Hedge Fund Perfomrance

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The hedge-fund industry has grown rapidly over the past two decades, offering investorsunique investment opportunities that often reflect more complex risk exposures than thoseof traditional investments. In this article we present a selective review of the recent academicliterature on hedge funds as well as updated empirical results for this industry.

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  • Electronic copy available at: http://ssrn.com/abstract=2637007

    Hedge Funds:

    A Dynamic Industry In Transition

    Mila Getmansky, Peter A. Lee, and Andrew W. Lo

    This Draft: July 28, 2015

    Abstract

    The hedge-fund industry has grown rapidly over the past two decades, offering investorsunique investment opportunities that often reflect more complex risk exposures than thoseof traditional investments. In this article we present a selective review of the recent academicliterature on hedge funds as well as updated empirical results for this industry. Our reviewis written from several distinct perspectives: the investors, the portfolio managers, theregulators, and the academics. Each of these perspectives offers a different set of insightsinto the financial system, and the combination provides surprisingly rich implications for theEfficient Markets Hypothesis, investment management, systemic risk, financial regulation,and other aspects of financial theory and practice.

    Keywords: Hedge Funds; Alternative Investments; Investment Management; Long/Short;Illiquidity; Financial Crisis.

    JEL Classification: G12

    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, DavidHsieh, Hossein Kazemi, Bing Liang, Tarun Ramadorai, and two anonymous referees for helpful commentsand suggestions. The views and opinions expressed in this article are those of the authors only and do notnecessarily 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 FinancialEngineering is gratefully acknowledged.

    Isenberg School of Management, University of Massachusetts, 121 Presidents Drive, Room 308C,Amherst, MA 01003, (413) 5773308 (voice), (413) 5453858 (fax), msherman@isenberg.umass.edu (email).

    Senior Research Scientist, AlphaSimplex Group, LLC.Charles E. & Susan T. Harris Professor, MIT Sloan School of Management, and Chief Investment

    Strategist, AlphaSimplex Group, LLC. Please direct all correspondence to Andrew Lo, MIT Sloan School,100 Main Street, E62618, Cambridge, MA 021421347, (617) 2530920 (voice), alo-admin@mit.edu (email).

  • Electronic copy available at: http://ssrn.com/abstract=2637007

    Contents

    List of Tables iii

    List of Figures vii

    1 Introduction 1

    2 Hedge Fund Characteristics 2

    2.1 Fees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Leverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Share Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.4 Fund Flows and Capital Formation . . . . . . . . . . . . . . . . . . . . . . . 7

    3 An Overview of Hedge-Fund Return Data 9

    3.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.2 Biases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.3 Entries and Exits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.4 Hedge Fund Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    4 Investment Performance 20

    4.1 Basic Performance Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.2 Performance Persistence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.3 Timing Ability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.4 Hedge-Fund Styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    5 Illiquidity 32

    5.1 Measures of Illiquidity and Return Smoothing . . . . . . . . . . . . . . . . . 325.2 Illiquidity and Statistical Biases . . . . . . . . . . . . . . . . . . . . . . . . . 355.3 Measuring Illiquidity Risk Premia . . . . . . . . . . . . . . . . . . . . . . . . 365.4 The Mean-Variance-Illiquidity Frontier . . . . . . . . . . . . . . . . . . . . . 37

    6 Hedge Fund Risks 39

    6.1 VaR and Risk-Shifting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406.2 Linear Factor Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416.3 Limitations of Hedge-Fund Factor Models . . . . . . . . . . . . . . . . . . . 496.4 Operational Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516.5 Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.6 Hedge-Fund Beta Replication . . . . . . . . . . . . . . . . . . . . . . . . . . 59

    7 The Financial Crisis 61

    7.1 Early Warning Signs of the Crisis . . . . . . . . . . . . . . . . . . . . . . . . 647.2 Winners and Losers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677.3 Post-Crisis Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727.4 Hedge Funds and Systemic Risk . . . . . . . . . . . . . . . . . . . . . . . . . 74

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  • 8 Implementation Issues for Hedge Fund Investing 78

    8.1 The Limits of Mean-Variance Optimization . . . . . . . . . . . . . . . . . . . 798.2 Over-Diversification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798.3 Investment Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818.4 An Integrated Hedge-Fund Investment Process . . . . . . . . . . . . . . . . . 868.5 The Adaptive Markets Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . 96

    9 Conclusion 104

    A Appendix 105

    A.1 Lipper TASS Fund Category Definitions . . . . . . . . . . . . . . . . . . . . 105A.2 Cleaning Lipper TASS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 106A.3 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

    References 110

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  • List of Tables

    1 Net-of-fee returns for a hypothetical fund of funds charging a 1% fixed fee anda 10% incentive fee and investing an equal amount of capital in two funds, Aand B, with both funds charging a 2% fixed fee and a 20% incentive fee, forvarious realized annual gross-of-fee returns for A and B. Net-of-fee returns arereported as a percent of assets under management (top panel). The bottompanel reports fees as a percentage of net profits of the total gross investmentreturns generated by A and B. No high-water mark or clawback provisionsare assumed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

    2 Summary statistics for cross-sectionally averaged returns from the LipperTASS 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 databasesample 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, attritionrate, average return, and percentage of funds that performed negatively arereported for each year from January 1996 through December 2014. Source:Lipper TASS database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 stylecategory. Correlations for the 10 main Lipper TASS hedge fund categories,Funds of Funds, and All Single Manager Funds found in the Lipper TASSdatabase from January 1996 through December 2014 are reported. The AllSingle Manager Funds category includes the funds in all 10 main Lipper TASScategories and any other single-manager funds present in the database (rela-tively few) while excluding funds of funds. Correlations are color-coded withthe highest correlations in blue, intermediate correlations in yellow, and thelowest correlations in red. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    6 Summary statistics for the returns of the average fund in each Lipper TASSstyle 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-valueof the Ljung-Box Q-statistic with three lags for the 10 main Lipper TASShedge fund categories, Funds of Funds, and All Single Manager Funds foundin the Lipper TASS database from January 1996 through December 2014 arereported. Sharpe and Sortino ratios are adjusted for the three-month U.S.Treasury Bill rate. The All Single Manager Funds category includes thefunds in all 10 main Lipper TASS categories and any other single-managerfunds present in the database (relatively few) while excluding funds of funds. 28

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  • 7 Conditional exposures of average hedge fund category returns to the sevenFung and Hsieh (2001) factors. The exposures for the 10 main Lipper TASShedge fund categories, Funds of Funds, and All Single Manager Funds foundin the Lipper TASS database are based on a multivariate regression with aconstant term. Regression outputs that are significant with 95% confidenceare indicated by * and shown in color (orange for negative and blue forpositive). Monthly correlations between hedge fund returns and all sevenfactors are presented. This analysis spans January 1996 through December2014. . . . . . . . . . . . . . . .