Hedge Fund Performance Research Articles

Do Hot Hands Exist among Hedge Fund Managers? An Empirical Evaluation

ABSTRACTIn measuring performance persistence, we use hedge fund style benchmarks. This allows us to identify managers with valuable skills, and also to control for option‐like features inherent in returns from hedge fund strategies. We take into account the possibility that reported asset values may be based on stale prices. We develop a statistical model that relates a hedge fund's performance to its decision to liquidate or close in order to infer the performance of a hedge fund that left the database. Although we find significant performance persistence among superior funds, we find little evidence of persistence among inferior funds.

The Road Less Traveled: Strategy Distinctiveness and Hedge Fund Performance

The Road Less Traveled: Strategy Distinctiveness and Hedge Fund Performance

Hedge Fund Systemic Risk Contribution, Capital Requirements and Performance

Hedge Fund Systemic Risk Contribution, Capital Requirements and Performance

Liquidity Risk and the Cross-Section of Hedge-Fund Returns

This paper demonstrates that liquidity risk as measured by the covariation of fund returns with unexpected changes in aggregate liquidity is an important determinant in the cross-section of hedge-fund returns. The results show that funds that significantly load on liquidity risk subsequently outperform low-loading funds by about 6% annually, on average, over the period 1994-2008, while negative performance is observed during periods of significant liquidity crises. The returns are independent of the liquidity a fund provides to its investors as measured by lockup and redemption notice periods, and are also robust to commonly used hedge-fund factors, none of which carries a significant premium during the sample period. These findings highlight the importance of understanding systematic liquidity variations in the evaluation of hedge-fund performance.

Out of the Dark: Hedge Fund Reporting Biases and Commercial Databases

We examine the potential for selection bias in voluntarily reported hedge fund performance data. We construct a novel set of hedge fund returns that have never been reported to a commercial hedge fund database. These returns allow a direct comparison of performance between funds that choose to report to commercial databases and funds that do not. We find that funds that report their performance to commercial databases significantly outperform non-reporting funds. Our results suggest that the voluntarily reported performance in commercial databases suffers from a selection bias that may exaggerate the average skill of the universe of hedge fund managers.

Systematic Funds Outperform Discretionary Funds

This article compares the performance of systematic and discretionary hedge funds. In systematic funds, most trades are originated and executed by computer programs written for that purpose. In discretionary funds, human traders make decisions when to buy and sell which financial securities. The analysis herein shows that systematic funds consistently outperform discretionary funds when performance is measured by Jensen’s Alpha. Systematic funds also exhibit lower correlation with the S&P 500 index and produce higher cumulative returns since inception, although with higher volatility.On the basis of absolute monthly returns, systematic hedge funds outperform discretionary hedge funds whenever markets are falling. When markets are rising, however, discretionary funds tend to deliver higher absolute returns than do systematic funds. On the absolute return basis, the difference between systematic and nonsystematic funds cannot be explained by liquidity considerations: both systematic and discretionary funds deliver comparable returns in both liquid and illiquid markets.Across a variety of other metrics, however, systematic funds perform much better than discretionary funds in limited liquidity across a variety of metrics: systematic funds produce less extreme drawdowns, higher Sharpe ratios, higher Jensen’s alpha, and attract higher Assets Under Management (AUM).Finally, systematic funds exhibit lower skewness and kurtosis than do discretionary funds. When adjusted for skewness and kurtosis, systematic returns are statistically indistinguishable from discretionary returns, suggesting that much of systematic funds’ outperformance comes from better ability to manage extreme events, performing better than discretionary funds in crisis conditions.

Factor Exposures and Hedge Fund Operational Risk: The Case of Amaranth

Hedge fund performance and risk measurement continues to present intriguing challenges to both academics and practitioners. Risk-return measures that are solely based on historical return series tend to provide limited information and the marginal new information revealed by another quantitative measure tends to be small, and approaches zero once three or more measures are considered. In this article we will examine the ris exposures and performance characteristics of Amaranth Advisors LLC. Amaranth Advisors LLC was created in 2000 as a multi-strategy hedge fund. Beginning operations with approximately $600 million in capital, it sought to employ a diverse group of arbitrage trading strategies particularly featuring convertible bonds, mergers and utilities. In 2002, Amaranth added energy commodity trading to its slate of strategies with JP Morgan Chase clearing its commodity trades. On August 4, 2006 NYMEX examined Amaranth’s positions and calculated that Amaranth held about 51% of the open interest in the September natural gas futures contract which would expire at the end of the month. NYMEX decided that this was too large and on August 8 NYMEX compliance officials notified Amaranth of their concerns. Amaranth complied with NYMEX’s directions and subsequently reduced its September and October positions. However, at the same time Amaranth increased its positions in September and October ICE contracts such that their overall positions in natural gas rose. The events that followed in late August and September led to huge losses with Amaranth losing significant value.

The effect of size, age, beta and disclosure requirements on hedge fund performance

In this study, hedge fund returns in excess of the S&P 500 were analyzed to determine the effects of beta, fund size, age and enforced registration in 2006 as a result of Rule 203(b)(3)-2. It was discovered that beta had a positive effect on performance, while the increasing age of a fund caused managers to suffer from ·style drift', thereby reducing the hedge fund's performance. It was also found that registration increased returns by 11.6 per cent by raising the net worth requirement for accredited investors, thereby providing funds with a more knowledgeable investor and increased asset base stability. This suggests that advisers have been able to use funds more efficiently by taking on more leveraged positions and holding less cash on hand, while pursuing a greater number of strategies.

Markov chain models to estimate the premium for extended hedge fund lockups

AbstractA lockup period for investment in a hedge fund is a time period after making the investment during which the investor cannot freely redeem his investment. It is routine to have a one‐year lockup period, but recently the requested lockup periods have grown longer. We estimate the premium for such extended lockup, taking the point of view of a manager of a fund of funds, who has to choose between two investments in similar funds in the same strategy category, the first having a one‐year lockup and the second having an n‐year lockup. Assuming that the manager will rebalance his portfolio of hedge funds on a yearly basis, if permitted, we define the annual lockup premium as the difference between the rates of return from these investments. We develop a Markov chain model to estimate this lockup premium. By solving systems of equations, we fit the Markov chain transition probabilities to three directly observable hedge fund performance measures: the persistence of return, the variance of return and the hedge‐fund death rate. The model quantifies the way the lockup premium depends on these parameters. Data from the TASS database are used to estimate the persistence, which is found to be statistically significant. Copyright © 2009 Wilmott Magazine Ltd

Measuring Funds of Hedge Funds Performance Using Quantile Regressions: Do Experience and Size Matter?

This article is the first to use quantile regression to analyze the impact of experience and size of funds of hedge funds (FHFs) on performance. In comparison to OLS regression, quantile regression provides a more detailed picture of the influence of size and experience on FHF return patterns. Hence, it allows one to study the relevance of these factors for various return and risk levels instead of average return and risk, as is the case with OLS regression. Because FHF size and age (as a proxy for experience) are available in a panel setting, one can perform estimations in an unbalanced stacked panel framework. This study analyzes time series and descriptive variables of 649 FHFs drawn from the Lipper TASS Hedge Fund database for the time period January 1996 to August 2007. The empirical results suggest that experience and size have a negative effect on performance, with a positive curvature at the higher quantiles. At the lower quantiles, however, size has a positive effect with a negative curvature. Both factors show no significant effect at the median. <b>TOPICS:</b>Real assets/alternative investments/private equity, statistical methods, performance measurement

Role of Managerial Incentives and Discretion in Hedge Fund Performance

ABSTRACTUsing a comprehensive hedge fund database, we examine the role of managerial incentives and discretion in hedge fund performance. Hedge funds with greater managerial incentives, proxied by the delta of the option‐like incentive fee contracts, higher levels of managerial ownership, and the inclusion of high‐water mark provisions in the incentive contracts, are associated with superior performance. The incentive fee percentage rate by itself does not explain performance. We also find that funds with a higher degree of managerial discretion, proxied by longer lockup, notice, and redemption periods, deliver superior performance. These results are robust to using alternative performance measures and controlling for different data‐related biases.

Performance Measurement of Hedge Funds Portfolios in a Downside Risk Framework

This article starts with the recognition of the limits of a mean–variance framework to measure the performance of hedge funds, whose returns are often not normally distributed and usually asymmetric. It then introduces the literature focused on performance measurement in a downside-risk framework. The authors present both traditional and alternative performance measures, including an index of their own construction. They conclude that three well-accepted methods provide different rankings of hedge funds and, explaining the differences thus observed, suggest that the hedge fund alphas computed using their own approach are more correct. <b>TOPICS:</b>Real assets/alternative investments/private equity, risk management, statistical methods, performance measurement

Investing in Hedge Funds When the Fund's Characteristics are Exploitable

We predict hedge fund performance using a measure that optimally combines the information revealed by hedge fund characteristics. Using a hedge fund consolidated database, we show not only that this strategy based on the proposed combination measure delivers superior performance but also that such performance is more persistent than employing conventional single-characteristic strategies. In fact, none of the considered single-characteristic strategies are able to deliver superior performance that persists when we control explicitly for fund-level share restrictions, minimum fund size restrictions, lags in reported data and various data biases as well as performance measure manipulation and nonlinearities in fund returns.

The performance persistence of equity long/short hedge funds

Studies examining the persistence of hedge fund performance vary greatly in their conclusions owing to different methodologies, databases, investigation periods and performance measures.1 This paper does not consider various approaches to clarify the picture, but, instead, focuses on a particularly flexible one. Every period, hedge funds are sorted into portfolios according to characteristics in the last period, and the portfolios are then tracked for the next period. After the tracking period, the sorting is repeated. This approach has been used in the mutual fund literature by Hendricks et al2 and Carhart,3 among others, and has several advantages. First, portfolio betas may be more stable than betas of individual funds because time-varying betas can offset each other on the portfolio level. This is particularly relevant for hedge funds: as they have fewer restrictions on borrowing, shorting, the use of derivatives and so on, they typically follow highly opportunistic strategies that lead to time-varying risk exposures. Secondly, beta measurement is more precise owing to diversification of idiosyncratic risk and long time series for the portfolio returns. Finally, suppose there is only a very small autocorrelation in fund returns. Given the high return variance, it is difficult to detect this correlation by looking at individual funds. As in the case of momentum strategies, we have to buy a portfolio of last period’s winners, not just one fund, to find persistence.

Monitoring Daily Hedge Fund Performance When Only Monthly Data is Available

This paper introduces a new approach to monitoring the daily risk of investing in hedge funds. Specifically, we use low-frequency (monthly) models to forecast high-frequency (daily) hedge fund returns. This approach addresses the common problem that confronts investors who wish to monitor their hedge funds on a daily basis — disclosure of returns by funds occurs only at a monthly frequency, usually with a time lag. We use monthly returns on investable assets or factors to fit monthly hedge fund returns, then forecast daily returns of hedge funds during the following month using the publicly observed daily returns on the explanatory assets. We show that our replication approach can be used to forecast daily returns of long/short hedge funds. In addition, for diversified portfolios such as hedge fund indices and funds-of-hedge-funds, it forecasts daily returns very accurately. We illustrate how our simple replication approach can be used to (1) hedge daily hedge fund risk and (2) estimate and control value-at-risk.

Hedge Fund Risk Dynamics: Implications for Performance Appraisal

ABSTRACTAccurate appraisal of hedge fund performance must recognize the freedom with which managers shift asset classes, strategies, and leverage in response to changing market conditions and arbitrage opportunities. The standard measure of performance is the abnormal return defined by a hedge fund's exposure to risk factors. If exposures are assumed constant when, in fact, they vary through time, estimated abnormal returns may be incorrect. We employ an optimal changepoint regression that allows risk exposures to shift, and illustrate the impact on performance appraisal using a sample of live and dead funds during the period January 1994 through December 2005.

Does Hedge Fund Performance Persist? Overview and New Empirical Evidence

Abstract The contribution of this paper is to provide an overview and new empirical evidence on hedge fund performance persistence, which has been a controversial issue in the academic literature during the last several years. In the first step, we review recent studies and put them into a joint evaluation of hedge fund performance persistence. In the second step, the methodological framework developed in the overview is used to present new empirical evidence. We find different levels of performance persistence depending on the statistical methodology and the hedge fund strategy employed. In our study, performance persistence cannot be explained by the use of option‐like strategies, but it can be partially explained by survivorship and backfilling bias. Differences among hedge fund strategies might be explained by return smoothing. Finally, we develop a rationale for choosing between different methodologies to measure performance persistence and conclude that the multi‐period Kolmogorov‐Smirnov test is the most useful for evaluating performance persistence of hedge funds.

Update to “An Examination of Fund Age and Size and Its Impact on Hedge Fund Performance”

This short article updates research originally published in the February 2007 issue of the investment journal <i>Derivatives Use</i>, <i>Trading &amp; Regulation</i> (re-titled of as May 2007 to <i>Journal of Derivatives &amp; HedgeFunds</i>). The original article and this update attempt to discover whether smaller, younger hedge funds offer stronger performance than larger, older hedge funds. Using indices created with six subsets of hedge fund data (small, medium, large, young, mid-age, and older funds), and Monte Carlo simulations, we examine the performance, volatility, and risk profiles of each fund group. <b>TOPICS:</b>Performance measurement, volatility measures, risk management

The Geography of Hedge Funds

This article analyzes the relationship between the risk-adjusted performance of hedge funds and their proximity to investments using data on Asia-focused hedge funds. I find, relative to an augmented Fung and Hsieh (2004) factor model, that hedge funds with a physical presence (head or research office) in their investment region outperform other hedge funds by 3.72% per year. The local information advantage is pervasive across all major geographical regions, but is strongest for emerging market funds and funds holding illiquid securities. These results are robust to adjustments for fund fees, serial correlation, backfill bias, and incubation bias. I show also that distant funds, especially those based in the United States and the United Kingdom, are able to raise more capital, charge higher fees, and set longer redemption periods, despite their underperformance relative to nearby funds. It appears that distant funds trade investment performance for better access to capital. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

Share Restrictions, Risk Taking and Hedge Fund Performance

This paper examines the impact of share restrictions on the risktaking and on the performance of hedge funds using the Hedge Fund Research (HFR) database. Share restrictions, in the form of longer lockup as well as notice and redemption periods, provide ‡exibility for the managers. Our results suggest that this ‡exibility allows hedge fund managers with lockup provision to take an excess risk, which is not compensated, when performance is measured as a unit of the risk taken by the manager. Speci…cally, we …nd that typical hedge funds with a lockup provision deliver 5.6-6.6% (3.8-5.4%) lower Fung and Hsieh (2004) appraisal ratios (Sharpe ratios) compared to their peers without a lockup provision. The results remain consistent even after controlling for various database biases, alternative risk and performance measures, and nonlinearities in hedge fund returns.