Asset Management Industry: Financials 2019

2019 Asset Management Industry: Financials

lamb quantitative research 6 January 2021

Š2021 Lamb Quantitative Research

Asset Management Industry: Financials 2019

Published by Lamb Quantitative Research

Edition: 6 January 2021 (1)

Description: Zurich, Switzerland, Lamb Quantitative Research, 2021- | Annual | Began with issue for 2019 | Asset Management Industry is a survey by Lamb Quantitative Research published annually covering the global asset management industry up to the previous year end. | This report covers the dynamics of aggregate industry financial data.

Contact Alistair Lamb contact@lambqr.com www.lambqr.com

Recommended citation: Lamb Quantitative Research. 2021. Asset Management Industry: Financials 2019. Zurich, Switzerland.

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Table of Contents Overview.............................................................................................................................................. 4 Purpose............................................................................................................................................ 4 Data ................................................................................................................................................. 4 Related publications ........................................................................................................................ 4 Executive Summary ............................................................................................................................. 5 Industry Income................................................................................................................................... 6 Aggregate Industry Income ............................................................................................................. 6 Industry Income Margins ................................................................................................................ 7 Does AuM Margin Increase with Scale? .......................................................................................... 9 Per Company AuM Margin ............................................................................................................ 10 Top Asset Managers by AuM and Revenue................................................................................... 11 Net Profit Margin........................................................................................................................... 13 End-To-End Efficiency .................................................................................................................... 13 Does Profit Margin Increase with AuM? ....................................................................................... 14 Industry Balance Sheet ...................................................................................................................... 16 Aggregate Industry Equity and Capital .......................................................................................... 16 Aggregate Industry Balance Sheet Metrics ................................................................................... 16 Per Company RoE and RoC ............................................................................................................ 18 Change in Assets Under Management .............................................................................................. 20 Industry Aggregate ........................................................................................................................ 20 Relative Contribution to Change in AuM ...................................................................................... 21 Explaining Subscription Levels....................................................................................................... 22 Per Company Net Inflows .............................................................................................................. 23 Private Equity Asset Managers .......................................................................................................... 25 Investment Return............................................................................................................................. 27 Industry Aggregate Investment Return ......................................................................................... 27 Investment Return Per Manager ................................................................................................... 27 Sharpe Ratio .................................................................................................................................. 28 Explaining Investment Return ....................................................................................................... 29 Where Do Asset Managers Invest? ............................................................................................... 29 Do Asset Managers Report Poor Investment Performance? ........................................................ 31 Appendix............................................................................................................................................ 32 Regression Results ......................................................................................................................... 32

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Table of Figures Table 1: Aggregate Industry Income Statement (USD bn) ...................................................................... 6 Figure 1: Revenue, EBT, Net Profit .......................................................................................................... 7 Table 2: Volume Weighted Industry Income Margins............................................................................. 8 Table 3: Equally Weighted Industry Income Margins ............................................................................. 8 Figure 2: Aggregate AuM Margin ............................................................................................................ 9 Figure 3: Aggregate Gross and Net Profit Margin ................................................................................... 9 Table 4: OLS Regression: AuM Margin ~ AuM......................................................................................... 9 Figure 4: AuM Margin by AuM .............................................................................................................. 10 Figure 5: Change (absolute) in AuM Margin from 2012 to 2019 Per Asset Manager ........................... 11 Figure 6: Per Company AuM Margin ..................................................................................................... 11 Table 5: Revenue and AuM in 2018 for Largest Asset Managers.......................................................... 12 Figure 7: Top 10 Largest by Revenue .................................................................................................... 12 Figure 8: AuM Margin of Top 10 Largest Asset Managers by AuM....................................................... 13 Figure 9: NPM per Asset Manager: Mean 2012-2019 and 2019 ........................................................... 13 Figure 10: Earnings Efficiency per AuM ................................................................................................. 14 Table 6: OLS Regression Result: Gross Profit Margin ~ AuM ................................................................ 14 Figure 11: AuM to GPM (All Years) ........................................................................................................ 15 Figure 12: Total Industry Equity and Capital ......................................................................................... 16 Table 7: Equally Weighted Balance Sheet Metrics ................................................................................ 16 Table 8: Volume Weighted Balance Sheet Metrics ............................................................................... 17 Figure 13: RoE and RoC ......................................................................................................................... 18 Figure 14: Per Company RoE ................................................................................................................. 18 Figure 15: Per Company RoC ................................................................................................................. 19 Table 9: Aggregate Industry AuM Change by Component .................................................................... 20 Figure 16: Change in Absolute AuM by Component ............................................................................. 20 Table 10: Components of yoy AuM Growth .......................................................................................... 21 Figure 17: Relative Change in AuM by Component .............................................................................. 21 Figure 18: Cumulative Contribution to AuM Growth ............................................................................ 22 Table 11: OLS Regression: Relative Net Inflows ~ Savings + ACWI ....................................................... 22 Figure 19: Relative Net Flows to Equity Index Change .......................................................................... 23 Table 12: Private Equity Peer Group Comparison ................................................................................. 25 Table 13: Components of AuM Change Private Equity ......................................................................... 25 Table 14: Aggregate Industry Investment Return ................................................................................. 27 Figure 20: Mean Investment Return Per Asset Manager ...................................................................... 28 Figure 21: Investment Return Sharpe Ratio Per Asset Manager........................................................... 29 Table 15: OLS Regression Result: Investment Return ~ ACWI .............................................................. 29 Table 16: Explaining Country Level Returns .......................................................................................... 30

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Overview Purpose This report analyzes and summarizes the asset management industry’s income and balance sheet metrics on an aggregate and per company level.

The reader will obtain an overview of the industry’s accounting metrics, how these have evolved between 2012 to 2019, and will see trends over time from a dataset that requires non-trivial effort to obtain. In particular, the report provides summaries of aggregated industry income and balance sheet, components in AuM changes, accounting margins, investment returns, investment performance, and private equity asset managers. Moreover, the report provides new insights through regression models to explain market return, returns to scale, net inflows, and performance reporting.

The audience for this report is CEOs, CFOs, financial analysts, financial journalists, industry consultants, advisory consultants, regulators, AM sales teams, AM clients and potential clients, private investors, portfolio managers, academics, and other interested persons.

Data Descriptions of the underlying datasets used in all reports are available in Asset Management Industry Review: Reference 2019 [1].

The data used in this report comes from these datasets described in [1]: (1) Detailed Data (2) AuM (3) Directory (4) certain external datasets such as equity index price levels, GDP and savings rates.

The Detailed Data set contains extended, time-series data on a subset of the asset managers from the Directory and AuM data sets. The Detailed Data consists almost exclusively of listed companies because detailed income, balance sheet data and AuM breakdown data is not publicly available for privately held companies. As a result, the classification of asset managers in the Detailed Data subset is skewed towards (a) listed (versus privately owned) managers (b) those with larger AuM compared to median AuM, and (c) those with United States as country of beneficial ownership. This subset of asset managers includes 78 out of the largest 500 managers (15%), but covers USD 56.5tr of the USD 107.2tr AuM held by the largest 500 asset managers (52%).

Industry aggregates are presented in this report typically as “equally weighted” (or “ew”) or “volume weighted” (or “vw”). Equally weighted statistics take the mean values for a metric regardless of the magnitude of contribution by each manager; volume weighted statistics sum the numerating variables before dividing by the sum of the denominating variables hence the relative volume of the contribution weights the mean.

Note that AuM change results from three factors each year: (a) net inflows (outflows) (b) acquisitions (disposals) (c) market appreciation (depreciation). Market appreciation includes foreign exchange effects.

Related publications This report is the second in a set of four reports: 1.

2.

3.

4.

Asset Management Industry: Top Asset Managers 2019 [2] Asset Management Industry: Structure and Growth 2019 [3] Asset Management Industry: Financials 2019 [4] (this report) Asset Management Industry: Financial Analysis 2019 [5]

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Executive Summary We identify the following trends for the industry from 2012-2019: 

        



We confirm the aggregate industry has exhibited almost zero profit growth from 2012 to 2019 (“the period”): 4AuM rose 6.4% p.a., revenue 2.7%, costs 5.2%, EBT 1.7%, tax expense 7.0% p.a., and net profit 0.7% p.a.

AuM margin has decreased at a rate of -3.3% p.a. and -2.9% p.a. on volume weighted and equally weighted views, respectively.

Gross profit margin (GPM) and net profit margin (NPM) have both remained stable over the period at 30% and 23% on an equally weighted basis.

Private equity asset managers have outperformed their traditional peers in AuM growth, margin, and have paid lower tax rates, although margin return on AuM is equivalent.

Investment returns have been the largest but most volatile contributor to AuM growth over net inflows and acquisitions.

Global net inflows can be well explained in a model with global savings and global equity market returns.

Regression analysis indicates both AuM margin and profit margins decrease with higher AuM. Asset manager investment returns have underperformed both S&P 500 and MSCI ACWI equity indices on a risk-adjusted basis.

Profit from one unit of AuM is unchanged from 2012 to 2019.

While equity to total capital ratio has decreased over time, both return on capital (RoC) and return on equity (RoE) appear to have peaked in 2014. Total capital has grown roughly 1.5 times faster than equity from 2012 to 2019.

Managers are less likely to report investment performance if they have underperformed.

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Industry Income Aggregate Industry Income Table 1 shows the estimated aggregate income statement for the total asset management industry from 2012 to 2019.

The key finding is that revenue has not kept pace with AuM, costs, or taxes, resulting in stagnant net profit over the period. Only a decrease in the tax rate starting in 2017 allowed the industry to exhibit overall profit growth for the period. The tax rate decrease can be attributed to the corporate income tax rate decrease in the United States in 2017 (The United States accounts for around 55% of global AuM).

Table 1: Aggregate Industry Income Statement (USD bn) Year AuM Revenue Costs Profit Before Tax 2012 70730 277 152 103 2013 77779 308 163 121 2014 81446 303 174 102 2015 80813 298 191 81 2016 85093 281 195 58 2017 99060 321 189 104 2018 95166 324 203 92 2019 109277 334 218 116 CAGR

6.4%

2.7%

5.2%

1.7%

Tax Expense 12 15 15 15 -4 20 15 19

Net Profit 90 106 88 69 64 86 84 95

7.0%

0.7%

The values are estimated by collecting accounting figures (revenue, tax, profit) for approximately 75 asset managers whose aggregate AuM represents between 47% (2012) and 52% (2019) of total industry AuM and extrapolating to cover the missing 53% (2012) to 48% (2019) of asset managers.

Due to rounding there may be slight discrepancies which means Revenue less Costs does not exactly equal Profit Before Tax, and Profit Before Tax less Tax Expense does not exactly equal Net Profit.

Costs include operating costs as well as interest, amortization, and depreciation.

Figure 1 shows industry aggregate Revenue, Profit Before Tax and Net Profit from Table 1 on the lefthand axis. The gap between Revenue and Profit Before Tax represents Costs, the gap between Profit Before Tax and Net Profit represents Tax Expense. The total industry AuM is included on the righthand axis for reference.

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Figure 1: Revenue, EBT, Net Profit

Industry Income Margins Table 2 and Table 3 track key industry margins over time calculated on a volume weighted and an equally weighted basis, respectively. Figure 2 plots the industry’s equally weighted and volume weighted AuM Margin from 2012-2018. Figure 3 shows the industry’s gross and net profit margins from 2012-2019. We highlight some observations from the results.

We confirm the conclusions of other studies and anecdotal evidence suggesting AuM Margins are decreasing: the downward trend in AuM Margin is statistically significant on volume weighted (p<0.01) and on equally weighted basis (p<0.01). The effect is more pronounced in years when the market rose because end year AuM will be higher than year average AuM. Costs to revenue ratio and gross profit margin have remained stable over time in both equally weighted and volume weighted views.

The slight decrease in tax rate in 2018 and 2019 has allowed net profit margin to remain stable in both volume and equally weighted view.

Due to the relatively higher representation of smaller managers in the equally weighted than volume weighted results, we conclude that smaller managers enjoy higher AuM margin, but the margin is decreasing at roughly the same relative rate per year (-3% p.a.). All other statistics are largely equivalent except that margins for smaller managers are more volatile.

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Table 2: Volume Weighted Industry Income Margins Year AuM Costs / Gross Tax Margin Revenue Profit Rate Margin 2012 0.34% 72% 28% 34% 2013 0.33% 67% 33% 26% 2014 0.32% 67% 33% 24% 2015 0.33% 71% 29% 27% 2016 0.30% 71% 29% 26% 2017 0.29% 66% 34% 29% 2018 0.31% 70% 30% 22% 2019 0.26% 70% 30% 21% CAGR

-3.3%

-0.7%

1.8%

-3.2%

Net Profit Margin 20% 24% 24% 22% 22% 24% 24% 24% 3.7%

We remove the Private Equity firms (Brookfield, KKR, BX, Apollo, Oaktree, Carlyle, Partners Group) as their margins typically appear as outliers against those of traditional asset managers. See section Private Equity Asset Managers for detailed comparisons between the two groups.

Revenues include performance fees, however performance fees typically constitute a small portion of revenue. An outlier is OZMâ&#x20AC;&#x2122;s revenue which has dropped from its peak in 2013 when a substantial amount of revenue was generated by achieving performance targets, but whose revenue has declined since.

Pzena Investment is removed from the Tax Rate calculation as its effective tax rate in 2016 is 6400%. For Vanguard we assume GPM, Tax Rate and NPM are zero.

Table 3: Equally Weighted Industry Income Margins Year AuM Costs / Gross Tax Margin Revenue Profit Rate Margin 2012 0.62% 71.47% 28.53% 26.33% 2013 0.62% 65.76% 34.24% 27.02% 2014 0.59% 64.08% 35.92% 25.32% 2015 0.61% 66.45% 33.55% 26.75% 2016 0.52% 68.89% 31.11% 25.17% 2017 0.54% 61.46% 38.54% 27.95% 2018 0.55% 72.90% 27.10% 21.25% 2019 0.51% 67.65% 32.35% 7.40% CAGR

-2.9%

-0.8%

1.8%

-16.6%

Net Profit Margin 20.16% 25.18% 26.83% 24.28% 23.50% 25.84% 20.10% 25.44% 3.4%

In some circumstances, due to tax refunds or tax penalties, particularly from prior years, tax rates are above 100% or below 0%. These have been kept in the calculation of Tax Rate.

GAM Investments had NPM of -308% in 2018. This is far below the next lowest NPM (-65.1%) of Man Group plc. If this manager is excluded from the above figures, the GPM and NPM are 33.9% and 26.5% respectively, which are in line with 2017 values.

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Figure 2: Aggregate AuM Margin

Figure 3: Aggregate Gross and Net Profit Margin

Does AuM Margin Increase with Scale?

We see that the industry exhibits decreasing returns to scale (DRS) instead of the expected increasing returns to scale (IRS) with respect to AuM Margin (Revenue / AuM). We find a highly statistically significant decrease in AuM Margin as AuM grows.

Table 4: OLS Regression: AuM Margin ~ AuM Dependent Variable: Log10 AuM Margin R-squared: 0.388 No. Observations: 531 F-statistic: 335.3 Independent Variable (constant) Log10 AuM

Co-efficient -1.7357 -0.2747

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Std. Err. 0.036 0.015

Adj. R-squared: 0.387 Prob (F-statistic): 0.00000 P>|t| 0.000 0.000

Asset Management Industry: Financials

[0.025, 0.975] -1.807, -1.664 -0.304, -0.245

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Figure 4: AuM Margin by AuM

We use the logarithm of AuM margin to avoid curvature in this variable which would decrease the validity of the regression.

For reference, exp10-3.50 = 0.03%, exp10-3.00 = 0.05%, exp10-2.50 = 0.08%, exp10-2.00 = 0.14%, exp10-1.50 = 0.22% We also controlled for the Year variable as we know that AuM Margin and AuM vary with the year, however find this variable to have no statistical significance in this regression.

The regression line is influenced by managers with very small AuM. If these are excluded, the line of best fit has a higher constant and steeper decline.

Per Company AuM Margin Figure 5 shows the absolute (not relative) difference in AuM margin from 2012 to 2019. The equally weighted mean is -10bps for the period. Figure 6 shows the mean and 2019 per company AuM margin.

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Figure 5: Change (absolute) in AuM Margin from 2012 to 2019 Per Asset Manager

Private Equity and outliers are excluded.

Figure 6: Per Company AuM Margin

Private equity managers are excluded.

Top Asset Managers by AuM and Revenue In this section, we highlight that AuM does not translate equally into revenue, even for the largest asset managers. Among the largest managers, it is important to note that Vanguard follows a mutual ownership model, so maintains its revenues in line with its costs and returning profits to mutual fund

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holders through lower expense ratios. The remaining managers are public or private managers looking to optimize profits over time.

Table 5 ranks the largest 20 managers in the sample set by revenue in 2019 next to their corresponding AuM rank in 2019. Figure 7 shows the top 10 largest managers by revenue (essentially showing the top 10 ranks by revenue), while Figure 8 shows the AuM Margin of the top 10 largest managers by AuM. Table 5: Revenue and AuM in 2018 for Largest Asset Managers Asset Manager Revenue 2019 AuM 2019 Rank by (USD bn) (USD bn) Revenue Blackrock 14.5 7430 1 Fidelity 12.5 3195 2 Allianz 8.0 2546 3 JP Morgan 7.3 2364 4 Vanguard 6.2 6200 5 Invesco 6.1 1226 6 Franklin Resources 5.8 698 7 T Rowe Price 5.6 1207 8 Goldman Sachs 5.4 1859 9 Natixis 3.9 1049 10 Morgan Stanley 3.8 552 11 Prudential Financial 3.6 1308 12 Amundi 3.0 1856 13 Ameriprise 2.9 494 14 Legg Mason 2.9 804 15 DWS 2.6 861 16 Wells Fargo 2.6 696 17 BNY Mellon 2.5 1910 18 AllianceBernstein 2.5 623 19 Schroders 2.4 575 20

Rank by AuM 1 3 5 6 2 13 21 14 8 15 28 11 9 31 19 18 22 7 23 27

Rank Difference 0 1 2 2 -3 7 14 6 -1 5 17 -1 -4 17 4 2 5 -11 4 7

Figure 7: Top 10 Largest by Revenue

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Figure 8: AuM Margin of Top 10 Largest Asset Managers by AuM

Volume weighted mean is for all managers in the Detailed dataset (77 in 2019), not just for the top 10.

Net Profit Margin Figure 9 shows the NPM in 2019 and the managerâ&#x20AC;&#x2122;s mean NPM from 2012 to 2019 for all managers in the dataset ordered by mean NPM. Figure 9: NPM per Asset Manager: Mean 2012-2019 and 2019

Data for VCTR and Liontrust starts in 2015; Generali, Janus Henderson, Standard Life Aberdeen in 2017; DWS and M&G in 2018.

Excludes outliers Oaktree and Vanguard.

End-To-End Efficiency We define end-to-end efficiency as the profit (gross or net) achieved per unit of AuM. Figure 10 shows the average end-to-end efficiency of the managers in the dataset from 2012 to 2019. lamb quantitative research

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We see that volume weighted gross earnings efficiency has been stable over the period, while volume weighted net profit efficiency has increased slightly (although not sufficiently to identify this statistically as a trend yet). Equally weighted gross and net measures have been mostly higher than volume weighted measures over the period, indicating that the smaller managers achieved higher profits for their AuM over the period.

Figure 10: Earnings Efficiency per AuM

We calculate that the gross profit from one unit of AuM in 2019 was almost six times (5.9) higher than that of the traditional managers, although this had decreased from much higher levels in 2012.

Does Profit Margin Increase with AuM? There is an assumption that the industry exhibits increasing returns to scale (IRS). The following analysis indicates the opposite. That is, gross profit margin is negatively correlated to AuM at a high level of significance (p-value). The result has a very low level of explanatory power (R-squared) however. Table 6 summarizes the results and Figure 11 provides a graphical representation of the relationship.

An interpretation of the results is that two companies with equal size would generate more gross profit than if the two companies were combined. However, we caution against taking the results too literally, because while the statistically significance of the result is high, its predictive power is low due to the high variance in the GPM. If anything, the result is helpful to advise that margins will unlikely increase as AuM grows, rather stay constant, or decrease slightly.

In this analysis we prefer gross profit margin (earnings before tax divided by revenue) over net profit margin (earnings after tax divided by revenue), otherwise we are implicitly also asking if tax rate decreases with AuM. This is to some extent true because the largest managers are in the United States, hence have in recent years had the lowest tax rates.

Table 6: OLS Regression Result: Gross Profit Margin ~ AuM Dependent Variable: GPM R-squared: 0.032 No. Observations: 504 F-statistic: 16.38 Independent Variable (constant) Log10 AuM

Co-efficient 0.4341 -0.0419

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Std. Err. 0.025 0.010

Adj. R-squared: 0.030 Prob (F-statistic): 0.000006 P>|t| 0.000 0.000

Asset Management Industry: Financials

[0.025, 0.975] 0.385, 0.483 -0.062, -0.022 14

Figure 11: AuM to GPM (All Years)

We controlled the regression for year (as we know there is a trend in AuM) but we find no statistical significance.

Outliers dropped from the analysis were those with GPM > .9 or GPM < .1 for a specific year, Vanguard, and pure Private Equity managers.

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Industry Balance Sheet Aggregate Industry Equity and Capital Figure 12 shows an estimate for the industry’s total demand for equity and capital over the time period. Capital has increased at roughly 1.5 times (9.6% / 6.5%) faster than equity according to Table 8 which has led to a decrease in the ratio of equity to capital.

Figure 12: Total Industry Equity and Capital

Aggregate Industry Balance Sheet Metrics We look at industry return on capital (RoC), return on equity (RoE), changes in equity and capital and ratio between equity and capital. Table 7 and Table 8 show balance sheet metrics calculated as equally weighted and volume weighted, respectively.

We see the equity to capital ratio for the industry has dropped from 2012 to 2019 from 68% to 52% on an equally weighted basis, and from 46% to 44% on a volume weighted basis. Correspondingly, capital growth has outpaced equity growth: 5.8% vs 3.8% on an equally weighted basis and 9.6% vs 6.5% on a volume weighted basis. We confirm there is a statistically significant correlation between equity to capital ratio and interest rates. (See Asset Management Industry: Financial Analysis 2019 [5]).

A disappointing conclusion for the industry is that while equity to capital ratios have fallen (i.e., higher leverage) and corporate bond interest rates have decreased, RoE has not grown. Said differently, falling equity to capital ratios have barely hidden declining RoE. Table 7: Equally Weighted Balance Sheet Metrics Year Return Return ∆yoy ∆yoy on on Capital Equity Capital Equity 2012 10.8% 20.5% 2013 12.8% 22.0% 6.3% 11.6% 2014 15.2% 22.5% 4.0% 2.0% 2015 13.8% 21.0% 1.1% -1.7% 2016 10.7% 18.0% 4.4% 0.9% 2017 12.1% 19.3% 12.3% 9.6% lamb quantitative research

Equity / Capital 67.9% 58.2% 58.0% 48.6% 50.1% 50.0%

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2018 2019

9.1% 10.9%

15.0% 19.6%

0.6% 11.7%

-0.3% 4.6%

51.6% 51.1%

Mean

11.9%

19.8%

5.8%

3.8%

54.4%

If GAM Investments is dropped from the dataset, ROE in 2018 increases from 15.9% to 19.5%. With the result that there is a less discernible downward trend over the whole period. Δyoy Capital and Δyoy Equity are based on the change in median capital and equity values per year to avoid the effect of some underlying values switching sign and near zero denominators.

The calculations are based on balance sheet data for these asset managers which are stand-alone (non-collocated) and where balance sheet information is available (typically publicly listed companies): AB, AMG, ANIMA, ASHM, Amundi, Apollo, Azimut, BX, Bank of New York, Bellevue, Blackrock, Brookfield, CI Financial, CNS, Diamond Hill Capital, Eaton Vance, FII, Franklin Resources, GAMI, Invesco, JHG, Jupiter, LAZ, LM, Magellan, Man, Manning & Napier, PTM, SDR.L, Silvercrest, State Street, T Rowe Price, VCTR, Virtus IP, WETF, WHG. For ROC calculations, APAM and OZM were also included.

It is meaningless to provide equally weighted profit growth since one company’s abnormally low profit in one year leads to “exceptional” relative profit growth such as a multiple such as 20 or 50 in the next year. Including these high multiples (of which there are a few), distorts an equally weighted mean calculation.

Table 8: Volume Weighted Balance Sheet Metrics Year Return Return ∆yoy ∆yoy on on Capital Equity Capital Equity 2012 4.4% 7.9% 2013 6.0% 9.7% 6.3% 6.3% 2014 5.4% 9.1% 11.6% 5.0% 2015 4.4% 7.6% -1.6% -3.4% 2016 4.3% 7.3% 1.3% 4.5% 2017 3.3% 9.0% 19.5% 12.2% 2018 3.7% 7.4% 11.1% 5.7% 2019 4.0% 8.2% 19.4% 15.3% Mean

4.4%

8.3%

9.6%

6.5%

Equity / Capital 46.1% 46.8% 44.8% 43.7% 45.8% 43.9% 42.4% 40.9% 44.3%

Error! Reference source not found. visualizes RoC and RoE for equally weighted and volume weighted series over the time period. While volume weighted RoE and RoC has remained around the same levels from 2012-2019, equally weighted RoE and RoC have fallen from their peaks in 2014.

This suggests that RoE has fallen for smaller managers, although their levels are still higher than those of larger managers. The variance in the series means we cannot statistically conclude there is a falling trend in the series, although future data may support that hypothesis.

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Figure 13: RoE and RoC

Per Company RoE and RoC Figure 14 and Figure 15 respectively show RoE and RoC per asset manager. Each figure shows values for 2019 and the mean over 2012-2019 and is sorted by the mean. It is important to re-iterate that the companies in the sample are all exchange listed, standalone managers (other non-balance sheet statistics include collocated managers), include private equity managers, cover a small subset of the overall asset manager population (38 in 2012, 44 in 2019), but cover an outsized portion of the total industryâ&#x20AC;&#x2122;s AuM (20% in 2012, 24.5% in 2019).

Figure 14: Per Company RoE

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Figure 15: Per Company RoC

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Change in Assets Under Management Industry Aggregate Table 9 estimates the change in aggregate industry AuM per year by component: net inflows, acquisitions, and investment return. Figure 16 plots the values from the table.

Section Investment looks in more detail at the investment return.

Table 9: Aggregate Industry AuM Change by Component Year Starting Net Inflows Acquisitions Investment AuM (USD (USD bn) (USD bn) Return (USD bn) bn) 2012 65737 1500 133 3360 2013 70730 1885 238 4927 2014 77779 1711 -365 2321 2015 81446 1226 295 -2154 2016 80813 1601 816 1863 2017 85093 3490 1198 9279 2018 99060 773 1406 -6073 2019 95166 4681 955 8476

Ending AuM (USD bn) 70730 77779 81446 80813 85093 99060 95166 109277

â&#x2C6;&#x2020;yoy Investment Return 5.0% 6.9% 3.0% -2.6% 2.3% 10.6% -6.1% 8.7%

Due to rounding there may be slight discrepancies which means the Ending AuM does not exactly equal the sum of Starting AuM, Net Inflows, Acquisitions, and Investment Return.

Investment Return may be overstated in positive return and inflow years and understated in negative return and outflow years because it does not account for return on inflows received and acquisitions during the year. Unfortunately, Average AuM is not published for all managers in the data set.

Net annual acquisitions must logically sum to zero for the industry overall. As we see overall positive acquisitions in the subset which has an overrepresentation of large, listed managers, we infer these managers are net acquirers. In 2014, JP Morgan sold its retirement plan services with AuM USD 167 bn to Great-West Financial; as Great-West Financial is not in the subset used to estimate AuM changes, this appears as a net disposal.

Investment Return includes changes due to FX translation. We note that in 2018 only 17% revenue for the subset was earned by managers with beneficial ownership outside of United States.

Figure 16: Change in Absolute AuM by Component

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Relative Contribution to Change in AuM Table 10 and Figure 17 show the contribution to the industry total year-on-year AuM change by component. Figure 18 shows the cumulative change by component.

Net inflows have remained positive and relatively constant over the period, although vary with equity market returns (see section Explaining Subscription Levels).

Acquisitions play only a small role (0.7% p.a.) in overall change to AuM, and logically the net change due to acquisitions over the asset manager universe must net to zero. The positive value for our sample indicates the asset managers in the sample (typically larger, listed managers) were net AuM acquirers.

Investment return is most the volatile but also highest contributor to year-on-year change to AuM. Table 10: Components of yoy AuM Growth Year Contribution Contribution to ∆yoy to ∆yoy AuM by Net AuM by Inflows Acquisitions 2012 2013 2014 2015 2016 2017 2018 2019

2.3% 2.7% 2.2% 1.5% 2.0% 4.1% 0.8% 4.9%

0.2% 0.3% -0.5% 0.4% 1.0% 1.4% 1.4% 1.0%

Contribution to ∆yoy AuM by Investment Return 5.1% 7.0% 3.0% -2.6% 2.3% 10.9% -6.1% 8.9%

Total ∆yoy AuM

Mean

2.6%

0.7%

3.6%

6.8%

St. Dev.

1.4%

0.7%

5.7%

7.0%

7.6% 10.0% 4.7% -0.8% 5.3% 16.4% -3.9% 14.8%

Figure 17: Relative Change in AuM by Component

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Figure 18: Cumulative Contribution to AuM Growth

Note: The lines in this plot are not stacked.

Explaining Subscription Levels We look to explain the level of subscriptions (relative to starting year AuM). We identify a two-factor linear model using global savings and a global equity index, MSCI All Country World Index (ACWI) as having a very high explanatory power (93%).

Savings represents global savings flowing to asset managers via regulatory pension schemes, and the ACWI Equity Index represents increased investor interest as equity markets rise. Savings is calculated from global GDP growth (World Bank: NY.GDP.MKTP.KD.ZG) multiplied by the gross savings percentage of GDP (World Bank: NY.GNS.ICTR.ZS). Contributions from savings are assumed to be mostly independent to changes in the market (see Table 10).

Table 11: OLS Regression: Relative Net Inflows ~ Savings + ACWI Dependent Variable: Net Inflows Relative R-squared: 0.949 to AuM No. Observations: 8 F-statistic: 55.33 Independent Variable Savings ACWI Equity Index

Co-efficient 0.0060 0.0852

Std. Err. 0.001 0.019

Adj. R-squared: 0.931 Prob (F-statistic): 0.000136 P>|t| 0.002 0.004

[0.025, 0.975] 0.003, 0.009 0.038, 0.132

We also attempted to model the relationship with an equity index (S&P 500, MSCI ACWI), then added a constant term to represent a constant savings rate. In the last iteration we proposed that the savings rate can be better proxied by the GDP less consumption than a constant factor. Other models, particularly non-linear, may better explain changes in subscriptions, but cannot be tested reliably with the limited data set (8 points) as can be seen from Figure 19.

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Figure 19: Relative Net Flows to Equity Index Change

Furthermore, we tested for lagged correlations between equity indices and the relative level of net inflows. We find no statistical result that suggests investors front run the market (i.e., increase investments when market is down, decrease investments when market is up). Nor do we do find a statistically significant 1-year lagged correlation between the equity market index and subscription level in the subsequent year (i.e., investors increase investments the year after the market returns a positive year, and decrease investment in the year following a bad year). We suspect were there a leading or lagging correlation, that is would be observable at a finer lag time such as 1 month, or that market volatility would be a factor.

Per Company Net Inflows The following tables show top and bottom asset managers ranked according to Absolute Net Inflows from 2012 to 2019 and Mean Net Inflows Relative to AuM. We note that the league tables are only based on managers from the Detailed dataset.

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Asset Manager

Asset Manager

Blackrock Vanguard JP Morgan Goldman Sachs Amundi Legal & General Blackstone Northern Trust Prudential Financial Apollo

Net Inflows 2012-2019 (USD bn) 2356 1990 556 477 459 354 203 203 185 169

NOAH Magellan Partners Group Azimut Brookfield Liontrust Apollo WisdomTree Blackstone Vanguard

Net Inflows per AuM - Mean 2012-2019 37.1% 18.2% 13.3% 12.4% 12.3% 10.4% 9.7% 9.6% 7.1% 6.5%

BrightSphere Manning & Napier Carlyle Allianz Janus Henderson Federated Hermes Ameriprise Legg Mason State Street Standard Life

-36 -45 -47 -48 -49 -66 -93 -93 -125 -198

Ameriprise Ashmore Carlyle Platinum Janus Henderson GAM Investments GAMCO Westwood Standard Life Manning & Napier

-2.5% -2.6% -3.7% -3.7% -4.5% -6.0% -7.0% -9.9% -10.1% -19.4%

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Private Equity Asset Managers The dedicated private equity managers do not fit with the traditional asset manager model, most notably because they hold investments on balance sheet where positions are not marked-to-market daily. This and their above average performance warrant a dedicated inspection.

On the standard accounting metrics below, the dedicated private equity asset managers have outperformed their traditional asset manager peers: their gross and net profit margins are fatter, they pay much less tax, and their AuM has grown faster due to higher net inflows.

Table 12: Private Equity Peer Group Comparison Company Mean GPM Mean NPM Mean Tax 2012-2019 2012-2019 Rate 20122019

47% 55% n/a 65% 76%

43% 50% n/a 63% 68%

-1% 16% 5% 2% 8%

15% 17% 5% 15% 25%

Mean Inflows / AuM 20122019 7% 10% 0% 1% 12%

68%

61%

11%

14%

13%

0%

30%

28%

13%

4%

-4%

6%

PE Group e.w. Mean

57%

52%

8%

14%

6%

3%

Non-PE e.w.

Mean

33%

24%

23%

8%

1%

4%

Blackstone Apollo Oaktree KKR Brookfield Partners Group Carlyle

CAGR AuM Growth 2012-2019

Mean market return on AuM 20122019 6% -2% 3% 7% 0%

Rounded to nearest percentile.

Values for Oaktree only to 2018 as Brookfield took it over in 2019.

CAGR AuM Growth 2012-2019 is higher than inflows plus market return due to net positive acquisition.

The volume weighted view for the private equity asset managers in the sample shows a similar picture: AuM in Private Equity has grown far faster (13.4%) than for the total industry less dedicated PE managers (5.3%). While slightly higher market returns have contributed to this growth, far higher levels of net inflows (6.2% versus 2.0% for the total industry) have predominately driven this growth.

Table 13: Components of AuM Change Private Equity Year Private Equity AuM ΔAuM Net Acq. / ΔMark (USD Inflows AuM et / bn) / AuM AuM 2012 761 2013 932 22.4% 8.2% 7.1% 4.8% 2014 989 6.2% -0.1% 1.0% 3.2% 2015 1072 8.4% 6.9% 0.6% 0.2% 2016 1133 5.7% 2.7% -0.5% 2.7% lamb quantitative research

Total Excluding Private Equity Net Acq. / ΔMarke Inflows AuM t / AuM ΔAuM / AuM 12.4% 5.7% -0.5% 5.2%

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2.6% 2.3% 1.4% 2.0%

0.2% -0.5% 0.4% 1.1%

9.9% 2.9% -3.1% 2.5% 25

2017 2018 2019 Mean

1370 1504 1729

20.9% 9.8% 15.0%

10.7% 8.7% 7.2%

1.8% 1.7% 9.5%

7.0% -1.5% 4.5%

23.5% -3.1% 19.2%

4.2% 0.6% 5.0%

1.5% 1.4% 0.8%

11.8% -8.5% 12.4%

12.6%

6.3%

3.0%

3.0%

8.9%

2.6%

0.7%

4.0%

Acq. / AuM = Net acquisitions through M&A divided by AuM for previous year Î&#x201D;AuM for non-private equity companies is not adjusted for increased representation over time relative to total global AuM (48% in 2012, 52% in 2019).

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Investment Return Industry Aggregate Investment Return In this and the next sections, we look at the increase in AuM by asset managers in our subset of managers through investments in markets. Table 14 provides the investment return per year, mean, and Sharpe ratio on a volume weighted basis compared to the MSCI World and S&P 500 indices.

Table 14: Aggregate Industry Investment Return Year Investment Investment Investment Return: Return: Return: S&P Aggregate MSCI World 500 Asset Manager 2012 5.0% 16.1% 16.0% 2013 6.9% 22.8% 32.4% 2014 3.0% -4.2% 13.7% 2015 -2.6% -2.4% 1.4% 2016 2.3% 7.9% 12.0% 2017 10.6% 24.0% 21.8% 2018 -6.1% -9.4% -4.8% 2019 8.7% 27.3% 33.1% Mean

3.5%

10.3%

15.7%

Sharpe Ratio

0.45

0.60

1.06

Outperformance Asset Manager Return to MSCI World

Outperformance Asset Manager Return to S&P 500

-11.1% -15.9% 7.1% -0.3% -5.6% -13.4% 3.3% -18.6%

-11.0% -25.5% -10.7% -4.0% -9.7% -11.2% -1.2% -24.4%

-6.8%

-12.2%

The risk-free rate used in the Sharpe Ratio calculation is the year-end US Federal Reserve Target Rate. This means the US Fed Reserve Target Rate is used to proxy the risk-free rate in each country which is may be a strong assumption, particularly for emerging market economies.

Investment Return Mean and Sharpe Ratio values differ from those in Figure 20 and Figure 21 because the date range covers 2012-2019 in this section and only 2013-2019 in those figures.

Investment Return Per Manager Figure 20 shows the mean investment return per asset manager for the period 2012-2019. Only 1 of 70 (1.4%) were able to outperform the S&P 500, 9 out of 70 (12.9%) were able to outperform the MSCI ACWI. The volume weighted and equally weighted means lay at 5.3% and 4.8%, respectively.

We would not expect asset manager investment returns to match equity market performance over the longer term rather to exhibit returns similar to balanced and multi-asset strategies, mirroring the returns of the mix of asset classes they manage.

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Figure 20: Mean Investment Return Per Asset Manager

Investment Return is calculated as the AuM increase due to market movements divided by the start of year AuM. Using this methodology overstates (understates) returns in years where net inflows for the year are positive (negative) and/or market returns are positive (negative). As most asset managers experience positive inflows, the mean returns for asset managers is probably lower than we calculate here.

Sharpe Ratio Comparing asset manager investment returns solely with equity benchmarks is â&#x20AC;&#x2DC;unfairâ&#x20AC;&#x2122; because most managers will manage lower return asset classes in addition to equities, and certain client mandates and funds are limited according to investment guidelines. A more appropriate benchmark may be a balanced strategy with the same mix of asset classes as the asset manager. As only some managers publish such data, we use the Sharpe ratio, a risk-adjusted benchmark, in this analysis instead. The results are however similar. Only 4 out of 70 (5.7%) managers beat the S&P 500 while 31 out of 70 (44.3%) beat the MSCI ACWI.

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Figure 21: Investment Return Sharpe Ratio Per Asset Manager

The US Federal Reserve Target Rate at year end is used as the risk-free rate for the calculation of Sharpe Ratio.

Explaining Investment Return We look to explain the investment return of the aggregate asset manager subset (which covers 52% of total global AuM in 2019).

We find that the investment return can be explained to a high degree of accuracy (86%) by changes in the MSCI World Equity Index. Investment return is calculated as change in AuM less acquisitions and subscriptions, but including underlying market (beta) return and alpha generation. The regression co-efficient, the change in investment return with respect to change in the index, is 0.35.

For a manager with only equity strategies, we would expect this to be close to 1.00, however assets under management typically include cash, bonds, real-estate, and other asset classes which have lower equity market beta.

Table 15: OLS Regression Result: Investment Return ~ ACWI Dependent Variable: Investment Return R-squared: 0.880 No. Observations: 8 F-statistic: 51.46 Independent Variable World Equity Index

Co-efficient 0.3506

Std. Err. 0.049

Adj. R-squared: 0.863 Prob (F-statistic): 0.000182 P>|t| 0.000

[0.025, 0.975] 0.235, 0.466

Where Do Asset Managers Invest?

We look to add to the results from the previous section by explaining the returns at a country level.

In the results in Table 16 we report the index that best explains investment returns of the asset managers of a particular country. Per country, we test between: lamb quantitative research

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(1) (2) (3) (4)

Home market equity stock index in local currency terms home market equity stock index in USD terms World (MSCI World Equity Index) US S&P 500 equity index.

Additionally, we look to see if the highest explanatory value (adjusted r-squared) is achieved with the addition of a constant or assuming a zero-intercept. The inclusion of a constant in the regression makes sense if one assumes AuM increases due to a fixed savings rate plus investment performance (typically due to equity market index change) of the asset manager, as the case appears according to Table 10. It is therefore surprising that only 7 out of 32 countries or regions had a constant factor in their growth of AuM. We find that AuM change per country is not always correlated most highly with local equity market.

Instead, certain countries exhibit strongest correlation either with World or United States equity market indices.

We warn, the results in this section should only be considered indicative, particularly due to the modeling assumption that a managerâ&#x20AC;&#x2122;s Country of Beneficial Ownership, the country of tax residency of largest group of proportional shareholders as far as known or can be estimated, is a fair representation of the country where it managers all of its assets. Clearly most managers manage assets in at least one country. Thus, the country classification used here can only be considered an estimate for a particular manager.

Statistically significant values are highlighted green, non-significant in red. Additionally, there are two probably non-sensical values highlighted red: (1) negative correlation co-efficient of less than -1 for South Africa (2) a savings rate of less than 0 for Portugal. The South African AuM was heavily impacted by M&A and currency depreciation.

Table 16: Explaining Country Level Returns Country Most Likely CoInvestment efficient Location and Currency (Local, USD) United States Home Local 0.55 Japan World 0.46 World World 0.56 Sweden World 0.70 Switzerland World 0.59 South Korea Home USD 0.52 Europe World 0.53 Asia Home USD 0.05 Thailand Home USD 0.53 Canada United States 0.36 Spain World 0.51 Colombia World 0.79 Finland United States 0.65 France World 0.59 Germany World 0.52 lamb quantitative research

Explanatory p-value Power

Constant Savings

96% 94% 91% 89% 87% 75% 81% 3% 75% 79% 77% 77% 76% 76% 74%

No No No No No Yes No Yes Yes No No No No No No

0.00003 0.00008 0.000253 0.000483 0.000672 0.001159 0.002377 0.002533 0.002973 0.003234 0.003982 0.004262 0.00456 0.004873 0.006214

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Constant Savings Rate

0.10 0.13 0.09

30

China United Kingdom Singapore Austria Brazil Denmark South Africa India Hong Kong Norway Australia Netherlands Portugal Italy Russia Belgium Ireland

Home Local World Home Local World Home Local World Home Local Home Local Home Local World World World Europe World Home USD World United States

0.18 0.52 0.30 0.59 0.01 0.64 -1.09 1.09 0.01 0.45 0.35 0.33 0.63 0.43 1.20 0.65 3.20

11% 73% 21% 70% 0% 64% 58% 55% 0% 54% 47% 43% 61% 41% 38% 32% 3%

0.006777 0.007215 0.008539 0.009129 0.014486 0.017024 0.028727 0.034588 0.035207 0.038645 0.061106 0.079819 0.085015 0.085679 0.10342 0.145522 0.659599

Yes No Yes No Yes No No No Yes No No No Yes No No No No

0.25 0.11 0.12

0.10

-0.06

Do Asset Managers Report Poor Investment Performance?

We find that among those asset managers who report investment performance, there is a strong tendency to only report investment performance when the manager is outperforming the benchmark in more than 50% of its AuM. More worrying is that so few managers report performance at all, forcing us to conclude that most asset managers underperform their benchmarks.

By comparing the number of managers who provide investment performance figures less than 50% in a company report versus the total number of investment performance figures provided, we test whether managers tend to report only above average investment performance and do not report below average performance. The investment performance figures are provided as a proportion of AuM which outperform the benchmark. We assume that in a â&#x20AC;&#x153;fairâ&#x20AC;? distribution, 50% of managers would have above 50%, and 50% below 50%. We see however that far fewer than 50% of managers report investment performance less than 50%. The test is performed using a binomial distribution, which leads us to reject the null hypothesis at a very high statistical significance.

Investment Performance relative to Benchmark for last

Proportion of companies reporting investment performance less than 50%

Number of companies reporting performance

1 year 3 years 5 years

23% 11% 8%

106 112 99

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Probability of this result occurring randomly (pvalue) < 0.000001 < 0.000001 < 0.000001

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Appendix Regression Results Does AuM Margin Increase with Scale?

OLS Regression Results ============================================================================== Dep. Variable: aummgn_l R-squared: 0.388 Model: OLS Adj. R-squared: 0.387 Method: Least Squares F-statistic: 335.3 Date: Sat, 02 Jan 2021 Prob (F-statistic): 2.22e-58 Time: 14:01:34 Log-Likelihood: -17.713 No. Observations: 531 AIC: 39.43 Df Residuals: 529 BIC: 47.98 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const -1.7357 0.036 -47.642 0.000 -1.807 -1.664 aum -0.2747 0.015 -18.312 0.000 -0.304 -0.245 ============================================================================== Omnibus: 50.799 Durbin-Watson: 2.239 Prob(Omnibus): 0.000 Jarque-Bera (JB): 83.685 Skew: -0.634 Prob(JB): 6.73e-19 Kurtosis: 4.475 Cond. No. 9.41 ==============================================================================

Does Profit Margin Increase with AuM? OLS Regression Results ============================================================================== Dep. Variable: gpm R-squared: 0.032 Model: OLS Adj. R-squared: 0.030 Method: Least Squares F-statistic: 16.38 Date: Sat, 02 Jan 2021 Prob (F-statistic): 6.00e-05 Time: 14:11:54 Log-Likelihood: 197.74 No. Observations: 504 AIC: -391.5 Df Residuals: 502 BIC: -383.0 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 0.4341 0.025 17.544 0.000 0.385 0.483 aum -0.0419 0.010 -4.047 0.000 -0.062 -0.022 ============================================================================== Omnibus: 30.737 Durbin-Watson: 2.132 Prob(Omnibus): 0.000 Jarque-Bera (JB): 40.033 Skew: 0.516 Prob(JB): 2.03e-09 Kurtosis: 3.917 Cond. No. 9.42 ==============================================================================

Explaining Subscription Levels OLS Regression Results ================================================================================== Dep. Variable: subs_rel R-squared (uncentered): 0.948 Model: OLS Adj. R-squared (uncentered): 0.931 Method: Least Squares F-statistic: 55.24 Date: Sat, 02 Jan 2021 Prob (F-statistic): 0.000137

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Time: 15:35:54 Log-Likelihood: 28.970 No. Observations: 8 AIC: -53.94 Df Residuals: 6 BIC: -53.78 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------savings 0.0242 0.005 5.177 0.002 0.013 0.036 idx 0.0857 0.019 4.459 0.004 0.039 0.133 ============================================================================== Omnibus: 0.785 Durbin-Watson: 1.741 Prob(Omnibus): 0.675 Jarque-Bera (JB): 0.563 Skew: 0.528 Prob(JB): 0.754 Kurtosis: 2.241 Cond. No. 5.15 ============================================================================== Notes: [1] R² is computed without centering (uncentered) since the model does not contain a constant.

[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8 warnings.warn("kurtosistest only valid for n>=20 ... continuing "

Explaining Investment Return OLS Regression Results ================================================================================== Dep. Variable: inv_ret R-squared (uncentered): 0.880 Model: OLS Adj. R-squared (uncentered): 0.863 Method: Least Squares F-statistic: 51.46 Date: Sat, 02 Jan 2021 Prob (F-statistic): 0.000182 Time: 15:55:47 Log-Likelihood: 19.257 No. Observations: 8 AIC: -36.51 Df Residuals: 7 BIC: -36.43 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------World 0.3506 0.049 7.174 0.000 0.235 0.466 ============================================================================== Omnibus: 3.592 Durbin-Watson: 2.807 Prob(Omnibus): 0.166 Jarque-Bera (JB): 1.356 Skew: 1.007 Prob(JB): 0.508 Kurtosis: 2.885 Cond. No. 1.00 ============================================================================== Notes: [1] R² is computed without centering (uncentered) since the model does not contain a constant.

[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8 warnings.warn("kurtosistest only valid for n>=20 ... continuing "

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