bonds and stocks) can be issued to raise funds in markets such as we see here: Credit, money, corporate debt markets, and stock markets. *Notes: 1. Be aware of the key difference between primary market issuance, when the funds from the financing goes directly to the firm; and secondary market trading of the security after issuance of the stock or bond. The actual "financing" part has ended once the instrument is traded on the secondary market. 2. Loans and some types of debt may be initially obtained directly from financial institutions but can later end up in a market through securitization of that obligation. Who holds these securities as investments? Some examples include: 1. individuals through brokerage firms. 2. institutions such as Banks, Hedge Funds, Insurance Companies, Investment Banks, Pension/Retirement Plans The individual instruments may be packaged into short or longerterm investment vehicles such as:

72 money market funds, exchange traded funds or notes, mutual funds, closed-end funds, and so on. Payments (in yellow) What do investors receive from these companies in return for financing them? These yellow highlighted areas designate the payments part of the financing: 1. Debts are normally to be repaid with interest; this means return of the principal balance loaned + the interest. The sum of principal and interest is called Debt Service. 2. Equities. A company may choose to pay dividends on its stock, investors may desire dividend growth over time. For small businesses, owners (who can be thought of as investors in their own businesses) may pay themselves what may be termed owner distributions or owner withdrawals instead of dividends. Summarizing, these are two basic types of payments to investors: 1. Debt Service: The principal plus interest. 2. Dividends (if paid) The sum of these two is a total outflow that is termed here financing obligations (the two yellow sections added together). Primary Source of Funds Cash flow is necessary to pay the financing obligations. However, what is the proper definition of cash flow as this primary source of funds? And where is this information obtained? Clarifications are provided next. 1.Additional Financing Excluded. If a company borrows more money or issues more stock to help pay for these financing obligations, this is cash flow but not a primary source of funds. The company is adding to its obligations to investors. Therefore, we must exclude any financing items from our analysis. 2. Limitations of the income statement. Unless the income statement is cash basis, the income statement is not directly a usable source of information. The income statement shows revenues, expenses, and net income (also known as net profit), but unless it is a cash basis income statement, is typically based on the

73 accrual method of accounting. Accrual accounting is a valid accounting method to recognize revenues when earned and expenses when incurred but does not necessarily reflect the movement of cash (cash flow) to cover the financing obligations. 3. Recurring Operations. To be called a primary source of funds, the cash flow must derive from the recurring, regular operations that form the core of the business. This requirement is not entirely obvious at times, and each company may need to be examined to determine this core and eliminate non-recurring sources of cash (such as the one-time sale of assets or a business). Net Free Cash Flow: Detail Subject to the points made above, cash flow is the primary source of payment, but has a specific definition. Refer to the Table above to walk through a simplified example of a cash flow statement. Inflows. We start with the cash sales as inflows to the firm then subtract three major categories of outflows* to obtain Net Free Cash Flow (abbreviated NFCF). *These outflow items deducted are operational cash outflows, capital expenditures (“capex”) and acquisitions and acquisitionrelated activity, as can be seen in the table. Recall that any financing items (e.g., debt or equity issuance) are excluded from the net free cash flow computation because they are external and are not part of the recurring regular business operations. Information Sources. Where is cash flow data found? Not surprisingly on the cash flow statement of the company’s annual report. There are other methods to derive cash flow figures, including from the balance sheet and income statements but this is beyond the scope of discussion. Concluding Remarks Linking up the items discussed, the yellow color coding helps us see the link from the primary source of payment--net free cash flow as defined--to the payments that cover the company's debt service and dividends. This introduction hopefully helped provide an overview of:

74 1.Financing and investing activity, from private non-financial firms seeking financing to individuals, institutions and other firms looking for places to invest. 2.The primary source of the funds (net free cash flow, as defined) to cover the financing obligations of firms to their lenders and investors.

A4. Credit Analysis Considerations This appendix lists several important considerations in credit analysis and evaluation for the extension of credit to business borrowers. Relevant excerpts from the author’s Interest Rate Analytics (2015) are included here. A4.1. Debt Service and Permanent Non-Amortizing Debt This section begins with some background information leading to the problem of accounting for permanent non-amortizing debt in the estimation of debt service. The cautionary approach to the treatment of debt service of Part 2 addresses the issue of permanent non-amortizing debt detailed in this section. Complicating the analysis of debt service capacity involves the existence of problem loans that are not necessarily recognized as such. The borrower may be capable of servicing its term debt, but a significant portion of other debt remains outstanding, unamortized, and perpetually rolled over in an “evergreening” process. If this type of debt can be termed out, lenders may initiate debt restructuring plans to have the outstanding debt amortized over an extended time to allow debt service to better align with cash flow. Debt Service Capacity. Debt service can be divided into amortizing and non-amortizing debt. Debt service generally refers to the return of the principal lent and interest (P + i) through amortization of the loan, either full or partial. A schedule of the periodic payments (and the principal and interest amounts contained within those repayments) can be referred to as an amortization schedule. Periodic payments can be monthly,

75 quarterly, or annually. For example, the debt service payments for a mortgage are typically made at regular monthly intervals. For a mortgage, the loan is scheduled to be paid off at the end of 30 years (or 360 months). Full amortization. A fully-amortizing loan has a remaining principal balance of zero after the last payment at the end of the term (i.e., the loan is “paid off”). Partial amortization and balloon loans. Loans with an amortization period longer that the term/maturity of the loan are only partially amortized and leave a balance remaining at the end of the loan term (i.e., a balloon); this remaining balance of the loan must be refinanced somehow. An example of such a partially amortizing loan is one with an amortization of 25 years while the loan matures in 5 years. Debt service as defined above is limited and does not account for the problem of permanent unamortized debt addressed in the next sections. Credit Lines. Credit lines are typically interest-only nonamortizing loans. The principal balance remains outstanding for a time, with pay-downs following collection of receivables and according to the borrower’s working capital cycle. Some simple non-exhaustive examples are detailed next. Seasonal Credit Lines. A credit line can include “seasonal” credit lines to certain borrowers such as agricultural concerns that can repay the loan when costs are recouped at the end of the growing season. A carryover balance, if remaining, may be difficult to roll into a fully-amortizing term loan if the borrower must wait until the next season to produce the cash flow to pay off the loan. This type of loan can carry considerable risk, and the lender must be familiar with the borrower and the market conditions that will generate the cash flow to repay the seasonal loan. Working Capital Lines. These are business lines of credit that typically not amortize, with interest paid on the outstanding balance. These credit lines are expected to be at a zero balance for at least 30 days throughout the year, referred to as a “clean up” period. The credit line is subject to renewal each year, based on acceptable credit criteria and loan

76 performance. The stated purpose of such lines of credit is for “working capital” which means in theory for short-term financing purposes; repayment is expected relatively quickly after the collection of receivables. Such external financing needs can naturally arise from periodic cash shortages, such as abnormal and/or temporary cash outflows that exceed the typical period of trading activities, or to take advantage of a larger-than-normal heavily discounted purchase of inventory. The credit line is cleaned up once receivables are collected and over the year the line should be at a zero balance much of the time. Exceptions. A reasonable exception to this short-term rule is a fast-growing firm that cannot collect its receivables fast enough to pay its vendors. A credit line as a form of interim financing helps supplement cash flow until sales growth eventually levels off and the receivables are collected. The cash received should provide sufficient cash to pay off the outstanding balance of the credit line. If a carryover debt remains, the balance may be rolled into a fully-amortizing term loan; the case where this does not occur is detailed next. Permanent non-amortizing debt. The crux of this appendix is a tendency for some “working capital” credit lines to become permanent, which means a long-term, interest-only, nonamortizing loan. Such loans may appear to be performing well from the standpoint of debt service coverage, as there are only interest payments due each month; provided that the borrower can find a way to “clean up” the line once a year for 30 days, such loans may continue to be rolled over indefinitely. However, here this is categorized as a “problem loan” because the accumulating principal balance is not being returned to the lender. Financial institutions may choose to roll over this debt from year to year or may attempt to term it out with a fully-amortizing loan that the borrower can service; if a term-out is not realistic, the debt may remain as permanent non-amortizing debt of the borrower, and the banks may not be compensated for the additional risk of this accumulating debt. Clarification. There are certain specialized financial units and entities that may lend against such

77 permanent non-amortizing debt. These include asset-based lending, secured lending, evergreen credit, working capital loans and A/R & inventory financing. This discussion excludes these types of lenders and loans because the lender is likely to be compensated for the additional risk they are taking; these types of loans would be expected to bear a higher interest rate and are closely monitored by making advances of credit only on evidence of adequate collateral. Non-Performing Loans and the Banking system. A significant number of these permanent non-amortizing lines of credit may remain in the portfolios of banks that are essentially nonperforming loans, but which are not being recognized or compensated as such. In a broader economic sense, this unamortized (unreturned) capital (principal) may represent a significant drain not only on the real return to banks but also a drag on economic vitality. To get a sense of the loss to the banking system of this “dead” capital, the author’s Interest Rate Analytics (2015) estimates a theoretical negative rate of return. Lax lending standards may undoubtedly be appealing to borrowers initially, but over the long-term, chronic over-lending (by the lender) and over-indebtedness (of the borrower) can engender poor financial and business management practices by the borrower and distort productive incentives. Unfortunately, prolonging this situation can lead to a crisis: Beyond a certain point, a bank attempting to recover its principal and increase its return by raising interest rates and/or by refinancing the excessive debt of a borrower may lead to outright bankruptcy of the firm because the borrower may no longer have an adequate primary source of repayment to service the debt. In severe enough cases where banks are ridden with portfolios of such non-performing debt, bankruptcy of the banks themselves and eventual takeover by the Federal Deposit Insurance Corporation (e.g., in the U.S.) may be a possibility.

78 Capital Investment Although often overlooked, a more in-depth understanding of the composition of the capital expenditures may be useful in evaluating firms, and especially for those that tend to be capitalintensive. Capex may often be treated as uniform with little if any distinction made between maintenance capex (roughly defined as a level of capex required for the business to maintain its position in the industry), and more technologically advanced capital expenditures intended to propel the firm towards a dominant position in the industry. Quality of Financial Information. The quality of the source financial information is crucial and can impact upon the results of an analysis of debt service capacity. Even audited financial statements which are considered of high quality may include estimates which may or may not fully and accurately reflect the firm’s financial condition. Consolidated financial statements remove related company transactions, correcting the sales, cost and liability data and are a critical element in establishing the quality of financial information. Combined financial statements are not a substitute for consolidated financial statements. Small business. For smaller firms with unaudited financial statements, reliability of the financial information is a potentially major issue. A reviewed financial statement may be the best quality available if made available. It is not unusual for smaller businesses to have their financial statements prepared according to a method of accounting other than Generally Accepted Accounting Principles (GAAP), or with significant elements lacking GAAP. This includes cash basis statements and tax returns which are not acceptable substitutes for GAAP-based financial statements. A financial analysis based on non-GAAP information may lead to erroneous conclusions about the company’s financial condition. Complexity and lack of transparency can present a major problem when analyzing smaller

79 businesses without consolidated statements and quality financial information. Related Entities. Major concerns arise from existence of significant outside entities related by the business owner(s) that are not applying for credit and therefore are not evaluated but whose financial condition can adversely affect the borrowing entity. For example, in the absence of consolidation, if the principals/owners of a business own commercial real estate (through an LLC or other entity) from which their business leases space, debt service on that real estate should be identified and included in some form of global cash flow analysis of the business borrower and related entities [in this case the commercial real estate-owning LLC, and perhaps other entities related by the owner(s)]. Other examples of financial quality problems include balance sheets that do not balance (i.e., total assets do not equal liabilities plus equity), missing financial information such as balance sheets or income statements, or relevant schedules. For small businesses, cash flow statements may not be available, and typically the credit-granting institution will prepare its own cash flow statements based on the balance sheet and income statement); particular attention should also be paid to financial statements with “unexplained” or “questionable” items including unexplained adjustments to retained earnings, particularly increases). Occasional misclassification of long-term nonoperating assets into operating cash flow can distort the figure upward for operating cash flow in years when those assets are liquidated. Accounting Methods and Preparers. For both large public companies and small businesses alike, a change or frequent changes of auditors, CPAs, or tax preparers could be a cause for concern, as well as changes in accounting methods -- proper comparisons with prior periods may no longer be possible. In the financial statements, interest expense may also be classified together with “financing costs” or “finance charges” although reporting methods may vary. From the perspective of the analyst, financial statements (including those of high quality) can present

80 interest figures and debt service data in such a way that a more time-consuming search through the Notes to the Consolidated Financial Statements may be required. Sufficient Financial History The quality of financial information is a prerequisite for credit analysis, but without an adequate historical data on the firm, identification of deteriorating fundamentals may be impossible. The business should have a substantial track record in its industry and consistency of financial performance can be identified with more historical data. Reliance on a good year or two of sales and profitability is not sufficient to be regarded as a basis for lending. Moreover, so-called accounting trickery can occur and may be difficult to detect over short periods, particular with a single year of data. However, over longer periods of time, a truer picture of the company’s financial condition may emerge. Given the constraints in obtaining extended historical data especially for smaller businesses, a minimum of three to five years of financial history is hoped for, although it is recognized that this represents a small sample. If more data are obtainable, as many as seven to ten years is preferable; although this may be impossible to obtain from smaller firms. Additional historical data can not only help identify the development of a long-term trend, but aids in comparisons between long-term trends and recent performance. For capital expenditures, an overall pattern may not be identifiable over the short-term and can in some industries can vary significantly from year to year. Independence This point cannot be overstated: Identifying whether a portion of revenues or expenses involves large related-party transactions/relationships that represent a lack of independence and control by the borrowing entity. Customer concentrations including reliance on revenue from government contracts also should be examined closely.

81 Retention of Owner Capital Substantial owner distributions/draws from small businesses are to be expected, but a possible source of credit risk is a pattern of a small and declining amount of capital being retained in the business. This could signify that owners are “cashing out” of the company and might be using debt to substitute their ownership interest. A possible warning sign of problems with the business includes a sudden decline in the principal(s) ownership percentage of the business without explanation. Loan Purpose An easily overlooked but fundamental aspect of the credit decision involves understanding the specific purpose for the loan. The purpose may not always be clear, or the stated purpose may differ from the actual intent of the borrower. A potential borrower may be reluctant to state that the loan request is to pay off existing debt (due to being asked out of another bank or financial institution), to cover losses of the firm, to give the owner(s) a big bonus, to buy a luxury car for the owner to use in business (as well as for personal use), or to use funds to funnel cash to an ailing related business entity (that is not the borrowing entity). The true purpose may be clarified by analysis of the company’s financial information (cash flow) to identify where significant outflows of cash may have occurred. Once the purpose can be properly identified, if the borrower is found to be creditworthy, the term and amortization of the loan can be better structured to match the company’s cash flows. Mismatching. A serious problem can occur in the granting and subsequent management of credit lines, as already discussed above as part of the issue of permanent nonamortizing loans. A credit line should typically only assist in short-term financing needs to cover a gap in receivables and payables, or unusually large purchases of inventory to take advantage of heavy discounts and the line would be expected to be clear much of the year. Some borrowers, however, may decide to make long-term purchases with the credit line that subsequently cannot be cleaned up. Simultaneous Lenders. Note that it may be possible for a borrower to find two banking institutions that allow

82 one credit line to pay off the other credit line to abide by the 30day “clean-up” period requirement. Depending on the quality and timing of the financial information it may be possible to omit information revealing a relationship with a second (or third) financial institution that permits such payoffs and pay-downs. In such a manner, the credit line can remain a (nearly) permanent, non-amortizing interest-only loan, alternating between financial institutions. Lending into Monetary Stimulus Certain external forces such as monetary policy can be a trap for firms choosing to borrow to take advantage of lower interest rates. A rough sketch is provided here with an example of an interest rate cut, which can stimulate additional activity that would otherwise not have occurred and set into motion a variety of events. Consumer demand may rise from lowered interest rates, both in the form of cheaper borrowing costs to finance the purchase of consumer goods, but also in the form of savings on existing debt service obligations such as mortgage repayments (assuming refinancing at a lower interest rate). The savings can increase disposable income and spur purchases that otherwise would not have been made in the absence of the lowered interest rates. Firms may benefit at least temporarily from a boost in their revenues and possibly net free cash flow. It is also possible that this consumer demand may contribute to rising consumer goods prices, inducing firms to supply the additional volume of purchases and to invest in capital expenditures (“capex”) to raise productive capacity for the heating-up market. Capex and Capital Goods Markets. If consumer goods purchases are rising as the expansion continues, the argument for adding more capital equipment than would have otherwise been purchased may be considered justified. Moreover, the decline in interest rates can stimulate additional borrowings by firms to purchase capital equipment to raise productive capacity for the heating-up market. Unfortunately, while managers may appear to be acting rationally, it may not be anticipated that the external signal generated by monetary policy (i.e., the lower interest rates) can bias decision-making and have long-lasting

83 negative consequences for firms. It should be clarified that change in monetary policy alone is not necessary to spur an industry-wide increase in capital expenditures. A recent technological advance may encourage firms industry-wide to increase capital investments utilizing the new technology. However, advantageous policy interest rates may further induce firms to borrow to invest in more equipment than would have otherwise been the case. Clustering and Cycles. If a single firm decides to increase its capital expenditures, the overall impact on markets is limited. However, in response to an external signal of monetary policy, multiple firms may tend to act similarly, collectively borrowing to boost their investments in capital equipment at approximately the same time, driving up the cost of the capital goods in the process, and boosting production. A major consequence of the clustering occurs later in the cycle. The collective debt-financed overinvestment can oversupply the market with the goods produced with the capital equipment (re: gluts). At some point, in the absence of sustained demand, the firms may suffer steep price declines of those goods, dissipation of revenues, profitability and eventual default and bankruptcy. Firms that rely heavily on capital expenditures may be more adversely affected than those in other less capital-intensive industries, suffering higher capex costs relative to revenues at the outset, and bearing the brunt of greater relative consumer goods price declines towards the end. If such policy-induced economic cycles develop, then it also may be possible to break down the phases of the cycle for an industry, whether expansionary, recessionary, or relatively “stable.” This also may aid in predicting periods during which industry-wide cash flows (NFCF) might be more likely to come under pressure, that will not be easily identifiable from the previous (growth) trends alone. Also see the section on Probability Theory below. A distinction must be drawn between businesses that have suffered from a debt-financed economic cycle as described above and poor management that is unrelated to this dynamic. In combination with industry analysis and comparison to peers in the

84 industry it also may be possible to pinpoint which firms lack effective decision-making capabilities entirely independent of any economic cycles. As noted in the section above on Capital Investment, the selection of capital equipment can be a crucial factor for survival in a competitive industry, requiring prescience and expertise. Poor capital investment decisions can lead to long-term weakness against the competition with negative impacts on the firm’s longterm debt service capacity. Actionable Steps? While difficult in practice, it could be argued that managers should attempt to give extra thought in taking on extra debt to acquire of capital equipment in response to an expansionary policy stimulus such as lowered interest rates. (Huerta de Soto 2012). Also see the discussion below on probability theory. Probability Theory and Normality Estimates for the arithmetic mean* of various financial measures are generally assumed to follow an approximately normal distribution. The central limit theorem provides a basis for the normality assumption, and roughly stated, relies upon the outcomes being generated additively and by numerous small independent processes. A major concern is whether the primary source of repayment and other variables tend to be influenced by a dominant external force, and whether some processes within economies may not be independent. This issue was touched upon in the context of economic theory above, where systemic monetary and financial forces may exert a significant impact on economic activity that can call into question the validity of normality assumptions. Therefore, clarifying the underlying probability distributions can be instructive in the analysis of debt service capacity. This topic has been researched in the author’s prior works including Financial Distortion Dynamics (2018) and Yield Distortion Dynamics (2020) and is covered in Part 2 of the main text of this book, as well.

85 *Note: Regarding the arithmetic mean, it should be noted that for some of the distributions that can plausibly describe the processes examined, there is no mean or variance. Cautionary Stress Testing: Dividend Coverage, 30 Years of Coca Cola to FY 2020 Summary *This report looks at thirty years of Coca Cola’s financial history to estimate its debt service and dividend coverage capacity. *Although companies may have proven track records as dividend growers, this analysis considers whether reliance on external sources of financing including refinancing plays a role. *In some cases, chronic cash flow shortfalls may be offset by the rolling over of accumulating debt. *The findings for KO are presented here for quick reference with links to videos and an industry report. Preliminary Results Coca Cola (KO) recently released its Fiscal Year 2020 audited financial statements. This report covers a 30-year observation period from FY 1991 to FY 2020. Metrics 1.Coverage capacity of financing obligations is estimated at 0.013 (range 0-1), with recent financing obligations at about 91.6% of revenues. 2.Exposure to a negative cash flow scenario is conservatively estimated at 0.078 depending on model assumptions (range 0-1). 3.Centrality of the cash flows is estimated at 13.2% of revenues. 4.Potential for coverage based on maximum acceptable exposures of 0.05 and 0.10 (MAE 0.05, MAE 0.10) are negative 7.7% to positive 3% of revenues, respectively. Capacity Tier and Ranking (estimate): 4.0 Note: Estimates may vary according to model assumptions, may be rounded and are not forecasts. See links and Summary of Definitions below for further detail. Individual Firm Analysis. An individual firm video summary corresponding to this report is here: https://youtu.be/5HRF3fPBkDo

Comparative Analysis (“Comps”) The 30-year period results are presented alongside those of each of the three decades to view the progression over time. The image below shows this comparison.

A video of this comparative analysis is available here: https://youtu.be/5dZaIpCie-0 For a beverage industry video and report to Interim 2020 which includes Fomento Económico Mexicano (FMX) and Diageo (DEO), see https://youtu.be/-4b-9zPgTtg and https://seekingalpha.com/instablog/471740-raoulkennedy/5506668-dividend-coverage-roundup-beverageindustry-interim-2020

87 Summary of Definitions and Interpretations. For more on the cautionary stress testing and dividend coverage capacity tiers/rankings methodologies, refer to Part 2. Additional Discussion Decades Breakdown. The historical record for KO suggests a near doubling of the financing obligations/revenues ratio each decade. In 2000 the figure stood at 23% of revenues (rounded); in 2010, the figure had risen to 40.2% (a 1.75x increase), and by 2020 to 91.6% (2.27x). The Coverage metric (metric 1) for the first decade (1991-00), second decade (2001-10), third decade (2011 to 2020) and the overall 30-year period are 0.025, 0.128, 0.007, and 0.013, respectively (range 0-1). The following is chart of the fitted distribution for this 30-year period (1991-2020). As of FY 2020, financing obligations of 91.6% are indicated by the upper arrow in the chart pointing to the right; using a limited definition of debt service detailed in the next section, the 2020 figure for financing obligations declines to 36.7% of revenues (the second lower arrow):

For further detail, see the individual firm and “comps” videos referenced above. Alternative Scenario. Limiting the assumption of debt service simply to current maturities of long-term debt (abbreviated as CPLTD) plus interest we arrive at a more optimistic scenario for the Coverage metric (metric 1). These reduced financing

88 obligations are shown in the following chart for the period 19912020.

Under this alternative scenario, the Coverage metric estimates improve to 0.18, 0.286, 0.023 and 0.045 for the first decade (199100), second decade (2001-10), third decade (2011 to 2020) and the overall 30-year period, respectively (range 0-1). Note that the drop in financing obligations/revenues in FY 2020 may be temporarily misleading due to a dip in current maturities that occurs in fiscal year 2020; in previous and subsequent years debt maturities are higher as seen in the following chart: note that the fiscal years progress from right (2017) to left (2024):

Barrick Gold (GOLD) Cautionary Stress Testing: Dividend Coverage, Barrick Gold Interim 2020 Summary *How capable are companies of covering dividends and debt service? *Heavy indebtedness and other factors may impair the capacity of firms to cover their dividend payments. *The findings for GOLD are reported here for quick reference with links to videos and industry reports. Background In August 2020 Warren Buffet acquired a stake in the company, followed by disclosure of sale of the entire stake in early 2021. The company has also been noted for its substantial efforts in reducing its debt load. An interesting finding is that as of late 2020 the financing obligations for the firm appear to be finely tuned to the centrality of net free cash flow (Metric 3) at 12% of revenues, corresponding to an estimate of 0.50 for the company’s coverage capacity (Metric 1). Preliminary Results

90 Metrics 1.Coverage capacity of financing obligations is estimated at 0.50 (range 0-1), with recent financing obligations at about 12% of revenues. 2.Exposure to a negative cash flow scenario is conservatively estimated at 0.065 to 0.10 (range 0-1) depending on model assumptions. 3.Centrality of the cash flows is estimated at 12% of revenues. 4.Potential for coverage based on maximum acceptable exposures of 0.05 and 0.10 (MAE 0.05, MAE 0.10) are from negative 15% to negative 1% of revenues, respectively. Capacity Tier and Ranking (estimate): 4.0 Note: Estimates may vary according to model assumptions, may be rounded and are not forecasts. See links and Summary of Definitions below for further detail. A video summary corresponding to this report is available here: https://youtu.be/kk4DVpvnFyk and screen cap here:

A6. Non-English Versions of Dividend Coverage Roundup: Petroleum Refiners 2911 (CVX, XOM) Interim 2020 French : Cautionary Global : Analyse Financière du Secteur Pétrolier, 2020,

91 Japanese: コーショナリーグローバル: 石油関連会社のファイナンシャル分析 2020 Spanish: Cautionary Global: Análisis Financiero del Sector Petrolero 2020.

Couverture des dividendes : Analyse financière du secteur pétrolier 2020 *This is a French language version of the Dividend Coverage Roundup report. * Les entreprises sont-elles capables de couvrir leurs obligations de financement ? *Ce rapport analyse trois sociétés pétrolières : Chevron, Equinor et Exxon Mobil ; des vidéos sont aussi disponibles pour avoir davantage de détails. *Les trois sociétés semblent avoir des difficultés de couvrir leurs obligations de financement ; XOM court un risque élevé de flux de trésorerie négatif. *Sans financement externe, on craint que ces sociétés ne puissent plus verser autant de dividendes. Les pétroliers analysés sont Chevron (CVX), Equinor (EQNR) et Exxon Mobil (XOM) ; (code de classification SIC 2911). CVX et XOM figurent sur la liste des « champions des dividendes » (voir ci-dessous). Résultats préliminaires La capacite des trois sociétés de couvrir leurs obligations de financement* actuelles est en doute (métrique 1 ; la gamme des estimations est de 0 à 100). Quant à l’exposition à un flux de trésorerie négatif (métrique 2), l’estimation de XOM est la plus élevée à 0,22, suivi par EQNR à 0,126. Métriques 3, 4, 5 : CVX dépasse les autres sociétés avec une estimation de centralité de 9,5% (métrique 3) et une estimation positive de 2% pour la métrique 5 (MAE 0,10). XOM et EQNR ont des flux de trésorerie négatifs à tous les paramètres spécifiés (MAE 0,05 et MAE 0,10).

92 * Toutefois, bien que CVX ait un chiffre de 2% (MAE 0,10) qui est supérieur à ceux des autres sociétés, son estimation de couverture (métrique 1) est relativement inférieure. Pourquoi cette contradiction apparente ? Cela s’explique par le fait que les métriques 4 et 5 se réfèrent à la queue gauche de la distribution ; le chiffre positif de CVX suggère que théoriquement il y a moins de résultats négatifs de flux de trésorerie par rapport aux autres sociétés. Toutefois, cet avantage est éclipsé par les obligations de financement excessives actuelles de CVX qui font partie de l’estimation de la métrique 1 (couverture). *Notes : (1) Le terme « obligation de financement » comprend les éléments suivants : a) service de la dette (paiements de capital et d’intérêts), et b) dividendes versés. (2) Métriques 4 et 5 : « MAE » représente théoriquement le montant maximum d’obligations qui puisse être couvert selon les paramètres spécifiés (MAE 0,05 et 0,10). (3) Bien que les codes de classification SIC des entreprises soient similaires ou identiques, leur comparabilité peut être limitée en raison de différences significatives entre les entreprises. L’image suivante est une capture d’écran d’une vidéo comparant les entreprises dans la série de Cautionary Global : Analyse financière : https://youtu.be/CtEreph21s8.

Des versions de cette vidéo sont également disponibles en anglais, espagnol et japonais. Pour les analyses individuelles de CVX et XOM, respectivement, voir https://youtu.be/kEuEGS0t-F0, et https://youtu.be/3d7c9-XqVKA. Références La liste des champions des dividendes (The Dividend Kings et Justin Law’s Articles). Veuillez consulter son rapport « Dividend Champions » publié mensuellement. D’autres publications sur les dividendes incluent Dividend Aristocrats (S & P) et Dividend Achievers (Nasdaq).

配当カバレッジ: 石油セクターの財務分析 2020 *This is a Japanese language version of the Dividend Coverage Roundup Report. *この分析では、企業が十分に元利と配当金をカバーできるかどうかを調べます. *このレポートでは、石油部門の三社を比較、分析します：三社とはシェブロン、エクイノア、エクソンモービルです. 詳細は、ビデオをご覧下さい.

94 *三社とも膨大なファイナンシング. オブリゲーションをカバーできるかが疑問；さらに、XOM のマイナスキャッシュフローのリスクは高いと推定. *三社とも、外部の資金調達がない限り元利と配当金の支払いは困難. 石油関係の三社は、シェブロン(CVX)、エクイノア(EQNR) 、エクソンモービル (XOM)です

(三社とも産業分類コー

ド SIC 2911「石油精製」です). CVX 社と XOM 社は、 Dividend Champions (配当チャンピオン) のリストに載っています(下記参照). 暫定的な結果メトリック＃１：三社ともファイナンシング.オブリゲーション（＊１参照）をカバーできる能力が欠如しているようです(;推定値は 0 から 100 です). メトリック＃2：マイナスキャッシュフローのリスクに関して、XOM 社の推定値は 0.22 でかなり高い数値で不安です. 次に EQNR 社の 0.126 でまだ高い数値です. メトリック＃３,＃４,＃５：CVX 社のセントラリテイー(中心)の推定値が 9.5%、そしてメトリック＃５の MAE 0.10 の 2%が、比較的に強力です. XOM と EQNR のメトリック＃４と＃５（＊２参照) の数値はマイナスなので、キャッシュフロー損失を被っているということになっています. CVX のメトリック＃５の高い数値の２％にもかかわらず、なぜ CVX のメトリック＃１（カバレッジ）の推定値が低いのか？理由は、分布の形です.

メトリック＃4 と＃5

95 が分布の左部分を表している(左部分は、マイナスキャッシュフローの数値が出る部分です). CVX の場合は、他の二社に比べて、マイナスキャッシュフローになる可能性が少ないことが推定されます. その代わりに CVX のメトリック＃1 (カバレッジ)の低い推定値が表しているのは、CVX が過剰なファイナンシング.オブリゲーションがあるということです. (*）ノート: (1) ファイナンシング. オブリゲーションとは、(a)実際の債務の返済額「元本資本および利払い」と(b)実際の配当金の支払い額で成り立つ. (2) メトリック＃４と＃５（指定されたパラメータ MAE 0.05 そして MAE 0.10) とは、企業が理論的にカバーできる最大のファイナンシング. オブリゲーション”額を推定の結果を表しています. (3) 産業分類コード（SIC コード）に関してですが、コードの数字は同じでも、それぞれ企業のビジネスモデルが若干異なる可能性があるので、SIC コードは正確ではない場合があります. 次の画像は、企業三社を比較するビデオのスクリーンショットです. コーショナリーグローバル:財務分析のビデオです.

日本語版は、 https://youtu.be/QkSaXByAEg4 でご覧

いただけます.

このビデオは、フランス語、英語、スペイン語版でもご視聴いただけます. CVX 社および XOM 社の個別の分析に関しては、 https://youtu.be/kEuEGS0t-F0 と https://youtu.be/3d7c9-XqVKA をご参照ください. 参照配当チャンピオンリスト(The Dividend Kings 及び Justin Law の記事). 最新のリストについては毎月のレポート Dividend Champions「配当金チャンピオン」を参照してください。その他のリストは、Dividend Aristocrats (S&P) 及び Dividend Achievers (Nasdaq)が含まれます。

Cobertura de Dividendos: Análisis Financiero del Sector Petrolero 2020 *This is a Spanish language version of the Dividend Coverage Roundup report.

97 *¿Son las empresas capaces de cubrir sus obligaciones de financiación? *Este informe analiza tres refinadores de petróleo: Chevron, Equinor y ExxonMobil; también se ofrecen vídeos para obtener detalles adicionales. * Las obligaciones de financiación parecen superar en gran medida la capacidad de todas las empresas, destaca la exposición negativa al flujo de caja de XOM. El pago de dividendos puede ser difícil sin financiación externa. Los refinadores de petróleo (código de clasificación SIC 2911 “Petroleum Refining”) analizados son Chevron (CVX), Equinor (EQNR) y Exxon Mobil (XOM). CVX y XOM son acciones de “campeónes de dividendos” según el listado de Dividend Champions (véase abajo). Resultados preliminares Todas las empresas parecen faltar capacidad de cubrir sus obligaciones de financiación* actuales (la métrica 1; las cifras son desde 0 hasta 100). En cuanto a la exposición a un flujo de caja negativo (métrica 2), el estimado de XOM es lo más alto de 0,22, seguido de EQNR de 0,126. CVX parece sobrepasar los demás con un estimado de centralidad del 9,5% y un estimado positivo del 2 % para la métrica 5 (MAE 0,10). Aparte de CVX, todas las empresas parecen sufrir pérdidas de flujo de efectivo en todos los parámetros especificados (MAE 0.05 y MAE 0.10) *. Sin embargo, aunque CVX tiene una cifra superior (2%) a los demás, su métrica de cobertura (métrica 1) es relativamente inferior. ¿Por qué? Esta contradicción aparente se explica porque las métricas 4 y 5 se refieren a la cola izquierda de la distribución, la cifra positiva de CVX sugiere que en teoría hay menos resultados de flujo de caja negativos posibles comparado con las otras empresas. Sin embargo, esta ventaja se ve eclipsada por las obligaciones de financiación actuales excesivas de CVX que se capta en la métrica 1 (cobertura). *Notas:

98 1) “Obligación de financiación” incluye los siguientes elementos: a) El servicio de la deuda (lo que incluye los pagos de capital principal e intereses), y b) Los dividendos pagados; 2) MAE representa teóricamente el máximo de obligaciones que se pueda cubrir según los parámetros especificados (0.05 y 0.10); 3) Aunque los códigos de clasificación SIC de algunas empresas pueden ser similares o idénticos, su comparabilidad puede ser limitada debido a diferencias significativas de las empresas. La imagen siguiente es una captura de pantalla de un vídeo comparando las empresas en la serie de Análisis Financiero: https://youtu.be/_xc-4RIG_8Y. Versiones de este video también están disponibles en francés https://youtu.be/CtEreph21s8, inglés https://youtu.be/lbRjb6007N8 y japonés https://youtu.be/QkSaXByAEg4.

Para los análisis individuales de CVX y XOM, respectivamente (en inglés), véase https://youtu.be/kEuEGS0t-F0 y https://youtu.be/3d7c9-XqVKA. Referencias La lista de Dividend Champions (The Dividend Kings y Justin Law's Articles). Para los listados más recientes, véase el informe de Law "dividend champions" del último mes. Otros listados incluyen los Dividend Aristocrats (S&P) y Dividend Achievers (Nasdaq).

99 ADDENDUM Conventions attempted in this and previous books 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 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 preferable to identify variations in viewpoints--practitioners can offer valuable insights from their first-hand experiences in

100 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 does not 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.

REFERENCES The references below are divided into two sections: Section 1. General and Section 2. Specialized (Epidemiology and Economy). The General references are for those selected primarily from the author’s previous reports or books, principally Yield Distortion Dynamics (2020), Financial Distortion Dynamics (2018), Economic Distortion Dynamics (2017), and Rate-of-Return Analytics (2016). Some of the references may not be directly relevant to the subject matter of this book; however, the impact of external factors on dividend coverage and debt service capacity, the relevance may become more apparent. The economic and financial system, political drivers of economies, legal and regulatory systems and most recently pandemics can all play a

101 significant role in the differential capacity of firms to pay dividends and service debt. T Section 2. The references for a separate* study on epidemiology and economy (Kennedy 2021) that also utilizes the cautionary tails framework are also appended after the general references. *This study is not included in this document.

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102 Barro, Robert J., "Are Government Bonds Net Wealth?", Journal of Political Economy (Vol. 82, no.6), University of Chicago, Nov/Dec 1974. Bar-Yam, Yaneer., Garcia, Rodolfo., Langlois-Meurinne, Jean., Kawakatsu Mari., “Preliminary Steps toward a Universal Economic Dynamics for Monetary and Fiscal Policy,” ArXiv; 1710.06285 (Dec 2017). Barua, Akrur., Buckley Patricia., “Rising corporate debt: Should we worry?” Issues by the Numbers in Deloitte Insights, April 2019 BIS, see Bank for International Settlements. Bohm-Bawerk, E. von., “The Function of Saving” Annals of the American Academy of Political and Social Science (May 1901). Borio, C. and Disyatat, P.., " Low Interest Rates and Secular Stagnation: Is Debt a Missing Link?" VOX, June 2014. Borio, Claudio., Erdem, Magdalena., Filardo, Andrew., Hofmann Boris., "The Cost of Deflations: A Historical Perspective," BIS Quarterly Review, March 2015. Bowles, Samuel., Kirman, Alan., Sethi, Rajiv., "Retrospectives: Friedrich Hayek and the Market Algorithm," Journal of Economic Perspectives (Vol. 31 No. 3), Summer, 2017 (pp. 215-30). Brainard, Lael., "Normalizing Monetary Policy When the Neutral Interest Rate Is Low," Presentation at the Stanford Institute for Economic Policy Research, Stanford, California, December 1, 2015. Brealey, Richard A., and Myers, Stewart., Principles of Corporate Finance, The McGraw-Hill Companies, Inc., 1996. Buffett, Mary., Clark David., The Warren Buffett Stock Portfolio, Simon & Shuster UK Ltd., 2013.

103 Cantillon, Richard., Essai sur la Nature du Commerce en Général (traduit de l’anglais), 1755. Chakravarti, Laha, and Roy, (1967). Handbook of Methods of Applied Statistics, Volume I, John Wiley and Sons, pp. 392-394. (re: Kolmogorov-Smirnov) Chi, Leisha., “Can Toshiba escape fate of corporate Japan’s zombie hordes?” BBC News Business, April 16, 2017. Christ, Carl., “A Simple Macroeconomic Model with a Government Budget Restraint,” Journal of Political Economy 76 (1) (Jan/Feb 1968), 53-67. Christ, Carl., “On Fiscal and Monetary Policies and the Government Budget Restraint,” The American Economic Review 69(3-5) (1979), 526-538. Cirillo, Pasquale (also see Taleb, Nassim Nicholas, et al., Statistical Consequences of Fat Tails) Cirillo, Pasquale., and Taleb, N.N., "On the Statistical Properties and Tail Risk of Violent Conflicts," Tail Risk Working Papers, ArXiv, Oct 19, 2015. Cirillo, Pasquale., Fontanari, Andrea., and Taleb, Nassim, Nicholas., “Gini estimation under infinite variance,”; Risk Research Program, ArXiv, July 9, 2017. Clarida, Richard., “Removing Accommodation” PIMCO Global Central Bank Focus, March 2017. Clayton, Jay., “Statement on Cryptocurrencies and Initial Coin Offerings,” Public Statement, U.S. Securities and Exchange Commission, December 11, 2017. Cobb, C. W.; Douglas, P. H. (1928). "A Theory of Production". American Economic Review 18 (Supplement): 139–165.

104 Cohen, Jerome B. and Zinbarg, Edward D.., Investment Analysis and Portfolio Management, Richard D. Irwin, Inc., Homewood, Illinois 1967. Cook, John. D., “Thick Tails,” John D. Cook Applied Mathematics Consulting (johndcookDOTcom), January 18, 2008. Cook, John D., “Obesity Index” John D. Cook Applied Mathematics Consulting (johndcookDOTcom), April 23, 2018. Cook, John D., “Six Sigma Events,” John D. Cook Applied Mathematics Consulting, (johndcookDOTcom), May 31, 2018. Cooke, Roger, et al., Fat-Tailed Distributions: Data, Diagnostics and Dependence, Wiley-ISTE, Vol. 1, 2014 Coyne, Christopher and Coyne, Rachel (Eds.)., Flaws and Ceilings: Price Controls and the Damage They Cause, Institute of Economic Affairs, 2015. Cramer, James J., Real Money, Simon & Schuster, 2009. Curdia, Vasco., “How Much Could Negative Rates Have Helped the Recovery?” FRBSF Economic Letters, Federal Reserve Bank of San Francisco, February 4, 2019. Damodaran, Aswath., Investment Valuation., John Wiley & Sons, Inc., 1996, (3rd Ed.) 2012. De La Calle, L.S., (1544) Instrucción de Mercaderes ... (see Grice-Hutchinson 1952) (Re: School of Salamanca) Devajaran, Shantayanan, and Anthony C. Fisher., “Hotelling’s Economics of Exhaustible Resources: Fifty Years Later,” Journal of Economic Literature Vol. XIX (March 1981), 65-73. De Waal, Alex (see Waal; re: political marketplace)

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106

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Section 2. Specialized References Epidemiology and Economy Study Note: The study is not included in this document but is referenced below under Kennedy (2021).

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122 Henrik Ullrich (11), Makoto Ohashi (12), Stefano Scoglio (13), Marjolein Doesburg-van Kleffens (14), Dorothea Gilbert (15), Rainer Klement (16), Ruth Schruefer (17), Berber W. Pieksma (18), Jan Bonte (19), Bruno H. Dalle Carbonare (20), Kevin P. Corbett (21), Ulrike Kämmerer (22). Bristow, Michael., “Coronavirus: Japan doctors warn of health system ‘breakdown’ as cases surge, BBC News, April 18, 2020. Britton, Tom., Ball, Frank., Trapman, Pieter., "A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2," Science, Vol 359 issue 6505, August 14, 2020, pp 846-849. Bullard, Jared, et al., “Predicting Infectious Severe Acute Respiratory Syndrome Coronavirus 2 From Diagnostic Samples,” Clinical Infectious Diseases, ciaa638, //doi.org/10.1093/cid/ciaa638 *Other authors: Kerry Dust, Duane Funk, James E Strong, David Alexander, Lauren Garnett, Carl Boodman, Alexander Bello, Adam Hedley, Zachary Schiffman, Kaylie Doan, Nathalie Bastien, Yan Li, Paul G Van Caeseele, Guillaume Poliquin. Chunichi News, "5.9% of Antibodies Positive," (in Japanese), Nagoya, Japan, April 30, 2020. Cirillo, P., Taleb, N.N. Tail risk of contagious diseases. Nat. Phys. 16, 606–613 (2020). Collins, James J., Wong, Felix., "Evidence that coronavirus superspreading is fat-tailed,” Proceedings of the National Academy of Sciences (PNAS), Nov 2020, 202018490; DOI: 10.1073/pnas.2018490117. Corman, Victor M., Drosten, Christian., et al. "Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR." Euro Surveillance 2020;25(3): pii=2000045. //doi.org/10.2807/1560-

123 7917.ES.2020.25.3.2000045. *Et al: Landt Olfert, Kaiser Marco, Molenkamp Richard, Meijer Adam, Chu Daniel KW, Bleicker Tobias, Brünink Sebastian, Schneider Julia, Schmidt Marie Luisa, Mulders Daphne GJC, Haagmans Bart L, van der Veer Bas, van den Brink Sharon, Wijsman Lisa, Goderski Gabriel, Romette Jean-Louis, Ellis Joanna, Zambon Maria, Peiris Malik, Goossens Herman, Reusken Chantal, Koopmans Marion PG, Cripps, Sally., Ioannidis, John., Tanner, Martin., "Forecasting for COVID-19 has failed," Forecasting News, International Institute of Forecasters, June 14th, 2020. ENE-COVID study Spain (see Pollán, et al.) Friston, Karl (see Sayers June 4, 2020) Lam, T. T. et al. Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins. Nature DOI://doi.org/10.1038/s41586020-2169-0 (2020). Karolinska Institute (see Sekine, Takuya., Perez-Potti, André., et al., also Schraer, Rachel) Kennedy, Raoul., “Cautionary Stress Testing: Epidemiology and Economy (Covid-19),” Seeking Alpha, (Parts 1-13), February 2020 to April 2021, and revised monographs including the oneyear study at issuu.com/raoulkennedy. Martensen, Chris., “Covid-19: A Result of Lab Manipulation?” Peak Prosperity, /youtu.be/eD3ztjqYGbg, May 13, 2020 Martensen, Chris., "Good News! Up To 50% Of Us May Be 'PreImmune' To Covid-19," Peak Prosperity online (//youtu.be/G7TWiweluwA), August 18, 2020. Miyazawa, Hiroyuki., (see Abema Times, May 2, 2020) Ogiwara, Kazuki., “Coronavirus Disease (COVID-19) Situation Report in Japan,” Toyo Keizai Online, 2020.

124 Pollán, Marina, M.D.., Pérez-Gómez, Beatriz, M.D., et al., “Prevalence of SARS-CoV-2 in Spain (ENE-COVID): A nationwide, population-based seroepidemiological study,” The Lancet, July 2, 2020 DOI://doi.org/10.1016/S01406736(20)31483-5 Sayers, Freddie., “German Virologist: Covid-19 Less Deadly than Feared,” The Post (Unherd), May 5, 2020 (//unherd.com/thepost/german-virologist-finds-covid-fatalityrate-of-0-24-0-36/) Sayers, Freddie, “Karl Friston: Up to 80% not even susceptible to Covid-19,” The Post (Unherd), June 4, 2020. (//unherd.com/2020/06/karl-friston-up-to-80-not-evensusceptible-to-covid-19/) Schraer, Rachel., “Coronavirus: Immunity may be more widespread than tests suggest,” BBC News Health, July 1, 2020. (re: Karolinska Institute) Streeck, Hendrik., (see Sayers, May 5, 2020). Sekine, Takuya., Perez-Potti, André., et al. “Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19,” bioRxiv, online 29 June 2020 (doi: 10.1101/2020.06.29.174888). Taleb, Nassim., "On single point forecasts for fat tailed variables," Forecasting News, International Institute of Forecasters, June 14th, 2020. Taleb, Nassim., Statistical Consequences of Fat Tails: Real World: Preasymptotics, Epistemology, and Applications, STEM Academic Press, 2020. Note on references: Online references may exclude “https.” Some dates may vary slightly according to time zone differences. Studies cited may not be peer-reviewed; authors may be listed in the references according to the order shown in the study.

125

Additional Notes, Resources, and Acknowledgements (Cumulative) Because the last section of references is cumulative, many of the references may not have been cited in this book. However, they may have some tangential relevance to certain topics covered or other related works by the author. 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 systemdynamics.org, the Taipei Institute of Banking and Finance, and the University of California, Davis Cooperative Extension and College of Agricultural and Environmental Sciences.

Cautionary Tails Framework and Applications: Dividends and Debt Service