Asset Management Industry: Financial Analysis 2019

2019 Asset Management Industry: Financial Analysis

IAL

Lamb Quantitative Research 24 January 2021

Š2021 Lamb Quantitative Research

Asset Management Industry: Financial Analysis 2019

Published by Lamb Quantitative Research

Edition: 24 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: Financial Analysis 2019. Zurich, Switzerland.

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Table of Contents Overview.............................................................................................................................................. 1 Purpose............................................................................................................................................ 1 Data ................................................................................................................................................. 1 Related publications ........................................................................................................................ 1 Executive Summary ............................................................................................................................. 3 Asset Manager Financial Performance................................................................................................ 4 Earnings Growth Relative to Market ............................................................................................... 4 Earnings Growth Per Company ....................................................................................................... 6 Does Gearing Improve EPS Growth? ............................................................................................... 7 Do Interest Rates Impact Balance Sheet Capital Mix? .................................................................... 8 Is Operational Efficiency Predictive of Future Returns? ................................................................. 9 Market Pricing of Asset Managers .................................................................................................... 12 Earnings Yield per Company .......................................................................................................... 12 Yields and Equity Risk Premium for Asset Managers .................................................................... 12 Price to Book ................................................................................................................................. 13 Forward Market Predictions Using Price-to-Book Ratio ............................................................... 14 Sensitivity of Asset Manager P/E Ratio to Interest Rates ............................................................. 15 Does the Market Reward Past or Future Performance? ............................................................... 16 Does Investment Performance Influence Net Inflows? ................................................................ 19 Company Summary ........................................................................................................................... 21 Appendix............................................................................................................................................ 25 Regression Results ......................................................................................................................... 25

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Table of Figures Table 1: Volume Weighted yoy Change in Profit, EPS, Dividend, Price, and Equity Market ................... 4 Figure 1: Volume Weighted Cumulative Change in Profit, EPS, Dividend, Price, and Equity Market ..... 5 Table 2: Equal Weighted yoy Change in Profit, EPS, Dividend, Price, and Equity Market ...................... 5 Figure 2: Equally Weighted Cumulative Change in Profit, EPS, Dividend, Price, and Equity Market ...... 6 Figure 3: Returns per Asset Manager ...................................................................................................... 6 Table 3: OLS Regression Result: Current Year EPS Growth ~ Gearing..................................................... 7 Table 4: OLS Regression Result: Next Year EPS Growth ~ Gearing ......................................................... 7 Figure 4: EPS Growth to Gearing ............................................................................................................. 8 Figure 5: Capital Mix and Interest Rates ................................................................................................. 9 Table 5: GPM Autocorrelation for n Years Forward ................................................................................ 9 Figure 6: GPM Autocorrelation ............................................................................................................. 11 Figure 7: Earnings Yield ......................................................................................................................... 12 Figure 8: Earnings and Dividend Yields.................................................................................................. 13 Figure 9: Aggregate Price to Book Ratio................................................................................................ 13 Figure 10: Price to Book Ratio per Company......................................................................................... 14 Table 6: OLS Regression Result: 1 Year Forward Aggregate Asset Manager Price ~ Aggregate P/B Ratio ............................................................................................................................................................... 14 Figure 11: MSCI ACWI vs. Aggregate Asset Manager P/B ..................................................................... 15 Table 7: OLS Regression Result: Earnings Yield ~ Federal Reserve Funding Rate ................................. 16 Table 8: OLS Regression Result: Earnings Yield ~ High Yield Corporate Bond Rate .............................. 16 Table 9: OLS Regression Result: Earnings Yield ~ Investment Grade Corporate Bond Rate ................. 16 Table 10: Regression Results of Future Mean EPS Growth to Adjusted P/E ......................................... 17 Figure 12: 2-year Forward Mean EPS Growth vs Adjusted P/E ............................................................. 18 Figure 13: Mean Net Profit Margin from 2016 to 2018 vs. P/E 2019 ................................................... 19 Figure 14: Net Inflows vs Investment Performance .............................................................................. 20 Table 11: Per Company Financial Metrics ............................................................................................. 21

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Overview Purpose This report covers multiple financial analysis aspects of the asset management industry, particularly exploring the performance of asset managers and their market pricing on an individual and aggregate level.

The reader will obtain an overview of the industry’s annual performance returns, how these have evolved from 2012 to 2019, and what factors are significant in their evolution. The report specifically provides time series of the industry’s return levels, individual company fundamental market metrics for listed asset managers, it looks at regressions covering prices and profit margin, gearing and earnings growth, balance sheet mix and interest rates, persistence of profit margin over time, priceearnings ratio and historical profit margin, and, ambitiously, looks at predicting forward price movements.

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 “equal 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] Asset Management Industry: Financial Analysis 2019 [5] (this report)

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

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Aggregate asset manager dividends have grown in line with asset manager stock prices and the MSCI ACWI while EPS and profit have grown faster for larger managers and slower for smaller managers.

Asset manager operational efficiency, defined as gross profit margin, exhibits significant persistence over time, suggesting that efficient asset managers stay efficient.

When gearing exceeds a certain level, EPS growth declines. Up to this level, no correlation between leverage and EPS growth exists.

Aggregate equity to capital ratio has consistently decreased between 2012 to 2019. While no significant correlation with interest rates was found, we suspect this correlates with a decline in U.S. investment grade bond yields over a longer time frame.

Price-to-book ratios of listed asset managers have remained stable over time and exhibit a significant degree of mean reversion such that 1-year forward aggregate asset manager returns are predicted based on the aggregate asset manager P/B. The mean equity risk premium and dividend yield of listed asset managers exhibits strong mean-reversion.

We find strong linear correlations when using U.S. interest rates as explanatory variables for asset manager earnings yield. Asset manager price movements are roughly twice as sensitive to bond yield movements as the equity market: a 1% increase in investment grade bond yield leads to a 1.25% increase in manager earnings yield, which is far higher than the broader equity market.

There is a statistically significant correlation between 1-year investment performance and next year relative net inflows.

We struggle to find any predictive variables for listed asset manager P/E beyond interest rates, although identify 1-year forward EPS growth as significant.

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Asset Manager Financial Performance Earnings Growth Relative to Market We compare the change in profit, EPS, and dividends with the change in industry market price and the equity market. Volume weighted and equal weighted tables and charts are presented in Table 1, Figure 1, Table 2, and Figure 2 respectively.

Criteria for inclusion in the volume weighted subset were that the company had complete data from 2012 to 2019, was publicly listed, and was not classified as a private equity asset manager. This excluded some managers that were listed after 2012. No outlier values are removed.

From the volume weighted statistics, EPS growth (13.7% p.a.) outpaced profit growth (11.4% p.a.), and more decisively dividend (8.7% p.a.) and share price growth (8.8% p.a.). Equity raisings, share buybacks, and non-controlling interests cause profit and EPS growth to diverge. Dividend growth (8.7% p.a.) has lagged EPS growth (13.7% p.a.), but has been in-line with the aggregate change in asset manager market capitalization growth (8.8% p.a.). The aggregate price growth (8.8% p.a.) has slightly underperformed the broad global equity market index, the MSCI ACWI (9.4% p.a.), and therefore significantly underperformed the U.S. S&P 500 index (15.6% p.a., not shown) over the period. The S&P 500 index is relevant as 80% of the asset managers in this result set are US based asset managers, while the MSCI ACWI’s country exposure to U.S. is 57%. Lastly, as EPS (13.6%) outpaced aggregate asset manager share price (8.7%), a decrease in P/E ratio occurred over the period (see section Yields and Equity Risk Premium for Asset Managers, specifically Figure 8 where earnings yield is elevated in 2018 and 2019).

Table 1: Volume Weighted yoy Change in Profit, EPS, Dividend, Price, and Equity Market Year Profit EPS Dividends Asset MSCI Manager ACWI Share Price 2013 58.8% 55.3% 0.0% 46.3% 22.8% 2014 8.7% 11.0% 12.7% -5.3% -4.2% 2015 -10.7% -1.8% 19.5% -7.2% -2.4% 2016 -11.0% -13.7% -3.6% 1.6% 7.9% 2017 33.4% 42.2% 35.3% 33.3% 24.0% 2018 -9.4% -2.3% -4.4% -26.1% -9.4% 2019 9.9% 5.2% 1.5% 19.1% 27.3% Mean

11.4%

13.7%

8.7%

8.8%

9.4%

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Figure 1: Volume Weighted Cumulative Change in Profit, EPS, Dividend, Price, and Equity Market

JHG, VCTR, OMU, SLA.L, Liontrust, DWS, M&G are not included as they listed after 2012.

According to the equal weighted statistics, profit growth (4.9% p.a.) is slightly lower than EPS growth (6.8% p.a.), and both are behind dividend (9.6% p.a.) and market price growth (10.9% p.a.). On an equal weighted basis, listed managers have slightly outperformed the MSCI ACWI equity index (9.4% p.a.) but still underperformed the S&P 500 equity index (15.6% p.a., not shown).

Criteria for inclusion in the equal weighted subset were that the company was publicly listed, and was not classified as a private equity asset manager. The set includes those managers listed after 2012. No outlier values are removed.

Equal weighted calculations were based on median growth rate across all managers per year, regardless of size of contribution. This results in more benign looking growth values than the volume weighted set but we point out a high level of variance across the individual managers in each year, which is obfuscated by larger, more stable managers and by using the median rather than mean to calculate average values.

In comparing volume weighted and equal weighted statistics we find profit and dividend growth higher on a volume weighted basis, but dividend and price growth higher on equal weighted basis.

Smaller managers receive higher representation in the statistics on an equal weighted basis.

Table 2: Equal Weighted yoy Change in Profit, EPS, Dividend, Price, and Equity Market Year Profit EPS Dividends Asset MSCI Manager ACWI Stock Price 2013 19% 22% 9% 55% 23% 2014 14% 17% 20% 4% -4% 2015 -4% 2% 11% -4% -2% 2016 -16% -8% 5% 3% 8% 2017 17% 6% 8% 23% 24% 2018 9% 10% 11% -25% -9% 2019 -5% -1% 3% 20% 27% Lamb Quantitative Research Asset Management Industry: Financial Analysis

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Mean

4.9%

6.8%

9.6%

10.9%

9.4%

Figure 2: Equally Weighted Cumulative Change in Profit, EPS, Dividend, Price, and Equity Market

Each point is the median value for all managers for the year.

Earnings Growth Per Company Figure 3 shows the growth in EPS, profit, dividends, and price per company averaged over the period 2012-2019 and sorted by EPS growth.

Figure 3: Returns per Asset Manager

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Results for these managers were limited to the period from their listing to 2019: DWS, Janus Henderson, Liontrust, M&G, Standard Life Aberdeen, Victory Capital. Oaktree results limited to 2012 to 2018.

These values are also provided in table form in section Company Summary.

Abbreviations for company names can be found in the company directory in reference [1].,

Does Gearing Improve EPS Growth?

We check if gearing is correlated to same year and next year growth. Naively, higher levels of gearing should lead to higher returns up to a certain level.

We define gearing as the total liabilities divided by equity. For example, a company with no liabilities has gearing of 0.0, liabilities equal to equity gives gearing of 1.0, and liabilities twice that of equity gives gearing of 2.0.

We find the opposite of our assumption to be true. We find that EPS growth in the same year and the next year decreases with higher gearing. We also identify that at gearing levels between 0.0 and 2.0, EPS growth is uncorrelated to gearing. That is, gearing begins to negatively impact EPS growth above roughly 2.0. It is also important to note that gearing only predicts a small amount of the variance of EPS growth, particularly at lower levels of leverage.

A key takeaway from this data is that at gearing levels below roughly 2 times equity, manager’ EPS growth in the same or next was negative on average.

We find no correlation when looking beyond the current and next year.

Table 3: OLS Regression Result: Current Year EPS Growth ~ Gearing Dependent Variable: EPS Growth R-squared: 0.044 No. Observations: 238 F-statistic: 10.78 Independent Variable (constant) Gearing

Co-efficient 0.0950 -0.0601

Std. Err. 0.031 0.018

Table 4: OLS Regression Result: Next Year EPS Growth ~ Gearing Dependent Variable: EPS Growth R-squared: 0.057 No. Observations: 209 F-statistic: 12.62 Independent Variable (constant) Gearing

Co-efficient 0.0637 -0.069

Std. Err. 0.033 0.019

Adj. R-squared: 0.040 Prob (F-statistic): 0.00118

P>|t| 0.002 0.001

[0.025, 0.975] 0.034, 0.156 -0.096, -0.024

Adj. R-squared: 0.053 Prob (F-statistic): 0.000472 P>|t| 0.052 0.000

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[0.025, 0.975] -0.000, 0.128 -0.107, -0.031

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Figure 4: EPS Growth to Gearing

Values with EPS growth below -1 and above 1, and with Gearing above 10 or below 0 are removed as outliers.

Do Interest Rates Impact Balance Sheet Capital Mix? We look to check the sensitivity of capital mix (equity to total capital) to changes in interest rates.

We observe that the ratio of equity to capital has declined almost constantly from 2012 to 2019 (see Figure 5). We initially hypothesize and test if cheaper cost of credit leads to a lower proportion of equity to capital.

We check four absolute credit interest rates: U.S. Fed Fund Target Rate, U.S. Investment Grade bond interest rate, and U.S. High Yield bond interest rate.

The only statistically significant correlation is with the Fed Fund Target Rate as an independent variable, but the correlation is positive while we expect a negative correlation such that equity is preferred over debt when interest rates rise. Hence, we reject any of these variables as significant.

A full discussion of this result is beyond the scope of this report, as the explanation could easily consist of additional factors, or only be observable with more than the 7 years of data.

We informally observe that the investment grade yield over a slightly longer period from beginning 2012 to end 2020 exhibits a clear negative trend, which would correlate to the near monotonic increase in gearing, even though its volatility forces us to reject it as significant between end 2012 to end 2019.

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Figure 5: Capital Mix and Interest Rates

Is Operational Efficiency Predictive of Future Returns?

In all disclaimers for investment products, there is a statement “past performance is not indicative of future returns�. In the case of the asset managers, we find statistical evidence to argue this statement does not hold. Stated in more formal terms, we reject the null hypothesis that past performance is not predictive of future performance. We find an autocorrelation in operational efficiency even at the furthest ends of the data set: performance for 2019 is still significantly correlated with performance in 2012.

We measure operational efficiency as gross profit margin (GPM), which is calculated as the earnings before tax divided by the revenue in a particular year. We check for autocorrelation with lags between 1 year and 7 years inclusive. For example, for one year, we check 2013 with 2012, 2014 with 2013, etc., while for 7 years, we check 2019 with 2012 only due to the limits of the data set. We exclude Vanguard because its GPM is assumed to always be zero, and the private equity managers.

We also exclude GAM Investments (GAMI) although this merits discussion: GAMI suffered heavy losses in 2018 (around 10 times its typical profit) after having an above average GPM from 2012 to 2017. While the result is an outlier from a statistical distribution perspective (-7.8 times standard deviation), we should arguably not exclude GAMI as it is indicative of operational risks which could befall any asset manager prima facie. We are comfortable assuming that risks to GPM are almost exclusively downside risks.

We expected to find some serial correlation but the level of significance to decrease over time such that the correlation for n years in the future would simply be a random distribution. While the results (see Table 5) show a declining significance for longer lag times, even at the largest lag time (7 years), the statistical significance is still extremely high.

Table 5: GPM Autocorrelation for n Years Forward Years Forward Adj. R-squared P>|t| Co-efficient 1 0.82 0.000 0.92 2 0.80 0.000 0.90 3 0.81 0.000 0.89

No. Obs. 410 348 286

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4 5 6 7

0.81 0.75 0.78 0.76

0.000 0.000 0.000 0.000

0.91 0.87 0.87 0.89

224 163 102 46

The results are very similar if the analysis is performed using net profit margin (NPM), that is, profit after tax divided by revenue, instead of GPM. We consider GPM a better measure of operational efficiency because the tax rate is essentially not within the control of the asset manager. It has a similar impact as non-controlling interests. It is important to recognize that the tax rate still plays an unavoidable role in the amount of cash available to be returned to shareholders or reinvested in the business, hence can be relevant depending on the analysis objective.

Figure 6 shows the scatter plots of GPM at t versus GPM at t + n years, where n is 1 to 7 inclusive. It can be observed, that the significance and co-efficient of the regression persists even as the number of observations decreases.

Similar research from the Journal of Finance (see reference [13]) over a broader set of stocks has pointed to persistence of revenue growth, but found any statistical significance dissipated moving closer to the profit line. This flies contrary to our findings here. The key difference was our focus on a single industry with commodity type product offerings and homogenous fundamental dynamics as compared to a large, noisy set of heterogenous companies: what holds in one saturated industrial segment, namely asset management, is unlikely to hold over the expanse of the US equity stock universe. Different time periods may also play a role, but a less likely explanation.

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Figure 6: GPM Autocorrelation

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Market Pricing of Asset Managers Earnings Yield per Company Figure 7 shows earnings yield (inverse of Price-Earnings ratio) for year-end 2012, 2019 and mean year-end earnings yield for 2012-2019. It is sorted (descending) by 2019 earnings yield. To illustrate, BrightSphere (BSIG) is by this measure the relative cheapest and Affiliated Managers Group (AMG) the relative most expensive.

Figure 7: Earnings Yield

Where earnings were negative (e.g., Fiera Capital, WETF) or the company did not exist (e.g., Oaktree, M&G, JHG, Liontrust in that year), no value is shown.

Mean values are calculated for the period when the company existed.

These values are provided in table form in section Company Summary.

Yields and Equity Risk Premium for Asset Managers Figure 8 shows the equal weighted earning yield, dividend yield (dividend/price), and their respective values less the risk-free rate (here the US Fed Fund Rate effective yield). The earnings yield less riskfree rate approximates the Equity Risk Premium (ERP) of the asset management industry.

Each series is strongly mean-reverting according to the Hurst Exponent. This metric indicates whether a series in mean-reverting, random walking, or trending.

The chart shows the industry’s ERP and dividend yield above average at the end of 2018, before falling again 2019. The dividend yield less risk-free rate has the lowest mean reversion.

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Figure 8: Earnings and Dividend Yields

Price to Book Figure 9 shows the progression of volume weighted and equal weighted price-to-book ratios (P/B) over the period. It indicates the ratio has remained stable from 2012 to 2019 on both measures, particularly for the volume weighted aggregate. The equal weighted P/B became relatively elevated in 2013, before falling back to its trend at around 4 times. The equally weighted aggregate P/B has roughly been two to three times higher than the volume weighted ratio. We conclude that P/B are lower for larger asset managers due to the weighting of larger managers in the volume weighted metric.

Both series exhibit mean-reversion when tested with the Hurst Exponential, although we will watch the volume weighted metric for an upward trend in future.

Figure 9: Aggregate Price to Book Ratio

We checked the equal weighted P/B ratio at 2013 but find the increase is not caused any outliers.

Figure 10 shows P/B mean and 2019 values per company, sorted by 2019 P/B, where a low (high) P/B indicates a cheap (expensive) valuation.

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Figure 10: Price to Book Ratio per Company

Private Equity asset managers are removed as some AuM are held on balance sheet.

These values are provided in table form in section Company Summary.

Forward Market Predictions Using Price-to-Book Ratio On the result from the previous section Price to Book that book value is mean reverting and that deviation in price away from this value represents an opportunity for value investors, we check whether P/B can predict future changes in asset manager price. See Table 6 and Figure 11.

We find that the volume weighted book value predicts 54% of the change in the aggregate in share price of asset managers one year later. While this seems like a significant finding for value and hedge investment strategies, we caution the strong correlation may result from the delay between end of year share price and the share price in the subsequent year during which final results are announced.

This explanation is not completely adequate though as annual results are released for asset managers very early in the next year from end January to April and companies report at least semiannually and quarterly, hence annual figures should not surprise the market completely. An explanation may be a low P/B indicates a manager has excess capital that can be returned to the market through dividends, share buybacks, or invested in future growth. As a cautionary note, there are many explanations for a correlation in statistics, and it is not possible to know if any are true. It may simply be that prices for smaller asset managers lag changes in prices for larger managers and the market overall.

Table 6: OLS Regression Result: 1 Year Forward Aggregate Asset Manager Price ~ Aggregate P/B Ratio Dependent Variable: Aggregate Asset R-squared: 0.614 Adj. R-squared: 0.549 Manager Price Change – 1 Year Forward No. Observations: 8 F-statistic: 9.530 Prob (F-statistic): 0.215 Independent Variable

Co-efficient

Std. Err.

P>|t|

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(constant) P/B Ratio

1.3959 -0.7861

0.410 0.255

0.014 0.021

0.393, 2,399 -1.409, -0.163

Figure 11: MSCI ACWI vs. Aggregate Asset Manager P/B

The two series in the figure look positively correlated because the P/B ratio is the reciprocal of the P/B (effectively the Book to Price ratio). The regression tests use P/B (not B/P) so are negatively correlated, meaning

As we expect asset manager stock prices to be highly correlated to equity markets, we also check the correlation between the aggregate change in book value of listed asset managers in our sample and the changes in MSCI ACWI Equity Index, however the correlation between asset manager P/B and one year forward equity market index (MSCI ACWI) is not significant.

Sensitivity of Asset Manager P/E Ratio to Interest Rates We find strong linear correlations when using U.S. interest rates as explanatory variables for asset manager earnings yield.

We find statistically significant correlations with explanatory power (Adj. R-squared) of 95%, 95%, and 73% when using U.S. high yield bond yield (Table 8) and U.S. investment grade bond yield (Table 9), and U.S. Federal Reserve Funding Rate plus constant (Table 7) respectively. The results indicate that mean earnings yield of the asset managers increases 1.2, 0.97, 1.35 times the corresponding increase in respective bond yields. By our calculations, this is roughly twice the sensitivity of the equity market to the same change in bond prices.

All three models have strong statistical significance, but we require more data points before ruling in favor of one or the other, particularly as the explanatory variables from each model are correlated, and the results not fully statistically robust (see detailed results in section Appendix / Regression Results / Sensitivity of PE Ratio to Interest Rates and the notes on OLS validity checks in section Regressions / Tests for Validation in [1]).

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The earnings yield is calculated from the equally weighted mean price-earnings ratio (P/E) for each year from all listed managers. Earnings yield, the mathematical inverse of P/E, is preferred to P/E, as the latter exhibits asymptotic behavior as earnings approach zero, which skews the results for linear regression.

Table 7: OLS Regression Result: Earnings Yield ~ Federal Reserve Funding Rate Dependent Variable: Earnings Yield R-squared: 0.773 Adj. R-squared: 0.735 No. Observations: 8 F-statistic: 20.45 Prob (F-statistic): 0.00401 Independent Variable (constant) Fed. Funding Rate

Co-efficient 0.0557 1.2164

Std. Err. 0.003 0.269

P>|t| 0.000 0.004

[0.025, 0.975] 0.048, 0.063 0.558, 1.875

Table 8: OLS Regression Result: Earnings Yield ~ High Yield Corporate Bond Rate Dependent Variable: Earnings Yield R-squared: 0.961 Adj. R-squared: 0.956 No. Observations: 8 F-statistic: 174.4 Prob (F-statistic): 0.000003 Independent Variable High Yield Bond

Co-efficient 0.9666

Std. Err. 0.073

P>|t| 0.000

[0.025, 0.975] 0.794, 1.140

Table 9: OLS Regression Result: Earnings Yield ~ Investment Grade Corporate Bond Rate Dependent Variable: Earnings Yield R-squared: 0.959 Adj. R-squared: 0.953 No. Observations: 8 F-statistic: 154.5 Prob (F-statistic): 0.000004 Independent Variable Investment Grade Bond

Co-efficient 1.3509

Std. Err. 0.105

P>|t| 0.000

[0.025, 0.975] 1.102, 1.600

We also checked the sensitivity of earnings yield to ten-year U.S. treasury rates but find much less explanatory power for the given sample.

Does the Market Reward Past or Future Performance?

We analyze whether historical operational efficiency, that is net profit margin, is rewarded by the market with higher price-earnings ratio (P/E) but find no relationship once we control for the impact of interest rates on P/E (see section Sensitivity of Asset Manager P/E Ratio to Interest Rates).

We checked the impact of the geometric mean NPM from current to 7 years previous, as seems reasonable given the results from section Is Operational Efficiency Predictive of Future Returns?, finding no statistically significant predictive power other than for the current year, which we discard as it is a component of same year earnings. We observe the same for GPM, profit, EPS growth, and AuM. Without controlling for interest rates we find significant results, which leads us to confirm that NPM is significantly correlated to interest rates.

As we find no evidence of the market rewarding historically high performance with high P/E, we test instead if P/E depends on forward earnings per share growth. While this is unhelpful for fundamental investors, it corresponds to the common practitioner assertion that the market captures future earnings expectations and assumes analysts’ forecasts roughly match actual earnings. The results are presented in Table 10. Once we control for interest rate, we only find statistically significant correlations between the adjusted P/E and 1-year forward EPS growth, but at a uselessly low Lamb Quantitative Research Asset Management Industry: Financial Analysis

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explanatory power (Adjusted r-squared at 0.03) of the overall variance. We control for interest rate using U.S. investment grade corporate bond yield. Without interest rates as a factor, the models have high statistical significance, leading us to a possibility that a major factor in financial analysts’ growth forecasts seems to be the result of interest rates.

We also check if NPM is predictive of EPS growth in the next year, but find no correlation.

At this point we cannot find any useful historical or future explanatory variables for P/E other than interest rates. This corresponds to the commonly expressed unpredictability of financial markets.

For reference, Table 10: Regression Results of Future Mean EPS Growth to Adjusted P/E Years Number of F-statistic Adjusted rp-value for Observations squared independent variable EPS growth over n future years 0 151 0.4 0.00 0.5324 1 156 5.9 0.03 0.0167 2 121 3.1 0.02 0.0829 3 98 0.9 0.00 0.3349 4 73 0.2 -0.01 0.6415 5 55 1.6 0.01 0.2120 6 34 0.5 -0.02 0.4969 7 22 0.2 -0.04 0.6303

p-value for constant regressor

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0035

Adjusted P/E is calculated as 1 / (eps / price – rate ) where ‘eps’ is earnings per share (EPS), ‘price’ is year-end stock market price, ‘rate’ is U.S. investment grade bond index yield.

Future EPS growth is calculated as the geometric mean EPS growth 0 to n years forward of the Adjusted P/E. Mean future EPS growth values above 200% and below -50% are removed as outliers.

Adjusted P/E above 300 and below 0 are removed as outliers.

For reference, Figure 12 shows the 2-year mean EPS growth (2018 and 2019) versus P/E in 2017 adjusted for interest rates. The p-value for the regression is 0.06, thereby outside standard statistical confidence intervals. Figure 13 shows the data points and regression line from the geometric mean NPM over prior 3 years to P/E in 2019.

P/E can be high due to high future growth expectations, but also due to one off accounting costs where EPS is expected to revert up in the next period (and vice-versa).

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Figure 12: 2-year Forward Mean EPS Growth vs Adjusted P/E

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Figure 13: Mean Net Profit Margin from 2016 to 2018 vs. P/E 2019

Mean NPM values below -100% are removed as outliers.

P/E values greater than 30 are removed as outliers.

Does Investment Performance Influence Net Inflows?

We check whether investment performance influences future net inflow levels. Overall manager investment performance, as reported by only some asset managers in annual reports to shareholders, is measured as the percentage of AuM which outperforms its benchmark before fees and commissions on a 1-, 3-, or 5-year basis.

We calculate relative net inflows as the net inflows relative to the manager’s AuM compared to the total net inflows for all managers relative to total AuM for all managers. We regress the 1-, 3-, and 5year investment performance against the relative net inflow level for each manager for the current year (y+0), next year (y+1), and two years later (y+2).

The results are presented in Figure 14 where ‘ip1’, ‘ip3’, ‘ip5’ correspond to investment performance over the last 1, 3, and 5 years; ‘sr’ refers to the net inflows relative to the overall market in the same year as the end of the performance measurement (‘y+0’), and 1 and 2 years later (‘y+1’, ‘y+2’). A regression line is drawn for each regression where the results are statistically significant (p<0.05).

We highlight the result that 1-year investment performance positively impacts net inflows in the subsequent year. 5-year Investment performance is also significant for next year net inflows, although point out that 5-year investment performance is the cumulative result of five 1-year performances, so do not find it surprising that the 1-year performance result is distributive to 5-year performance. None of the regressions are statistically significant for net inflows in 2 years.

Lamb Quantitative Research Asset Management Industry: Financial Analysis

19

Figure 14: Net Inflows vs Investment Performance

As can be observed in Figure 14, there is a tendency only for managers with above average performance to report performance, while underperformance is much less likely to be reported. This can be observed from the larger number of data points on the right-hand side of the graphs, but is also confirmed by the regression output and discussed in [3] Asset Management Industry: Financials / Do Asset Managers Report Poor Investment Performance?

Lamb Quantitative Research Asset Management Industry: Financial Analysis

20

Company Summary Table 11 provides the key financial metrics for each company in the sample set used in this report.

Table 11: Per Company Financial Metrics Asset Manager

AuM Growth (%)

Mean Net Inflows / AuM (%)

Investment Return (Mean %)

Revenue Growth

AuM Margin

Cost Growth

Profit Growth

GPM Mean

NPM Mean

Equity to Capital

Equity to Capital Growth

EPS Growth

Dividend Growth

Payout Ratio

Payout Ratio Growth

AB

4.7

-0.2

4.4

4.3

0.4

1.9

19.6

20.3

17.0

50

0

23

11

115

-10

AMG

6.6

2.0

5.1

2.7

0.4

5.0

-3.7

35.6

28.8

54

0

-37

ANIMA

18.3

5.3

3.9

5.4

0.3

3.4

7.4

59.6

41.5

56

2

7

APAM

6.3

-1.3

7.9

5.9

0.7

1.7

24.2

27.3

16.7

64

ASHM

4.2

-2.6

6.4

-1.4

0.6

-1.7

-0.2

70.3

58.9

80

6

2

2

74

-1

AXA IM

2.6

1.7

1.1

-4.9

0.2

-6.0

-0.5

25.0

17.9

Allianz

0.5

-0.6

1.4

-1.3

0.4

-0.1

-0.8

39.2

25.7

Ameriprise

1.0

-2.5

3.0

0.1

0.6

-0.4

3.2

22.5

17.4

Amundi

8.2

4.3

0.3

6.5

0.2

5.8

6.7

48.5

32.1

45

-3

9

13

53

4

Apollo

14.3

9.7

-1.8

0.3

1.2

0.0

-8.2

54.7

50.2

33

2

9

6

65

-3

Aviva

0.3

-0.9

-1.2

0.9

0.2

-0.1

9.2

17.0

13.1

Azimut

12.2

12.4

-1.6

9.5

1.8

9.2

9.4

34.2

28.9

11

-3

12

22

81

9

BNY Mellon

4.4

1.0

4.3

-4.5

0.2

-5.2

-1.6

28.2

21.8

12

12

27

0

BOK Financial

8.3

-0.9

7.1

11.4

0.5

-15.3

38.8

35.2

24.9

BSIG

3.4

-2.1

6.9

1.9

0.4

-3.6

72.9

20.5

11.5

-4

7

88

BX

13.3

7.1

6.1

7.8

1.8

2.0

21.2

46.6

42.5

46

3

33

21

114

-9

Bellevue

22.7

-0.3

9.2

12.9

1.2

13.2

6.7

27.7

23.4

39

4

4

10

-156

6

Blackrock

8.8

4.7

2.6

5.7

0.2

0.0

9.2

39.8

29.5

67

2

11

12

43

1

Brookfield

21.7

12.3

0.0

25.1

1.0

19.9

30.1

75.7

68.3

41

-1

4

2

35

-2

CI Financial

3.8

-0.9

1.3

1.1

1.6

0.9

2.0

40.4

29.2

50

-6

9

-4

62

-12

CNS

5.9

-2.1

7.2

5.2

0.6

3.5

10.1

39.1

25.6

76

-7

9

6

119

-2

CSAM

3.2

3.8

-1.9

-1.9

0.4

-2.5

-1.3

24.6

18.0

Lamb Quantitative Research

Asset Management Industry: Financial Analysis

First Year

56 11

52

4

-31

122

21

Last Year

Asset Manager

AuM Growth (%)

Mean Net Inflows / AuM (%)

Investment Return (Mean %)

Revenue Growth

AuM Margin

Cost Growth

Profit Growth

GPM Mean

NPM Mean

Equity to Capital

Equity to Capital Growth

EPS Growth

Dividend Growth

Payout Ratio

Payout Ratio Growth

Carlyle

3.5

-3.7

6.4

1.6

1.7

19.0

-8.4

29.7

28.1

21

-2

32

23

-334

-6

DWS

1.8

0.0

10.1

0.8

0.1

0.0

3.3

29.0

19.9

62

Diamond Hill Capital Eaton Vance

12.0

1.5

9.3

9.4

0.7

5.6

14.7

49.2

32.8

69

9

17

2

61

-13

12.7

4.9

4.2

4.6

0.4

4.5

7.0

30.8

20.9

38

0

11

-25

51

-33

Eurizon

9.8

5.6

3.2

11.7

0.2

1.1

20.4

68.6

53.6

FII

5.3

-2.1

6.3

4.3

0.3

5.3

4.3

29.4

20.3

56

5

6

-11

69

-16

Fidelity

8.4

0.6

7.5

6.5

0.4

3.9

13.7

24.1

19.6

Fiera Capital

10.7

-1.4

5.0

22.6

0.3

23.3

0.0

6.6

4.7

50

-7

15

-18

Franklin Resources GAMCO

-1.4

-0.5

-2.0

-2.9

0.9

-1.7

-4.9

36.6

24.5

75

5

-3

6

24

10

0.0

-7.0

6.9

-1.2

0.9

-1.3

1.0

39.6

27.0

-29

-7

1

-40

18

-40

GAMI

-5.5

-6.0

-3.0

-7.7

0.7

-2.4

0.0

-11.2

-16.0

76

-10

-39

-100

63

-100

Generali

0.9

1.4

-7.6

8.2

0.0

12.1

4.2

54.9

40.0

Goldman Sachs HIMCO

10.2

4.4

3.8

6.2

0.4

5.5

11.6

22.1

15.1

4.7

-1.0

5.1

6.2

0.8

6.1

9.7

17.9

12.2

HSBC

2.2

1.7

1.9

-0.9

0.2

-1.9

1.5

27.5

19.4

Invesco

7.5

0.6

3.1

9.5

0.5

14.3

-0.6

28.2

21.4

37

JHG

0.1

-4.5

6.1

2.4

0.2

2.0

-4.8

26.6

26.1

73

JP Morgan

6.5

3.6

4.1

4.4

0.3

5.1

5.2

29.0

19.3

Jupiter

3.6

1.2

1.8

-0.2

1.1

-3.3

7.4

37.0

29.0

71

2

9

10

83

0

KKR

12.8

1.4

7.1

-2.2

5.6

7.8

-6.6

64.7

63.4

27

-3

7

-7

78

-13

LAZ

5.1

2.2

2.6

4.1

0.6

3.8

5.2

31.0

20.6

21

11

93

-8

LM

2.7

-1.7

3.1

1.3

0.4

-2.7

0.0

5.3

4.3

59

-7

21

-60

Legal & General

11.6

3.6

7.1

4.2

0.1

5.4

3.5

49.8

39.7

Lamb Quantitative Research

First Year

2018

0

2017

Asset Management Industry: Financial Analysis

-4

-2

10

53

12 2017

43

22

Last Year

Asset Manager

AuM Growth (%)

Mean Net Inflows / AuM (%)

Investment Return (Mean %)

Revenue Growth

AuM Margin

Cost Growth

Profit Growth

GPM Mean

NPM Mean

Equity to Capital

Liontrust

18.1

10.4

0.0

12.1

0.5

13.4

6.1

22.1

17.2

35

86

2014

M&G

1.3

-1.6

9.2

-0.1

0.1

0.0

4.0

87.7

71.3

18

138

2018

Magellan

33.7

18.2

9.7

31.5

0.7

25.7

33.0

73.3

55.2

91

-5

Man

9.5

1.0

2.8

-1.0

1.4

-10.5

0.0

5.2

4.4

63

-2

-10.0

-19.4

7.1

-10.6

0.8

-8.2

-22.6

26.8

23.8

62

3

6.1

3.3

3.6

10.4

0.5

12.1

-6.0

27.3

16.9

11

-5

Manning & Napier Morgan Stanley NOAH

44.9

37.1

-1.6

43.2

0.5

39.5

49.6

53.4

45.5

Natixis

3.8

1.5

2.1

7.6

0.3

6.2

11.5

27.9

19.3

Northern Trust OZM

6.2

2.6

4.1

5.9

0.0

6.5

6.6

33.3

22.9

0.7

-2.3

3.5

-8.6

2.8

-12.6

-34.1

15.0

5.3

Oaktree

4.0

-0.4

3.3

32.6

0.5

0.0

-28.3

PFG

7.2

1.2

6.6

12.4

0.3

10.5

22.7

31.3

25.0

PTM

0.2

-3.7

2.7

-0.6

1.1

1.9

-1.3

86.3

57.0

98

12.5

13.3

-0.4

16.5

1.5

16.1

16.3

67.9

61.2

75

5.9

2.5

4.3

5.2

0.3

4.0

11.0

26.8

21.1

Partners Group Prudential Financial Prudential plc

EPS Growth

47

-9

Dividend Growth

Payout Ratio

70

68

-4

66

-26

170

13

116

57

Payout Ratio Growth

2018

92 129

0

-12

19

20

68

1

39

2

5

11

81

5

1

8

17

52

8

3.7

-2.0

-5.5

0.4

-1.0

-10.7

44.0

35.8

11.6

2.6

8.3

8.9

0.4

12.1

3.5

43.0

44.0

8.5

3.4

3.5

0.3

0.3

-4.5

14.3

32.0

21.8

SDR.L

7.6

3.6

4.0

1.4

0.5

0.6

3.7

33.0

27.2

41

SLA.L

-2.2

-10.1

1.4

-3.9

0.1

-4.9

-1.1

31.8

24.4

41

SLAM

6.4

3.7

2.1

12.4

0.3

13.8

19.7

38.5

28.6

Silvercrest

10.6

2.6

6.5

8.9

0.4

12.9

-3.0

26.4

19.5

52

Asset Management Industry: Financial Analysis

114 -19

-9

Last Year

-18

4

-8.9

First Year

16

4

Pzena Investment SCHW

Lamb Quantitative Research

Equity to Capital Growth

2017

52

23

Asset Manager

AuM Growth (%)

Mean Net Inflows / AuM (%)

Investment Return (Mean %)

Revenue Growth

AuM Margin

Cost Growth

Profit Growth

GPM Mean

NPM Mean

State Street

5.1

-0.7

5.7

6.2

0.1

7.6

3.6

24.3

19.7

T Rowe Price

9.7

0.6

9.3

8.1

0.5

6.8

12.4

48.6

31.8

UBS

4.6

0.9

3.4

-0.6

0.3

-0.1

-4.1

27.5

22.2

VCTR

21.0

-1.8

4.0

11.1

0.3

8.6

44.1

VTAM

8.3

4.9

2.3

7.5

0.5

5.2

12.6

33.7

27.7

Value Partners Vanguard

7.3

0.2

6.4

11.8

1.6

20.3

3.2

39.2

33.7

14.5

6.5

7.3

5.7

0.1

5.7

0.0

0.0

0.0

Virtus IP

11.5

-0.4

5.5

9.1

0.6

8.5

13.7

21.9

WETF

16.9

9.6

1.9

15.5

0.5

17.5

0.0

Mean

7.6

1.9

3.8

5.8

0.7

4.7

6.9

7.3

Equity to Capital

Equity to Capital Growth

91

-7

EPS Growth

Dividend Growth

Payout Ratio

Payout Ratio Growth

4

11

28

7

15

4

49

-9

38

2015

434

92

-72

4

15.0

52

-17

14

25.4

17.6

70

-17

35.3

26.8

51.4

-4.2

5

80

First Year

1

22 43

9.6

4.4

58.8

-6.4

* = Asset Manager is classified as Private Equity Empty cells represent invalid values such as metrics only valid for exchange listed companies or negative or infinite values First Year and Last Year are only populated if not respectively 2012 or 2019.

Lamb Quantitative Research

Asset Management Industry: Financial Analysis

24

Last Year

Appendix Regression Results Sensitivity of PE Ratio to Interest Rates OLS Regression Results ============================================================================== Dep. Variable: ey R-squared: 0.773 Model: OLS Adj. R-squared: 0.735 Method: Least Squares F-statistic: 20.45 Date: Mon, 18 Jan 2021 Prob (F-statistic): 0.00401 Time: 21:16:55 Log-Likelihood: 30.930 No. Observations: 8 AIC: -57.86 Df Residuals: 6 BIC: -57.70 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 0.0557 0.003 18.867 0.000 0.048 0.063 ffr 1.2164 0.269 4.522 0.004 0.558 1.875 ============================================================================== Omnibus: 6.711 Durbin-Watson: 2.312 Prob(Omnibus): 0.035 Jarque-Bera (JB): 2.061 Skew: -1.196 Prob(JB): 0.357 Kurtosis: 3.676 Cond. No. 130. ============================================================================== Notes: [1] 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 " OLS Regression Results ================================================================================== Dep. Variable: ey R-squared (uncentered): 0.961 Model: OLS Adj. R-squared (uncentered): 0.956 Method: Least Squares F-statistic: 174.4 Date: Mon, 18 Jan 2021 Prob (F-statistic): 3.33e-06 Time: 20:46:14 Log-Likelihood: 23.399 No. Observations: 8 AIC: -44.80 Df Residuals: 7 BIC: -44.72 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------hy 0.9666 0.073 13.207 0.000 0.794 1.140 ============================================================================== Omnibus: 0.942 Durbin-Watson: 0.795 Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.012 Skew: -0.091 Prob(JB): 0.994 Kurtosis: 2.942 Cond. No. 1.00 ============================================================================== Notes: [1] R² is computed without centering (uncentered) since the model does not contain a constant.

Lamb Quantitative Research Asset Management Industry: Financial Analysis

25

[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 " OLS Regression Results ================================================================================== Dep. Variable: ey R-squared (uncentered): 0.959 Model: OLS Adj. R-squared (uncentered): 0.953 Method: Least Squares F-statistic: 164.5 Date: Mon, 18 Jan 2021 Prob (F-statistic): 4.06e-06 Time: 20:46:09 Log-Likelihood: 23.174 No. Observations: 8 AIC: -44.35 Df Residuals: 7 BIC: -44.27 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------ig 1.3509 0.105 12.826 0.000 1.102 1.600 ============================================================================== Omnibus: 0.757 Durbin-Watson: 0.624 Prob(Omnibus): 0.685 Jarque-Bera (JB): 0.627 Skew: 0.470 Prob(JB): 0.731 Kurtosis: 2.002 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 "

Does Gearing Boost EPS Growth?

OLS Regression Results ============================================================================== Dep. Variable: eps_g R-squared: 0.044 Model: OLS Adj. R-squared: 0.040 Method: Least Squares F-statistic: 10.78 Date: Fri, 22 Jan 2021 Prob (F-statistic): 0.00118 Time: 22:02:41 Log-Likelihood: -87.107 No. Observations: 238 AIC: 178.2 Df Residuals: 236 BIC: 185.2 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 0.0950 0.031 3.090 0.002 0.034 0.156 gearing -0.0601 0.018 -3.284 0.001 -0.096 -0.024 ============================================================================== Omnibus: 4.392 Durbin-Watson: 1.895 Prob(Omnibus): 0.111 Jarque-Bera (JB): 5.257 Skew: -0.116 Prob(JB): 0.0722 Kurtosis: 3.690 Cond. No. 2.71

Lamb Quantitative Research Asset Management Industry: Financial Analysis

26

============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

OLS Regression Results ============================================================================== Dep. Variable: eps_g_1y R-squared: 0.072 Model: OLS Adj. R-squared: 0.067 Method: Least Squares F-statistic: 14.95 Date: Fri, 22 Jan 2021 Prob (F-statistic): 0.000151 Time: 22:02:41 Log-Likelihood: -67.918 No. Observations: 195 AIC: 139.8 Df Residuals: 193 BIC: 146.4 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 0.0728 0.033 2.214 0.028 0.008 0.138 gearing -0.0744 0.019 -3.866 0.000 -0.112 -0.036 ============================================================================== Omnibus: 4.960 Durbin-Watson: 2.065 Prob(Omnibus): 0.084 Jarque-Bera (JB): 5.236 Skew: -0.233 Prob(JB): 0.0730 Kurtosis: 3.654 Cond. No. 2.68 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Do Interest Rates Impact Balance Sheet Capital Mix? OLS Regression Results ============================================================================== Dep. Variable: ey R-squared: 0.773 Model: OLS Adj. R-squared: 0.735 Method: Least Squares F-statistic: 20.45 Date: Mon, 18 Jan 2021 Prob (F-statistic): 0.00401 Time: 12:41:17 Log-Likelihood: 30.930 No. Observations: 8 AIC: -57.86 Df Residuals: 6 BIC: -57.70 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 0.0557 0.003 18.867 0.000 0.048 0.063 ffr 1.2164 0.269 4.522 0.004 0.558 1.875 ============================================================================== Omnibus: 6.711 Durbin-Watson: 2.312 Prob(Omnibus): 0.035 Jarque-Bera (JB): 2.061 Skew: -1.196 Prob(JB): 0.357 Kurtosis: 3.676 Cond. No. 130. ============================================================================== Notes:

Lamb Quantitative Research Asset Management Industry: Financial Analysis

27

[1] 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 "

Forward Market Predictions Using Price-to-Book Ratio OLS Regression Results ============================================================================== Dep. Variable: am_pr_t+1 R-squared: 0.614 Model: OLS Adj. R-squared: 0.549 Method: Least Squares F-statistic: 9.530 Date: Sat, 23 Jan 2021 Prob (F-statistic): 0.0215 Time: 20:01:25 Log-Likelihood: 4.8234 No. Observations: 8 AIC: -5.647 Df Residuals: 6 BIC: -5.488 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const 1.3959 0.410 3.405 0.014 0.393 2.399 vw -0.7861 0.255 -3.087 0.021 -1.409 -0.163 ============================================================================== Omnibus: 0.030 Durbin-Watson: 1.286 Prob(Omnibus): 0.985 Jarque-Bera (JB): 0.255 Skew: -0.061 Prob(JB): 0.881 Kurtosis: 2.135 Cond. No. 16.9 ============================================================================== Notes: [1] 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 "

Does Investment Performance Influence Net Inflows? ip1: Shift = 1 OLS Regression Results ============================================================================== Dep. Variable: sr R-squared: 0.054 Model: OLS Adj. R-squared: 0.042 Method: Least Squares F-statistic: 4.593 Date: Sat, 23 Jan 2021 Prob (F-statistic): 0.0351 Time: 23:12:42 Log-Likelihood: 34.381 No. Observations: 82 AIC: -64.76 Df Residuals: 80 BIC: -59.95 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const -0.1031 0.068 -1.525 0.131 -0.237 0.031 ip 0.2304 0.108 2.143 0.035 0.016 0.444 ============================================================================== Omnibus: 15.544 Durbin-Watson: 1.616 Prob(Omnibus): 0.000 Jarque-Bera (JB): 31.941 Skew: -0.625 Prob(JB): 1.16e-07

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Kurtosis: 5.790 Cond. No. 8.31 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ip5: Shift = 1 OLS Regression Results ============================================================================== Dep. Variable: sr R-squared: 0.066 Model: OLS Adj. R-squared: 0.054 Method: Least Squares F-statistic: 5.192 Date: Sat, 23 Jan 2021 Prob (F-statistic): 0.0256 Time: 23:12:43 Log-Likelihood: 31.005 No. Observations: 75 AIC: -58.01 Df Residuals: 73 BIC: -53.37 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const -0.1650 0.090 -1.836 0.070 -0.344 0.014 ip 0.2764 0.121 2.279 0.026 0.035 0.518 ============================================================================== Omnibus: 12.588 Durbin-Watson: 1.580 Prob(Omnibus): 0.002 Jarque-Bera (JB): 22.875 Skew: -0.551 Prob(JB): 1.08e-05 Kurtosis: 5.471 Cond. No. 9.93 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ip3: Shift = 0 OLS Regression Results ============================================================================== Dep. Variable: sr R-squared: 0.099 Model: OLS Adj. R-squared: 0.089 Method: Least Squares F-statistic: 10.31 Date: Sat, 23 Jan 2021 Prob (F-statistic): 0.00181 Time: 23:12:42 Log-Likelihood: 46.841 No. Observations: 96 AIC: -89.68 Df Residuals: 94 BIC: -84.55 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] -----------------------------------------------------------------------------const -0.1576 0.060 -2.606 0.011 -0.278 -0.038 ip 0.2744 0.085 3.211 0.002 0.105 0.444 ============================================================================== Omnibus: 20.778 Durbin-Watson: 1.714 Prob(Omnibus): 0.000 Jarque-Bera (JB): 44.825 Skew: -0.780 Prob(JB): 1.85e-10 Kurtosis: 5.962 Cond. No. 8.25 ============================================================================== Notes:

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

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