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Hedge Funds: a Dynamic Industry in Transition

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NBER WORKING PAPER SERIES HEDGE FUNDS: A DYNAMIC INDUSTRY IN TRANSITION Mila Getmansky Peter A. Lee Andrew W. Lo Working Paper 21449 http://www.nber.org/papers/w21449 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 August 2015 We thank Vikas Agarwal, George Aragon, Guillermo Baquero, Monica Billio, Keith Black, Ben Branch, Nick Bollen, Stephen Brown, Jayna Cummings, Gregory Feldberg, Mark Flood, Robin Greenwood, David Hsieh, Hossein Kazemi, Bing Liang, Tarun Ramadorai, and two anonymous referees for helpful comments and suggestions. The views and opinions expressed in this article are those of the authors only and do not necessarily represent the views and opinions of any other organizations, any of their affiliates or employees, or any of the individuals acknowledged above. Research support from the MIT Laboratory for Financial Engineering is gratefully acknowledged. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w21449.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2015 by Mila Getmansky, Peter A. Lee, and Andrew W. Lo. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Hedge Funds: A Dynamic Industry In Transition Mila Getmansky, Peter A. Lee, and Andrew W. Lo NBER Working Paper No. 21449 August 2015 JEL No. G01,G11,G12,G20,G23,G24 ABSTRACT The hedge-fund industry has grown rapidly over the past two decades, offering investors unique investment opportunities that often reflect more complex risk exposures than those of traditional investments. In this article we present a selective review of the recent academic literature on hedge funds as well as updated empirical results for this industry. Our review is written from several distinct perspectives: the investor's, the portfolio manager's, the regulator's, and the academic's. Each of these perspectives offers a different set of insights into the financial system, and the combination provides surprisingly rich implications for the Efficient Markets Hypothesis, investment management, systemic risk, financial regulation, and other aspects of financial theory and practice. Mila Getmansky Andrew W. Lo Isenberg School of Management MIT Sloan School of Management Room 308C 100 Main Street, E62-618 University of Massachusetts Cambridge, MA 02142 121 Presidents Drive, Amherst, MA 01003 and NBER [email protected] [email protected] Peter A. Lee AlphaSimplex Group, LLC One Cambridge Center Cambridge, MA 02142 [email protected] Contents List of Tables iii List of Figures vii 1 Introduction 1 2 Hedge Fund Characteristics 2 2.1 Fees ........................................ 3 2.2 Leverage...................................... 4 2.3 ShareRestrictions................................. 6 2.4 FundFlowsandCapitalFormation . 7 3 An Overview of Hedge-Fund Return Data 9 3.1 DataSources ................................... 10 3.2 Biases ....................................... 11 3.3 EntriesandExits ................................. 14 3.4 HedgeFundIndexes ............................... 18 4 Investment Performance 20 4.1 BasicPerformanceStudies ............................ 21 4.2 PerformancePersistence ............................. 24 4.3 Timing Ability . 24 4.4 Hedge-FundStyles ................................ 26 5 Illiquidity 32 5.1 Measures of Illiquidity and Return Smoothing . 32 5.2 Illiquidity and Statistical Biases . 35 5.3 Measuring Illiquidity Risk Premia . 36 5.4 The Mean-Variance-Illiquidity Frontier . 37 6 Hedge Fund Risks 39 6.1 VaRandRisk-Shifting .............................. 40 6.2 LinearFactorModels............................... 41 6.3 Limitations of Hedge-Fund Factor Models . 49 6.4 OperationalRisks................................. 51 6.5 RiskManagement................................. 53 6.6 Hedge-Fund Beta Replication . 59 7 The Financial Crisis 61 7.1 Early Warning Signs of the Crisis . 64 7.2 WinnersandLosers................................ 67 7.3 Post-CrisisPerformance ............................. 72 7.4 HedgeFundsandSystemicRisk . .. .. 74 i 8 Implementation Issues for Hedge Fund Investing 78 8.1 The Limits of Mean-Variance Optimization . 79 8.2 Over-Diversification................................ 79 8.3 InvestmentImplications ............................. 81 8.4 An Integrated Hedge-Fund Investment Process . 86 8.5 TheAdaptiveMarketsHypothesis. 96 9 Conclusion 104 A Appendix 105 A.1 LipperTASSFundCategoryDefinitions . 105 A.2 CleaningLipperTASSData ........................... 106 A.3 Glossary...................................... 107 References 110 ii List of Tables 1 Net-of-fee returns for a hypothetical fund of funds charging a 1% fixed fee and a 10% incentive fee and investing an equal amount of capital in two funds, A and B, with both funds charging a 2% fixed fee and a 20% incentive fee, for various realized annual gross-of-fee returns for A and B. Net-of-fee returns are reported as a percent of assets under management (top panel). The bottom panel reports fees as a percentage of net profits of the total gross investment returns generated by A and B. No high-water mark or clawback provisions areassumed..................................... 5 2 Summary statistics for cross-sectionally averaged returns from the Lipper TASS database with no bias adjustments, adjustments for survivorship bias, adjustments for backfill bias, and adjustments for both biases during the sam- ple period from January 1996 through December 2014. For each database sample the number of fund-months, annualized mean, annualized volatility, skewness, kurtosis, maximum drawdown, first-order autocorrelation, and p- value of the Ljung-Box Q-statistic with three lags are reported. 14 3 Statistics for entries and exits of single-manager hedge funds, including num- ber of entries, exits, and funds at the start and end of a given year, attrition rate, average return, and percentage of funds that performed negatively are reported for each year from January 1996 through December 2014. Source: LipperTASSdatabase............................... 16 4 Information about hedge-fund index providers, index family, and the avail- ability of total-industry and category indexes for commonly used monthly, daily, and replication hedge-fund indexes. 19 5 Monthly correlations of the average returns of funds in each hedge-fund style category. Correlations for the 10 main Lipper TASS hedge fund categories, Funds of Funds, and All Single Manager Funds found in the Lipper TASS database from January 1996 through December 2014 are reported. The All Single Manager Funds category includes the funds in all 10 main Lipper TASS categories and any other single-manager funds present in the database (rela- tively few) while excluding funds of funds. Correlations are color-coded with the highest correlations in blue, intermediate correlations in yellow, and the lowestcorrelationsinred. ............................ 26 6 Summary statistics for the returns of the average fund in each Lipper TASS style category and summary statistics for the corresponding CS/DJ Hedge- Fund Index. Number of fund months, annualized mean, annualized volatility, Sharpe ratio, Sortino ratio, skewness, kurtosis, maximum drawdown, corre- lation coefficient with the S&P 500, first-order autocorrelation, and p-value of the Ljung-Box Q-statistic with three lags for the 10 main Lipper TASS hedge fund categories, Funds of Funds, and All Single Manager Funds found in the Lipper TASS database from January 1996 through December 2014 are reported. Sharpe and Sortino ratios are adjusted for the three-month U.S. Treasury Bill rate. The “All Single Manager Funds” category includes the funds in all 10 main Lipper TASS categories and any other single-manager funds present in the database (relatively few) while excluding funds of funds. 28 iii 7 Conditional exposures of average hedge fund category returns to the seven Fung and Hsieh (2001) factors. The exposures for the 10 main Lipper TASS hedge fund categories, Funds of Funds, and All Single Manager Funds found in the Lipper TASS database are based on a multivariate regression with a constant term. Regression outputs that are significant with 95% confidence are indicated by “*” and shown in color (orange for negative and blue for positive). Monthly correlations between hedge fund returns and all seven factors are presented. This analysis spans January 1996 through December 2014......................................... 44 8 Conditional exposures of average hedge fund category returns to four in- vestable factors. The exposures for the 10 main Lipper TASS hedge fund categories, Funds of Funds, and All Single Manager Funds found in the Lipper TASS database are based on a multivariate regression with a constant term. Regression outputs that are significant with 95% confidence are indicated by “*” and shown in color (orange for negative and blue for positive). Monthly correlations between hedge fund returns and all four factors are presented. This analysis spans January 1996 through December 2014. 45 9 Out-of-sample analysis for the
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