INSTITUTIONAL PERSPECTIVES May 018 Highly Selective Active Managers, Though Rare, Outperform Key Takeaways ffresearch shows that highly skilled active managers with high active share, low R and a patient investment approach have systematically outperformed by meaningful amounts over time. ffthese managers, which academics have termed highly selective, can be identified ex-ante. ffstudies showing active underperformance ignore the degree to which many active portfolios have resembled their benchmarks. ffhighly selective active managers performance is strongest during periods of increased volatility in the market. Active share has been gaining in recognition as a measure of a portfolio s level of active management. A recent paper combining both academic research and ClearBridge internal analysis on active share found that managers with high active share and longterm conviction (longer holding periods) systematically outperform their benchmarks net of fees. 1 These findings are consistent with studies investigating the importance of a portfolio s R (R-squared), which is a statistical metric of active management that measures how much of the variability of the portfolio s returns can be explained by variability in the benchmark model. We believe that an understanding of both active share and R helps to contextualize recent capital flows and performance trends and highlights the scarcity and value of highly selective active management. Measurement of active share as a metric of active management is a fairly recent activity. In their seminal 009 study How Active Is Your Fund Manager? A New Measure That Predicts Performance, Martijn Cremers and Antti Petajisto first defined active share as an investment tool that measures the percentage of a portfolio s stock holdings that differ from its benchmark. 3 And a recent paper by Cremers and Ankur Pareek, building on previous research, shows that only strategies with holdings that differ significantly (top quintile of active share) from their benchmarks tend to outperform
HIGHLY SELECTIVE ACTIVE MANAGERS, THOUGH RARE, OUTPERFORM Exhibit 1: Large Cap and Small Cap Performance by Active Share and (1990 01)..0 Low Active Share Low Active Share Low Active Share High Active Share High Active Share High Active Share Low Fund High Fund Low Fund High Fund 1. Seven-factor alpha (%) 1.0 0. 0.0-0. -1.0-1. -.0 Large Cap Small Cap Source: Cremers 017. Performance of large cap and small cap mutual funds shown as seven-factor alphas by active share and fund holding duration (1990 01). net of fees. (Results of this research are not susceptible to benchmark manipulation managers choosing improper benchmarks that may be easier to beat suggesting that such benchmark gaming does not account for the outperformance. ) Of these strategies, those with longer holding periods have shown stronger performance (longer holding periods defined as two or more years, as short-term mispricing tends to be competed away by institutions, while the market s ability to arbitrage away long-term mispricing is more limited). In fact, Cremers and Pareek show that strategies with top-quintile active share and longduration portfolios have generated five-factor net alpha of.0% per year on an equal-weighted basis and five-factor alpha over the benchmark net return of as much as 3.7% per annum in the 1990 013 period. 6 Conversely, funds with low active share have generated negative alpha. Research also shows that highly selective active managers outperform in both large cap and small cap. A recently published paper by Cremers demonstrates that managers in the top quartile of both active share and fund holding duration generated annualized sevenfactor net alpha 7 of 1.37% in the large cap category and 1.9% in the small cap category in the 1990 01 period (Exhibit 1). 8 It should be noted that the small cap managers tend to have better performance than the large cap managers in general, as well as higher average active share (of roughly 89%) versus large cap managers (of about 7%). 9 R Confirms Highly Selective Active Management Outperforms The conclusions generated by this active share analysis reflect the research done on the R of mutual fund returns. R, a more traditional statistical metric of active management, results from a regression analysis comparing the returns of a portfolio to those of a benchmark model; it measures how much of the variability of the portfolio s returns can be explained by variability in the benchmark model. A high R indicates that the benchmark explains most of the portfolio s returns; in other words, the portfolio s behavior is not very differentiated from the benchmark. The portfolios of highly selective managers, on the other hand, have low R, which has been shown to be a determining factor in relative outperformance; in fact, research has shown that portfolios with low R historically have generated alpha (calculated using the Fama and French 1993 and Carhart 1997 model (FFC)), net of fees, of 60 basis points annually (Exhibit ). 10 On top of this, when low R is combined with skill (defined as top-quintile alpha generation in prior periods), alpha generation rises to 3.8% annually (Exhibit 3). R, then, much like active share, shows that outperforming managers can be identified ex-ante. Measures of active management like active share and R are important because analysis showing that active managers consistently underperform often ignores the degree to which many active managers portfolios generally have resembled their benchmarks. To that point,
INSTITUTIONAL PERSPECTIVES May 018 Exhibit : Alpha and R Levels (Entire Universe) 1.0 Exhibit 3: Alpha and R Levels (Managers with Highest Prior- Period Alpha). Alpha Net of Fees (%) 0. 0.0-0. -1.0 0.60-0.3-1.1-1.39-1.6 Alpha Net of Fees (%) 3.. 1. 0. -0. 3.80 0.7-1.01-0.7-1.19-1. 1 R1 ) 3 (Highest 3 R ) R Quintiles (Highest -1. 1 1 R R) ) 3 (Highest R) R ) R Quintiles Alpha measured using FFC model. Source: Amihud and Goyenko 013. Alpha measured using FFC model. Source: Amihud and Goyenko 013. the median mutual fund s R from 1990 to 010 was 93%, meaning that, for most portfolios, over 90% of return variability can be replicated by major stock indexes. 11 In Cremer s research, by definition, only % of active managers made the cutoff for top-quartile active share. 1 This data goes a long way to explaining much of the average active manager s persistent underperformance. Sharpe observed in his 1991 paper The Arithmetic of Active Management that the sum of all money managers equals the market, making it impossible to outperform on average, net of fees. Hence, it is not surprising to find the average active manager with a portfolio closely resembling a passive product, and delivering nearly passive returns when adjusted for fees. Indeed, research estimates that only a small percentage of funds (16%) engages in what Petajisto defines as active stock picking (taking large but diversified positions away from the index ). 13 Exhibit : Cross-Sectional Return Dispersion is Cyclical 3 30 Levels of Volatility Affect Performance Opportunity The amount of available alpha in the market (or the amount of opportunity to outperform even for the highly selective managers) is not consistent over time. Researcher Anna Von Reibnitz studied this phenomenon in a paper on the impact of cross-sectional dispersion of returns on mutual fund performance. The paper strongly suggests that the market environment affects manager performance. Cross-sectional return dispersion measures the difference in performance of stocks within an index or a benchmark. The math states that if all the stocks in the S&P 00 Index rise or fall the exact same amount in a period, generating cross-sectional return dispersion of zero, outperformance in that period will be impossible. Von Reibnitz points out that the market has periods of high and low crosssectional return dispersion (Exhibit ). From 197 to 013, the active manager universe generated excess returns Cross-Sectional Return Dispersion Percent (%) 0 1 10 0 199 1997 00 007 01 017 Source: Active vs Passive: How Will the World of Investing Evolve? (part 1 of ) dated January 31, 017. UBS 018. All rights reserved. Reproduced with permission. As of November 30, 017. 3
HIGHLY SELECTIVE ACTIVE MANAGERS, THOUGH RARE, OUTPERFORM Exhibit : Dispersion Provides Opportunity for Highly Selective Active Managers (197 013) 10 Low-Dispersion Market High-Dispersion Market 8 6 Alpha (%) 9.1 0 -.8.3 0.0-0.69-0.1-0.07-0.31-0.03-0.93 1 (Highest (Highest R) R ) 3 3 R) R ) R Quintiles Annualized net alpha in the top and bottom quintiles of cross-sectional return dispersion shown by R quintiles. Alpha measured using FFC model. Source: Von Reibnitz 01. measured as FFC alpha of 3.06% on an annualized basis during the months ranked in the top 0% of return dispersion. During periods of least return dispersion, the universe of active managers generated FFC alpha of -0.7% based on the same FFC alpha methodology. Note that the highly selective active managers, defined as those in the lowest R quintile, generated FFC alpha of over 9% on an annualized basis during the periods of highest cross-sectional return dispersion. In short, highly selective active management outperforms when there is dispersion in the market, according to Von Reibnitz s study. Outside of the top two dispersion quintiles, highly selective managers don t generate meaningful alpha despite outperforming over the period studied (Exhibits and 6). 1 Consequently, it is not surprising that active performance, even for many highly selective managers, has been lackluster over the last several years. Years with generally high cross-sectional return dispersion were 197 7, 198, 1988, 1990 91, 1998 0, and 008 09. 1 This helps to explain Cremer s findings that since 1990, highly selective active, patient managers had the best performance in 1990 001 and 007 13. 16 Recent years have featured historically low crosssectional return dispersion (which may be the result of historically low market volatility) and, hence, low opportunity for outperformance. We believe the history of cross-sectional return dispersion suggests that exactly timing highly selective active managers periods of outperformance is difficult. Even the best performers will go through some periods of underperformance, especially when they have high active share. In an analysis done by Vanguard, 98% of outperforming managers have at least four years of underperformance over the 000 1 period, with 9% underperforming for seven years or more. 17 Where Are the Highly Selective Active Managers? One key message from both active share and R research is that highly selective active managers are scarce. The proportion of managers that fit this description decreases even further when patience
INSTITUTIONAL PERSPECTIVES May 018 Exhibit 6: Low R Outperforms in Periods of High Return Dispersion (197 013) 10 Low R R HighR R 8 6 Alpha (%) 9.1 0 0.0-0.93 -.6-1.39-1.6 0. -0.81-0.78-0.69 - - 1 Dispersion) 3 (Highest Dispersion) Dispersion Quintiles Annualized net alpha for the highest and lowest quintile R shown in each cross-sectional return dispersion quintile. Alpha measured using FFC model. Source: Von Reibnitz 01. longer holding periods is added as a characteristic. One recent study found that only 1.6% of mutual fund assets are managed with a combination of high active share and patience. 18 Index funds did not really become well accepted until the late 1990s, while ETFs did not become widely available until the 000s; the SPY, which tracks the S&P 00 Index, launched only years ago. We submit that, in the absence of competition for capital from passive products, active managers success was at least partially measured by adherence to a specific investment style value, growth, etc. which was measured by tracking error. To minimize tracking error, many active managers likely shifted portfolios so that they more closely resembled the benchmark. However, as the prevalence of low-cost passive products increased, investors started to substitute these active managers with ETFs and index funds. This move was likely hastened by low market volatility, which dampened active returns. Conclusion Academic research shows that a group of skilled, truly active managers have systematically outperformed their benchmarks by significant amounts. As mentioned, these managers can be identified ex-ante using both active share and R methodologies defined in academic research. They share key common characteristics: high active share or low R (one must differ from the benchmark to beat it) due to their superior conviction and skill; patience, expressed as long holding periods (one must have the opportunity to take advantage of long-term mispricings); and reasonable fees (high fees detract from performance). Highly selective active managers are especially well positioned to add value today, particularly if volatility and return dispersion begin to re-emerge after years of low volatility.
HIGHLY SELECTIVE ACTIVE MANAGERS, THOUGH RARE, OUTPERFORM 1 ClearBridge Investments, High Conviction Active Management Asserts Strength Over Time, May 017. We use the term highly selective following Amihud, Y., and Goyenko, R. Mutual Fund s R as Predictor of Performance. The Review of Financial Studies, 013. 3 Cremers, M., and Petajisto, A. How Active Is Your Fund Manager? A New Measure That Predicts Performance. Review of Financial Studies, 009. Cremers, M., and Pareek, A. Patient Capital Outperformance: The Investment Skill of High Active Share Managers Who Trade Infrequently. Journal of Financial Economics, 016. Shleifer, A., and Vishny, R. Equilibrium Short Horizons of Investors and Firms. American Economic Review Papers and Proceedings, 1990; The Limits of Arbitrage. Journal of Finance, 1997. Academic findings on this are mixed. 6 Cremers and Pareek 016. A 01 working draft of this paper examining the 199 013 period found absolute outperformance over the benchmark of 1.9% per year. The five factors are: market, size, value, momentum and liquidity. 7 As defined in Cremers, M., Petajisto, A., and Zitzewitz, E. Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation. Critical Finance Review, 01. The seven factors are: market, size (small cap and mid cap), value (large, mid cap and small cap) and momentum. 8 Cremers, M. Active Share and the Three Pillars of Active Management: Skill, Conviction and Opportunity. Financial Analysts Journal, 017. 9 Cremers 017. Performance of large cap and small cap mutual funds shown as seven-factor alphas by active share and fund holding duration (1990 01). 10 Amihud and Goyenko 013. Data covers 1988 010. Amihud and Goyenko s benchmark model is the FFC four-factor model and the results are also robust for the market-based factor model of Cremers, Petajisto and Zitzewitz (010). As such, high R indicates that variability in portfolio returns can be explained by variability in major stock indexes. 11 Amihud and Goyenko 013. Drawn from a sample of,60 all cap equity funds. 1 Cremers 017. 13 Petajisto, A. Active Share and Mutual Fund Performance. Financial Analysts Journal, 013. Drawn from a sample set of,70 funds. 1 Von Reibnitz, A. When Opportunity Knocks - Cross-Sectional Return Dispersion and Active Fund Performance. Working paper, September 01. 1 Von Reibnitz 01. 16 Cremers 017. 17 Wallick, D.W., Wimmer, B.R., CFA, Balsamo, J. Keys to improving the odds of active management success. Vanguard Research, 01. 18 Cremers 017. About the Author Dimitry Dayen, CFA Director, Senior Research Analyst for Energy 1 years of investment industry experience Joined ClearBridge Investments in 01 Member of the CFA Institute Member of the CFA Society New York BA in Economics from New York University ClearBridge Investments 60 Eighth Avenue, New York, NY 10018 800 691 6960 ClearBridge.com Past performance is no guarantee of future results. Copyright 018 ClearBridge Investments. All opinions and data included in this paper are as of May and are subject to change. The opinions and views expressed herein are of the portfolio management team named above and may differ from other managers, or the firm as a whole, and are not intended to be 6 a forecast of future events, a guarantee of future results or investment advice. This information should not be used as the sole basis to make any investment decision. The statistics have been obtained from sources believed to be reliable, but the accuracy and completeness of this information cannot be guaranteed. Neither ClearBridge Investments nor its information providers are responsible for any damages or losses arising from any use of this information. Performance source: Internal. Benchmark source: Standard & Poor s. Neither ClearBridge Investments LLC nor its information providers are responsible for any damages or losses arising from any use of this information.