Funds Of Hedge Funds Research Articles

Indirect effects of flow-performance sensitivity on fund performance

This study examines changes in the flow-performance sensitivity (FPS) among funds of hedge funds (FoHFs) after the global financial crisis and the Madoff scandal. We also explore whether these changes in FPS affect fund performance. After the crisis, investors in FoHFs began to prioritize fund reliability over fund performance, particularly in the case of young and small FoHFs with limited track records and investor bases. As a result, the overall FPS of FoHFs decreased in the post-crisis period, primarily due to decreases in FPS among young and small FoHFs. Furthermore, we observe that funds with lower sensitivity exhibit significantly worse performance than those with higher sensitivity. These results are robust to both risk-adjusted and relative performance measures. Our findings suggest that the performance-based incentive structure prevalent in the hedge fund industry imperfectly aligns fund managers’ interests with those of investors and that FPS plays a positive role in driving fund performance.

Different in Nature, Common in Style: View Commonality of Single Hedge Funds and Funds of Hedge Funds

Using style analysis, the authors show the common factor exposures of single hedge funds and funds of hedge funds (FoHFs). Despite the different nature and characteristics of the two categories, there are significant style similarities through the different business cycles. The authors argue that in general, FoHFs use substantially more leverage than single hedge fund strategies and found a positive trade-off between leverage and style exposure for the former and a negative one for the latter. Furthermore, the style in both category changes according to the business cycle, with single hedge fund strategies having more flexibility in allocation and aggressive style drifts. However, the similarities in style exposure increase in times of financial turmoil. Additionally, the exposure in equity and bond-related factors are significant and identify style drifts from the past. Finally, the authors found that both categories engage most in equity markets related factors—Betting Against Beta factors (BAB), High minus Low (HML) portfolios sorted by book-to-market to indicate value, Small minus Big (SMB) portfolios sorted by market-cap to indicate size, and Conservative minus Aggressive (CMA) portfolios. <b>TOPICS:</b>Real assets/alternative investments/private equity, portfolio construction, factor-based models, performance measurement <b>Key Findings</b> • In spite of their different natures, single funds and FoHFs show striking style similarity with significant exposure to the equity markets. Their style is largely determined by dynamically shifting between long and short exposure to the factors HML, SMB, and BAB. • FoHFs have much stronger absolute factor exposure, which is partly determined by the large amounts of leverage used. • The similar style exposure spikes in times of financial stress. However, single strategies show larger advantages in exploiting business cycles, flexible factor allocation, and excess returns.

Funds of hedge funds: Are they really the high society for little guys?

Funds of hedge funds (FOFs) have been depicted as a type of conservative vehicle to many institutional and retail investors for accessing hedge fund investment. We challenge this view by investigating the role of tail risk exposure in FOFs. We find that the tail risk exposure determines a fund’s return and significantly influences a fund’s performance over one to three-month horizon. In particular, we find that the marginal extreme losses to one unit change of tail risk exposure in bear markets nearly double the marginal extreme gains in bull markets. This non-linear payoff structure must be evaluated carefully by anyone who wishes to invest in FOFs.

Fund selection, style allocation, and active management abilities: Evidence from funds of hedge funds’ holdings

AbstractThis study examines whether funds of hedge funds (FOHFs) provide superior before‐fee performance through managers’ fund selection, style allocation, and active management abilities. Using reported holdings of Securities and Exchange Commission–registered FOHFs, we find that FOHF managers have fund selection abilities, as hedge funds held by FOHFs outperform their style indices and over half of the individual hedge funds in the Lipper Trading Advisor Selection System (TASS) database. We also find that FOHF managers add value through active management of FOHFs’ holdings, while evidence on their style allocation abilities is mixed. Our findings suggest that FOHFs generate superior before‐fee performance and that FOHF managers’ skillset is broader than previously documented. Thus, our study helps explain why FOHFs continue to survive and suggests that FOHF fee structure reform merits consideration.

Fund Selection and Style Allocation Abilities: Evidence from Funds of Hedge Fundss Holdings

This study examines whether funds of hedge funds (FOHFs) provide superior before-fee performance through managers’ fund selection, style allocation, and active management abilities. Using reported holdings of SEC-registered FOHFs, we find that FOHF managers have fund selection abilities, as hedge funds held by FOHFs outperform their style indices and over half of the individual hedge funds in the TASS database. We also find that FOHF managers add value through active management of FOHFs’ holdings, while evidence on their style allocation abilities is mixed. Our findings suggest that FOHFs generate superior before-fee performance and that FOHF managers’ skillset is broader than previously documented. Thus, our study helps explain why FOHFs continue to survive and suggests that FOHF fee structure reform merits consideration.

The profiles of merged hedge funds, funds of hedge funds, and CTA

This study examines the profiles of 412 merged hedge funds (HFs), funds of HFs (FoHFs), and commodity trading advisors (CTAs) over the January 1994 through December 2013 period. We find that merged HFs, FoHFs, and CTAs are typically younger than live HFs but older than dead funds. We also find that merged HFs, FoHFs, and CTAs have relatively fewer constraints than live and dead ones. Specifically, they have shorter lockup period, use less leverage and request less value of purchasing initial investments or units. In addition, we find that past performance is a significant determinant for mergers. Thus, HF, FoHF, and CTA mergers could be motivated by a need to eradicate bad performing funds. These results are robust to different performance measures and hold after correcting for backfill bias and removing the effects of artificial serial correlation.

Liquidity Risk and Economies of Scale in Fundsof Hedge Funds

Using data from the Lipper/TASS hedge fund database over the period 1994–2011, the authors examine the effect of liquidity risk on the relationship between size and performance for funds of hedge funds (FOFs). After confirming a significant positive size effect for FOFs, they explicitly introduce liquidity risk and find that this scale effect becomes more pronounced among FOFs with lower liquidity risk. To the extent that more illiquid FOFs exhibit higher liquidity risk, the results provide evidence in support of the liquidity hypothesis that size does not erode, but instead helps improve, performance, since FOFs do not directly manage portfolios of securities and thus are less affected by the liquidity costs associated with trading a large portfolio.

Time-Varying Style Analysis and the Leverage of Funds of Hedge Funds

20 years ago, Sharpe (1992) developed the Style Analysis for mutual funds; in this analysis, the weights mutual funds allocate to major asset classes are constrained to sum up to 1. In this paper we develop a Time-Varying Style Analysis (TVSA) in which the weights must sum up to 1 but are allowed to vary with time, and show its interest in the world of Funds of Hedge Funds (FoHFs). We show that this approach sheds light on the leverage utilized by funds of hedge funds and subsequently use this estimate to determine the impact of leverage on future performance. Using predictive multivariate analysis, we show that the impact of leverage is negative, effect that, to the best of our knowledge, has never been reported. We also show that TVSA leads to lower median tracking error than the classical style analysis and allow us to determine an estimate of the aggregate leverage of the FoHF industry. Moreover, applying Darolles and Vaissie (2011) decomposition to our results confirms that funds of hedge fund picking skills are not enough to compensate for the fees they charge. Our results prove to be robust to adjustments for backfilling, survivorship and autocorrelation bias and to various robustness checks.

Risk and Return in Hedge Funds and Funds of Hedge Funds: A Cross-Sectional Approach

The objective of this paper is to examine whether the available data on hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find the best risk measure that captures the cross-sectional variation in HF and FOHF returns. Using the “live funds” and the “dead funds” datasets provided by Hedge Fund Research Inc. (HFR), I concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs.

Risk and Return in Hedge Funds and Funds-of-Hedge Funds: A Cross-Sectional Approach

The objective of this study is to examine whether the available data on individual hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find an appropriate risk measure that captures the cross-sectional variation in HF and FOHF returns and compare the risk-return relationship in HFs and FOHFs. Using the “live funds” and the “dead funds” datasets provided by Hedge Fund Research Inc. (HFR), we concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures are analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures are compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results show that the available data on HFs and FOHFs exhibits different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs are found, it is difficult to determine the risk measures that best capture the cross-sectional variation in FOHF returns.

Assessing the Performance of Funds of Hedge Funds

This paper studies the performance of a sample of funds of hedge funds (FoHFs) from January 1994 to August 2009. We apply the false discoveries (FD) technique of Barras, Scaillet and Wermers (2010) to separate the FoHFs into skilled, zero-alpha and unskilled. We measure the alpha of the FoHFs using two models – (1) a 16-factor model with a combination of factors from Fung and Hsieh (2004) and Capocci, Corhay and Hubner (2005) and (2) a 13-factor model of hedge fund indices from Dow Jones Credit Suisse. Applying the FD procedure to the first model, we find that, after fees, the majority of FoHFs do not channel alpha from single-manager hedge funds. Applying the FD procedure to the second model, we find that only a very small fraction of FoHFs deliver after-fees alpha per se, i.e. on top of the alpha of the hedge fund indices. A series of robustness checks confirms the results of the FD procedure. We also compare the performance of our sample of FoHFs to artificial FoHFs constructed by randomly picking hedge funds. The lack of significant differences in the average performance of the real and artificial FoHFs confirms the results obtained by the FD procedure.

Diversification in Funds of Hedge Funds: Is It Possible to Overdiversify?

Many institutions are attracted to diversified portfolios of hedge funds, referred to as Funds of Hedge Funds (FoHFs). In this article, we examine a new database that separates out for the first time the effects of diversification (the number of underlying hedge funds) from scale (the magnitude of assets under management). We find with others that the variance-reducing effects of diversification diminish once FoHFs hold more than 20 underlying hedge funds. This excess diversification actually increases their left-tail risk exposure once we account for return smoothing. Furthermore, the average FoHF in our sample is more exposed to left-tail risk than are naive $1/N$ randomly chosen portfolios. This increase in tail risk is accompanied by lower returns, which we attribute to the cost of necessary due diligence that increases with the number of hedge funds.

Diversification in Funds of Hedge Funds: Is it Possible to Overdiversify?

Samuelson (1967) argues that as a general matter it is easy to show that investors should be maximally diversified. For this reason many institutions are attracted to diversified portfolios of hedge funds, referred to as Funds of Hedge Funds (FOFs). In this paper we examine a new database that separates out for the first time the effects of diversification (the number of underlying hedge funds) from scale (the magnitude of assets under management). We find with others that the variance reducing effects of diversification peter out once FOFs hold more than 20 underlying hedge funds. Yet the majority of FOFs are more diversified than this. We find a new and surprising result that this excess diversification actually increases the left tail risk exposure of FOFs particularly once we account for the extent to which hedge fund returns are smoothed. Furthermore the average FOF in our sample is more exposed to left tail risk than are naive 1/N randomly chosen portfolios. This increase in tail risk is accompanied by lower returns which we attribute to the cost of necessary due diligence which increases with the number of underlying hedge funds in the FOFs .

A Comparison of the European and U.S. Funds of Hedge Funds Industries

This article empirically investigates regional differences in the performance of funds of hedge funds (FHFs). It uses descriptive variables and time series for 533 FHFs from the Lipper TASS Hedge Fund Database and segregates them into regional peer groups. The article shows that fees are generally higher in Europe than in the U.S., that redemption periods are longer in the U.S. than in Europe, and that assets under management tend to be higher in Europe. Additionally, we find that European-based FHFs exhibit a stronger home bias than their U.S. counterparts and that U.S.-based FHFs appear to invest more heavily in emerging hedge fund managers.

The Value-Added of Investable Hedge Fund Indices

This article empirically investigates the risk and performance of three types of alternative beta products over the January 2002 to September 2009 time period: funds of hedge funds (FHFs), investable hedge fund indices (IHFIs), and hedge fund replication strategies (HFRS). The authors show that IHFIs are true alternative beta products with high correlations and beta to non-investable hedge fund indices. Their results further suggest that, in a best case scenario, IHFIs outperform FHFs and HFRS on a risk-adjusted basis. However, in the worst case scenario, IHFIs underperform both investments. If one takes the average of all IHFIs, one finds that they perform equally as well as FHFs. Hence, IHFIs constitute a solid alternative to FHF investments, while costing substantially less, and offering generally more transparency and liquidity. The authors propose that fee-sensitive investors especially should consider taking a core-satellite approach to their hedge fund portfolio, with the core represented by cheap passive hedge fund beta through IHFIs, and the satellite represented by more expensive and actively managed alpha-generating FHFs. <b>TOPICS:</b>Real assets/alternative investments/private equity, mutual funds/passive investing/indexing, performance measurement

Dynamic Prediction of Financial Distress in Hedge Funds and Funds-of-Hedge Funds

This study establishes survival/hazard models with time-varying covariates, as well as fixed covariates, under three specifications of the Cox Proportional Hazards (CPH) model for Hedge Fund (HFs) and Funds-of-Hedge Funds (FOHFs). With the development of the SAS Macro program for generating survival probabilities, the dynamic changes in survival probabilities are predicted over the lifetime of HFs and FOHFs after the estimation of baseline hazards using mixed CPH model. This model incorporates both fixed and time-varying covariates. The resulting dynamic survival probabilities show that the mixed model developed in this study is effective for real-time prediction of the cumulative survivals of HFs and FOHFs. The estimated models exhibit satisfactory predictive accuracy in forecasting the occurrence of failures in HFs and FOHFs.

Selecting Prior Year’s Top Fund of Hedge Funds as This Year’s Choice

Can investors select winning funds of hedge funds (FOFs) by merely assuming a simple trading strategy? Can historical information present insight on future returns? Financial theory presumes that stock markets are efficient and using any type of trading strategy will not be successful in the long-run. This article investigates a pure simple trading strategy to see if selecting last year’s top-performing FOFs as this year’s choice can outperform three FOF indexes and the S&amp;P 500 Index. We further apply the strategy to the top-performing onshore and offshore funds, respectively, and compare them to the HFR Onshore and HFR Offshore indices, respectively. We generally find that selecting (up to) the six best performing FOFs is a winning strategy that outperforms both the market and the benchmark FOFs indices. There appears to be little benefit to over-diversifying in excess of this number. This finding applies both to onshore and offshore FOFs. <b>TOPICS:</b>Real assets/alternative investments/private equity, performance measurement

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

The Risk of Funds of Hedge Funds: An Empirical Analysis of the Maximum Drawdown

Funds of hedge funds (FHF) are perceived to be the premier choice for institutional investors looking to enter the alternative investment asset class. This article empirically investigates the maximum drawdowns of FHF. It analyzes the time series and descriptive variables of 649 FHFs from the Lipper TASS Hedge Fund database for January 1996 through August 2007. The empirical results suggest 1) the number and the magnitude of drawdowns decreases as the experience of the FHF increases, 2) the average recovery time is higher for older FHFs, 3) there is no difference between small and large FHFs in regards to the magnitude of a maximum drawdown, 4) the higher a maximum drawdown of a FHF, the longer it takes to recover, and 5) most maximum drawdowns occur during times of financial market turmoil. Thus, our findings question especially the acclaimed ability of FHFs to deliver absolute returns. We also show that the beta risks involved with FHFs are high. We find that the advantages of FHFs are more likely to be in their low long-term correlations with traditional asset classes, as well as in their low volatility. <b>TOPICS:</b>Real assets/alternative investments/private equity, risk management, performance measurement

Risk and Return in Hedge Funds and Funds of Hedge Funds: A Cross-Sectional Approach

The objective of this study is to examine whether the available data on individual hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find an appropriate risk measure that captures the cross-sectional variation in HF and FOHF returns and compare the risk-return relationship in HFs and FOHFs. Using the “live funds” and the “dead funds” datasets provided by Hedge Fund Research Inc. (HFR), we concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures are analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures are compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results show that the available data on HFs and FOHFs exhibits different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs are found, it is difficult to determine the risk measures that best capture the cross-sectional variation in FOHF returns.