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

Synthetic hedge funds

We provide evidence on the performance and the replication success of a broad sample of 72 synthetic hedge funds from January 2009 to December 2013. Thereby, we assign the term “synthetic hedge fund” to mutual funds and exchange-traded funds with hedge fund indices as their benchmarks. Replication success is measured through different perspectives from distributional characteristics to risk-adjusted performance. We find an overall significant underperformance of synthetic hedge funds compared to an appropriate benchmark index. Furthermore, mutual funds (associated with active portfolio management) can produce return characteristics closer to hedge fund benchmarks than exchange-traded funds (associated with passive management) can. From a single strategy perspective, we find a picture of heterogeneity. Regarding the market environment, we show larger return differences for unusual market conditions than for regular ones.     

Performance analysis of a collateralized fund obligation (CFO) equity tranche

This article examines the performance of the junior tranche of a collateralized fund obligation (CFO), i.e. the residual claim (equity) on a securitized portfolio of hedge funds. We use a polynomial goal programming model to create optimal portfolios of hedge funds, conditional to investor preferences and diversification constraints (maximum allocation per strategy). For each portfolio, we build CFO structures that have different levels of leverage, and analyze both the stand-alone performance as well as potential diversification benefits (low systematic risk exposures) of investing in the equity tranche of these structures. We find that the unconstrained mean-variance portfolio yields a high performance, but greater exposure to systematic risk. We observe the exact opposite picture in the case of unconstrained optimization, where a skewness bias is added, thus proving the existence of a trade-off between stand-alone performance and low exposure to systematic risk factors. We provide evidence that leveraged exposure to these hedge fund portfolios through the structuring of CFOs creates value for the equity tranche investor, even during the recent financial crisis.     

Style analysis and Value-at-Risk of Asia-focused hedge funds

In this paper we identify risk factors for Asia-focused hedge funds through a modified style analysis technique. Using an Asian hedge fund index, we find that Asian hedge funds show significant positive exposures to emerging equity markets. For both a static and rolling period style analysis, our model provides a high explanatory power for returns of the considered hedge fund index. We further conduct a Value-at-Risk analysis using the results of a rolling window style analysis as inputs. Our findings suggest that the considered parametric models outperform a simple historical simulation that is purely based on past return observations.     

Diversification in the hedge fund industry

We provide evidence of a significant relation between diversification and performance in the hedge fund industry. Measuring diversification across four distinct dimensions, we find a significant positive relation between hedge fund performance and diversification across sectors and asset classes. We show that on a risk adjusted basis, hedge funds that diversify across sectors and asset classes outperform other funds by an average of 1.1% per year. However, diversification across styles and geographies exhibits a significant negative association with hedge fund returns. Funds that diversify across styles and geographies underperform other funds by an average of 1% per year. For fund of hedge funds, we find a significant positive relation between performance and diversification across sectors. However, diversifying across asset classes and geographies is found to exhibit a negative relation with fund performance. Finally, we find that the motive to engage in diversification is consistent with managerial incentive structure in the hedge fund industry.     

Measuring the market impact of hedge funds

Hedge funds often employ opportunistic trading strategies on a leveraged basis. It is natural to find their footprints in most major market events. A “small bet” by large hedge funds can be a sizeable transaction that can impact a market. This study estimates hedge fund exposures during a number of major market events. In some episodes, hedge funds had significant exposures and were in a position to exert substantial market impact. In other episodes, hedge fund exposures were insignificant, either in absolute terms or relative to other market participants. In all cases, we found no evidence of hedge funds using positive feedback trading strategies. There was also little evidence that hedge funds systematically caused market prices to deviate from economic fundamentals.     

Hedge fund strategies,performance &diversification: A portfolio theory & stochastic discount factor approach

For 5500 North American hedge funds following 11 different strategies, we analyse the stand-alone performance of these strategies using a stochastic discount factor approach. Employing the same data, we then consider the diversification benefits of each hedge fund strategy when combined with a portfolio of US equities and bonds. We compute the out-of-sample Black-Litterman portfolios, with Bayes-Stein, higher moments, simulations, desmoothed data and allowance for regimes as robustness checks. All but two hedge fund strategies out-perform the market as stand-alone investments; and all but one provide significant diversification benefits. The higher is an investor’s risk aversion, the more beneficial is diversification into hedge funds.     

Funds of hedge funds: performance, risk and capital formation 2005 to 2010

Using a comprehensive data set of funds-of-hedge funds, we extend the results of Fung et al. (J. Finance 63:1777–1803, 2008) (FHNR) with an augmented version of the Fung and Hsieh (Financ. Anal. J. 60:65–80, 2004a; J. Empir. Finance 18:547–569, 2004b) model to document performance characteristics from January 2005 to December 2010. We find that our sample period is divided into three distinct subperiods: January 2005 to June 2007 (pre-subprime crisis); July 2007 to March 2009; and April 2009 to December 2010 (post-credit crunch) during which the average fund of hedge funds delivered positive alpha only in the first subperiod. We divide the funds of hedge funds sample into those who have alpha and the rest, which we call beta-only. The empirical results show a dramatic decline in the population of alpha producing funds of hedge funds post 2008 compared to the FHNR findings. When we repeat our analysis with a synthetic hedge fund index replicator, we find qualitatively similar results.     

Mutual fund performance when parent firms simultaneously manage hedge funds

This study examines the performance of mutual funds managed by firms that simultaneously manage hedge funds. We find that the reported returns of mutual funds in these “side-by-side” associations with hedge funds significantly underperformed those of mutual funds that shared similar fund and family characteristics but differed in that they were not affiliated with hedge funds. Digging deeper into performance, we find that the underperformance was confined to return gaps, a return measure that captures the impact of unobservable managerial actions. Interestingly, mutual funds with investment styles that were most closely aligned to affiliated hedge funds generated reported-return alphas and return gaps that underperformed by the greatest amount. Finally, we find that side-by-side mutual funds received less of a contribution to performance from IPO underpricing than similar unaffiliated mutual funds or affiliated hedge funds. Evidence does not support the hypothesis that affiliations with hedge funds allow side-by-side mutual funds to attract superior stock-picking talent. Our evidence does not allow us to rule out the possibility that management firms maximized fee income by strategically transferring performance from mutual funds to hedge funds.     

Style chasing by hedge fund investors

This paper examines whether investors chase hedge fund investment styles. We find that better-performing and more popular styles are rewarded with higher inflows in subsequent periods. This indicates that investors compare hedge fund styles in terms of recent performance and popularity, and they subsequently reallocate funds from less successful to more successful styles. Furthermore, we find evidence of competition between individual hedge funds of the same style. Funds outperforming the other funds in their styles and funds whose inflows exceed the average flows in their styles experience higher inflows in subsequent periods. One of the reasons for competition among same-style funds is investors’ search for the best managers. The high minimum investment required to invest in a hedge fund limits investors’ diversification opportunities and makes this search particularly important. Finally, we show that hedge fund investors’ implementation of style chasing in combination with intra-style fund selection represents a smart strategy.     

Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.     

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds’ ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.     

The role of hedge funds as primary lenders

We examine the role of hedge funds as primary lenders to corporate firms. We investigate both the reasons and the implications of hedge funds’ activities in the primary loan market. We examine the characteristics of firms that borrow from hedge funds and find that borrowers are primarily firms with lower profitability, lesser credit quality, and higher asymmetric information. Our results suggest that hedge funds serve as lenders of last resort to firms that may find it difficult to borrow from banks or issue public debt. We also examine the effect of hedge fund lending on the borrowing firms and find that borrowers’ profitability and creditworthiness improve subsequent to the loan. This beneficial effect of hedge fund lending is corroborated by our finding of positive abnormal returns for borrowers’ stocks around the loan announcement date. Overall, our findings are consistent with hedge funds adding value through their lending relationships and financial markets perceiving these activities as good news for the firms.     

Performance measurement of hedge funds using data envelopment analysis

Data envelopment analysis (DEA) is a nonparametric method from the area of operations research that measures the relationship of produced outputs to assigned inputs and determines an efficiency score. This efficiency score can be interpreted as a performance measure in investment analysis. Recent literature contains intensive discussion of using DEA to measure the performance of hedge funds, as this approach yields some advantages compared to classic performance measures. This paper extends the current discussion in three aspects. First, we present different DEA models and analyze their suitability for hedge fund performance measurement. Second, we systematize possible inputs and outputs for DEA and again examine their suitability for hedge fund performance measurement. Third, two rules are developed to select inputs and outputs in DEA of hedge funds. Using this framework, we find a completely new ranking of hedge funds compared to classic performance measures and compared to previously proposed DEA applications. Thus, we propose that classic performance measures should be supplemented with DEA based on the suggested rules to fully capture hedge fund risk and return characteristics.      

Trapped in diversification – another look at the risk of fund of hedge funds

Recent literature implies that despite being more diversified, fund of hedge funds (FOFs) are exposed to tail risk. We propose an explanation for this phenomenon; tail risk is a systematic risk factor for hedge funds, which by construction, explains the higher portion of the returns in the diversified portfolios. Our study suggests that not only an additional tail risk factor improves the explanatory power of the factor model, the relative importance of tail risk factor increases with the number of underlying hedge funds in an FOF portfolio. Furthermore, we demonstrate that FOFs with a short history, higher management fees, leverage and requiring shorter lockup periods are more sensitive to tail risk.     

The Italian hedge funds industry: An empirical analysis of performance and persistence

This study analyses Italian hedge funds performance and persistence. The peculiarity of the Italian hedge fund industry is that 95% of the hedge funds are funds of hedge funds (FoHF), whereas only 5% of them employ other investment styles. Using monthly data on FoHF provided by MondoHedge, we examine the impact of both market variables and funds’ own characteristics on funds performance using panel data. We find that the European, the Japanese and the emerging markets equity markets, and the commodity market have a positive impact on Italian FoHF performances, while the US Bond Market negatively affects them. Moreover, we find performance fees and notice days to have a negative impact on funds performances. Finally, we test the presence of performance persistence. Employing two different nonparametric methods, we find that funds performances are persistent on a monthly and quarterly basis, while the regression-based parametric method provides evidence of persistence only on a monthly basis.     

The risk in hedge fund strategies: Theory and evidence from long/short equity hedge funds

Theory suggests that long/short equity hedge funds' returns come from directional as well as spread bets on the stock market. Empirical analysis finds persistent net exposures to the spread between small vs large cap stocks in addition to the overall market. Together, these factors account for more than 80% of return variation. Additional factors are price momentum and market activity. Combining two major branches of hedge fund research, our model is the first that explicitly incorporates the effect of funding (stock loan) on alpha. Using a comprehensive dataset compiled from three major database sources, we find that among the three thousand plus hedge funds with similar style classification, less than 20% of long/short equity hedge funds delivered significant, persistent, stable positive non-factor related returns. Consistent with the predictions of the Berk and Green (2004) model we find alpha producing funds decays to “beta-only” over time. However, we do not find evidence of a negative effect of fund size on managers' ability to deliver alpha. Finally, we show that non-factor related returns, or alpha, are positively correlated to market activity and negatively correlated to aggregate short interest. In contrast, equity mutual funds and long-bias equity hedge funds have no significant, persistent, non-factor related return. Expressed differently, L/S equity hedge funds, as the name suggests, do benefit from shorting. Besides differences in risk taking behavior, this is a key feature distinguishing L/S funds from long-bias funds.     

Stochastic dominance analysis of Asian hedge funds

We employ the stochastic dominance approach that utilizes the entire return distribution to rank the performance of Asian hedge funds as traditional mean-variance and CAPM approaches could be inappropriate given the nature of non-normal returns. We find both first-order and higher-order stochastic dominance relationships amongst the funds and conclude that investors would be better off by investing in the first-order dominant funds to maximize their expected wealth. By investing in higher-order dominant funds, risk-averse investors can maximize their expected utilities but not their wealth. In addition, we find the common characteristic for most pairs of funds is that one fund is preferred to another in the negative domain whereas the preference reverses in the positive domain. We conclude that the stochastic dominance approach is more appropriate compared with traditional approaches as a filter in hedge fund selection. Compared with traditional approaches, the SD approach, not only is assumption free, but also provides greater insights to the performance and risk inherent in a hedge fund's track record.     

Commonality in hedge fund returns: Driving factors and implications

We measure the commonality in hedge fund returns, identify its main driving factor and analyze its implications for financial stability. We find that hedge funds’ commonality increased significantly from 2003 until 2006. We attribute this rise mainly to the increase in hedge funds’ exposure to emerging market equities, which we identify as a common factor in hedge fund returns over this period. Our results show that funds with a high commonality were affected disproportionately by illiquidity and exhibited negative returns during the subsequent financial crisis, thereby providing little diversification benefits to the financial system and to investors.     

Does the choice of performance measure influence the evaluation of hedge funds?

The Sharpe ratio is adequate for evaluating investment funds when the returns of those funds are normally distributed and the investor intends to place all his risky assets into just one investment fund. Hedge fund returns differ significantly from a normal distribution. For this reason, other performance measures for hedge fund returns have been proposed in both the academic and practice-oriented literature. In conducting an empirical study based on return data of 2763 hedge funds, we compare the Sharpe ratio with 12 other performance measures. Despite significant deviations of hedge fund returns from a normal distribution, our comparison of the Sharpe ratio to the other performance measures results in virtually identical rank ordering across hedge funds.