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Hedge fund replication gained considerable attention during the period surrounding 2007 when it was anticipated to become for hedge fund investors what index funds are for equity investors. The hedge fund replication concept only lacked a track record. This paper aims to present an updated evaluation.

Performance is evaluated on both a raw‐return basis and a risk‐adjusted basis using Fung and Hsieh's 8‐factor model. Particular emphasis is given to analyzing the performance of these products during the financial crisis and to highlighting the specific characteristics that distinguished them from their hedge fund cousins during this period.

The results show that the hedge fund replication space is definitely proving its existence as a credible hedge fund investment alternative. Talented hedge fund managers will always be in high demand, but they may have to just prove their high compensation a bit harder.

Although this study is based on a short sample period, the results indicate that hedge fund replication products delivered returns that were at par with the returns of hedge funds. The replication products performed comparatively well during the crisis probably as a result of having less exposure to illiquid assets.

To the best of the authors' knowledge, this article uses the most extensive data set of 22 hedge fund replication products to analyze their performance.

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

Good market timing skills can be an important factor contributing to hedge funds’ outperformance. In this chapter, we use a unique semiparametric panel data model capable of providing consistent short period estimates of the return correlations with three market factors for a sample of Long/Short equity hedge funds. We find evidence of significant market timing ability by fund managers around market crisis periods. Studying the behavior of individual fund managers, we show that at the 10% significance level, 17.12% of funds exhibit good linear timing skills and 21.32% of funds possess some level of good nonlinear market timing skills. Further, we find that market timing strategies of hedge funds are different in good and bad markets, and that a significant number of managers behave more conservatively when the market return is expected to be far above average and more aggressively when the market return is expected to be far below average. We find that good market timers are also likely to possess good stock selection skills.

Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

This study examines several aspects of active portfolio management by equity hedge funds between 1996 and 2013. Consistent with the idea that cross-sectional return dispersion is a proxy for the market’s available alpha, our results show that equity hedge funds achieve their strongest performance during periods of elevated dispersion. The performance advantage is robust to numerous risk adjustments. Portfolio managers may use the current month’s dispersion to plan the extent to which the following month’s investment approach will be active or passive. We also estimate the active share for equity hedge funds and find an average of 53%. We further document the average annual expense ratio for managing hedge funds’ active share to be about 7%. This figure is remarkably close to active expense ratios reported previously for equity mutual funds, which may be interpreted as evidence of uniform pricing for active portfolio management services.

The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

This research aims to examine hedge funds’ performance, risk and flow before and after the implementation of the Stop Trading on Congressional Knowledge (STOCK) Act.

This paper includes the use of different factor models to highlight the performance and risk of hedge funds before and after the implementation of the STOCK Act. Hedge fund holdings are retrieved from Thomson Reuters Lipper Hedge Fund Database (TASS).

This study finds significant differences before and after the implementation of the STOCK Act. The results for the entire sample period indicate that hedge funds suffered lower-alpha, standard deviation and idiosyncratic risk after the implementation of the STOCK Act.

The paper’s originality and value lie in addressing the relationship gap between the STOCK Act and hedge fund performance.

The purpose of this paper is to investigate to what extent hedge funds are subject to irrationality in their investment decisions. The authors advance the hypothesis that irrational behavior affects hedge fund returns despite their sophistication and active management style.

The irrational component may follow a pattern consistent with the observed hedge fund returns yet far distant from market fundamentals. The authors include factors beyond the original version of capital asset pricing model such as Fama and French and Carhart models, as well as less stringent models, such as APT and Fung and Hsieh, to test whether these models are able to capture the irrational nature of the residuals.

After finding that institutional irrational sentiments play a role in hedge fund returns, we note that the returns are not completely shielded against irrational trading; however, hedge fund returns appear to be affected only by the irrational component derived from institutional trading rather than that emanated from individuals.

Different sources of irrationality may have asymmetric effects on hedge fund returns. Using a different set of sophisticated investors along with different market sentiment proxies may yield different results.

The authors argue that investors can use irrational beta to gauge the extent of institutional irrational sentiments prevailing in markets for the purpose of re-adjusting their portfolios and therefore use the betas as an early warning sign. It can also guide investors in avoiding funds and strategies that display greater irrational behavior.

The study advance the idea that the unexpected, hereafter irrational, component may follow a pattern consistent with the observed hedge fund returns, yet different from market fundamentals.

The purpose of this paper is to determine if hedge funds perform poorly as claimed by more recent research. The authors find hedge funds perform well from 2001 to 2013 when compared to sample of firms known to experience superior performance, namely, a sample of seasoned equity offerings (SEOs).

This paper uses a portfolio approach in comparing the performance of hedge funds and SEO firms. Other comparisons involve a number of common methodologies used to compute and analyze short-run and long-run returns.

Contrary to a growing and prevalent belief, the paper offers evidence hedge funds as a whole have performed well for a recent 13-year period. This finding includes periods up to six years around SEO announcement months.

This paper is limited to examining monthly returns for a portfolio of hedge funds. This limitation led to incorporating a portfolio approach.

The findings suggest that a portfolio of hedge funds are an important investment consideration. This consideration has practical implications because investing in a portfolio of hedge funds has become more available for all investors in recent years.

Society can be enhanced as this paper helps future investors make optimal investment decisions.

This paper adds to the hedge fund research by being the first paper to compare the performance of hedge funds with that for firms undergoing an important corporate event. The findings are new and can impact investment decision making.

This paper aims to investigate the use of the bias ratio as a possible early indicator of financial fraud – specifically in the reporting of hedge fund returns. In the wake of the 2008-2009 financial crisis, numerous hedge funds were liquidated and several cases of financial fraud exposed.

Risk-adjusted return metrics such as the Sharpe ratio and Value at Risk were used to raise suspicion for fraud. These metrics, however, assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions).

Results indicate that potential fraud would have been detected in the early stages of the scheme’s life. Having demonstrated the credibility of the bias ratio, it was then applied to several indices and (anonymous) South African hedge funds. The results were used to demonstrate the ratio’s scope and robustness and draw attention to other metrics which could be used in conjunction with it. Results from these multiple sources could be used to justify further investigation.

The traditional metrics for performance evaluation (such as the Sharpe ratio), assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions). The bias ratio, which does not rely on normally distributed returns, was applied to a known fraud case (Madoff’s Ponzi scheme).

The effectiveness of the bias ratio in demonstrating potential suspicious financial activity has been demonstrated.

The financial market has come under heightened scrutiny in the past decade (2005 – 2015) as a result of the fragile and uncertain economic milieu that still (2015) persists. Numerous risk and return measures have been used to evaluate hedge funds’ risk-adjusted performance, but many fail to account for non-normal return distributions exhibited by hedge funds. The bias ratio, however, has been demonstrated to effectively flag potentially fraudulent funds.