Systematic risk and the cross section of hedge fund returns

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.     

Macroeconomic risk and hedge fund returns

This paper estimates hedge fund and mutual fund exposure to newly proposed measures of macroeconomic risk that are interpreted as measures of economic uncertainty. We find that the resulting uncertainty betas explain a significant proportion of the cross-sectional dispersion in hedge fund returns. However, the same is not true for mutual funds, for which there is no significant relationship. After controlling for a large set of fund characteristics and risk factors, the positive relation between uncertainty betas and future hedge fund returns remains economically and statistically significant. Hence, we argue that macroeconomic risk is a powerful determinant of cross-sectional differences in hedge fund returns.     

Higher risk,lower returns: What hedge fund investors really earn

The returns of hedge fund investors depend not only on the returns of the funds they hold but also on the timing and magnitude of their capital flows in and out of these funds. We use dollar-weighted returns (a form of Internal Rate of Return (IRR)) to assess the properties of actual investor returns on hedge funds and compare them to buy-and-hold fund returns. Our main finding is that annualized dollar-weighted returns are on the magnitude of 3% to 7% lower than corresponding buy-and-hold fund returns. Using factor models of risk and the estimated dollar-weighted performance gap, we find that the real alpha of hedge fund investors is close to zero. In absolute terms, dollar-weighted returns are reliably lower than the return on the Standard & Poor's (S&P) 500 index, and are only marginally higher than the risk-free rate as of the end of 2008. The combined impression from these results is that the return experience of hedge fund investors is much worse than previously thought.     

The financial crisis and hedge fund returns

The financial crisis has focused the lens of politicians and regulators on hedge funds as a source of systemic and operational risk in asset markets. We examine the extent to which available data can provide useful information regarding the impact of hedge funds on the financial system. Using data from January 1994 through September 2008, we find dramatic changes in the exposures of hedge funds to risk factors, accompanied by a significant and widespread increase in correlation between hedge fund and factor returns. Lastly, the discontinuity at zero in the cross-sectional distribution of hedge fund returns persists throughout the sample.     

The price of skill: Performance evaluation by households

Skilled investors make money off uninformed investors. By acting as intermediaries, they provide a hedge to the uninformed investors themselves. I present a model in which households have imperfect information about expected returns. Non-traded income shocks lead them to rebalance, sometimes at the wrong time. Active funds hedge this risk by trading on superior information. In equilibrium, they pay off when non-traded income disappoints, earning a premium that makes them appear to underperform index funds after fees. Empirical results using aggregate fund flows support the model. A corresponding asset pricing test can account for the apparent underperformance of active funds.     

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.     

Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs

We develop a new factor selection methodology of spanning the space of hedge fund risk factors with all available exchange traded funds (ETFs). We demonstrate the efficacy of the methodology with out-of-sample individual hedge fund return replication by ETF clone portfolios. This is consistent with our interpretation of ETF returns as proxies to risk factors driving hedge fund returns. We further consider portfolios of “cloneable” and “noncloneable” hedge funds, defined as top and bottom in-sample R² matches, and demonstrate that our ETF clone portfolios slightly outperform cloneable hedge funds out of sample.     

Hedge Fund Indices: Reconciling Investability and Representativity

Following a growing concern among investors about the quality of hedge fund index return data, this paper addresses the question of whether designing hedge fund indices that fulfil the usual requirements (in particular representative and investable) is or not a feasible task, given a variety of features that are specific to that industry. To test whether or not investability should necessarily come at the cost of representativity, we use a well‐known methodology in the asset pricing literature based on the concept of factor replicating portfolios. Our results suggest that it is actually possible to construct representative indices based on a limited number of funds that are open to new investments, except perhaps in the case of equity market neutral strategies, provided that: i) these funds are suitably selected and ii) a portfolio is constructed with the objective of replicating the common trend in hedge fund returns for a given strategy. A range of robustness tests are performed that show that high correlation of the factor replicating portfolios with the common factor of returns for each strategy is remarkably stable with respect to modifying the number of funds in the replicating portfolio or changing the frequency of rebalancing.     

Value at risk and the cross-section of hedge fund returns

Using two large hedge fund databases, this paper empirically tests the presence and significance of a cross-sectional relation between hedge fund returns and value at risk (VaR). The univariate and bivariate portfolio-level analyses as well as the fund-level regression results indicate a significantly positive relation between VaR and the cross-section of expected returns on live funds. During the period of January 1995 to December 2003, the live funds with high VaR outperform those with low VaR by an annual return difference of 9%. This risk-return tradeoff holds even after controlling for age, size, and liquidity factors. Furthermore, the risk profile of defunct funds is found to be different from that of live funds. The relation between downside risk and expected return is found to be negative for defunct funds because taking high risk by these funds can wipe out fund capital, and hence they become defunct. Meanwhile, voluntary closure makes some well performed funds with large assets and low risk fall into the defunct category. Hence, the risk-return relation for defunct funds is more complicated than what implies by survival. We demonstrate how to distinguish live funds from defunct funds on an ex ante basis. A trading rule based on buying the expected to live funds and selling the expected to disappear funds provides an annual profit of 8–10% depending on the investment horizons.     

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.     

Conditional Properties of Hedge Funds: Evidence from Daily Returns*

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations.     

Speculative capital and currency carry trades

In this paper, we study a two-country general equilibrium model with partially segmented financial markets, where hedge funds emerge endogenously. Empirically, we show that the hedge fund investment strategy predicted by our model, which we call the “risk-adjusted carry trade” strategy, explains more than 16% of the overall hedge fund index returns and more than 33% of the fixed income arbitrage sub-index returns. The flow of new money to hedge funds affects market interest rates, exchange rates, and both the hedge funds’ contemporaneous and expected future returns as predicted by the model.     

On the characteristics and performance of long-short, market-neutral and bear mutual funds

We evaluate the return performance of long-short, market-neutral and bear mutual funds using multi-factor models and a conditional CAPM that allows for time-varying risk. Differences in the bearish posture of these mutual funds result in different performance characteristics. Returns to long-short mutual funds vary with the market, returns to market-neutral mutual funds are uncorrelated with the market and returns to bear mutual funds are negatively correlated. Using the conditional CAPM we document significant changes in the market-risk exposure of the most bearish of these funds during different economic climates. We then assess the flow-performance relationship for up to 60 months following up and down markets and find that investors direct flows towards market-neutral and bearish funds for several months after down markets. Market-neutral funds provide a down market hedge, but bear funds do not generate the returns that investors hope for.     

Share restrictions and asset pricing: Evidence from the hedge fund industry

This paper presents evidence on the relation between hedge fund returns and restrictions imposed by funds that limit the liquidity of fund investors. The excess returns of funds with lockup restrictions are approximately 4–7% per year higher than those of nonlockup funds. The average alpha of all funds is negative or insignificant after controlling for lockups and other share restrictions. Also, a negative relation is found between share restrictions and the liquidity of the fund's portfolio. This suggests that share restrictions allow funds to efficiently manage illiquid assets, and these benefits are captured by investors as a share illiquidity premium.     

Do hedge funds' exposures to risk factors predict their future returns?

This paper investigates hedge funds' exposures to various financial and macroeconomic risk factors through alternative measures of factor betas and examines their performance in predicting the cross-sectional variation in hedge fund returns. Both parametric and non-parametric tests indicate a significantly positive (negative) link between default premium beta (inflation beta) and future hedge fund returns. The results are robust across different subsample periods and states of the economy, and after controlling for market, size, book-to-market, and momentum factors as well as the trend-following factors in stocks, short-term interest rates, currencies, bonds, and commodities. The paper also provides macro-level and micro-level explanations of our findings.     

HEDGE FUND SURVIVAL: NON-NORMAL RETURNS, CAPITAL OUTFLOWS, AND LIQUIDITY

We analyze the factors that influence the survival probability of hedge funds reported in the Lipper TASS database. Particular emphasis is placed on (1) non-normality of returns and assets under management (AUM), (2) short-term capital outflows, and (3) liquidity constraints associated with a hedge fund's cancellation policy. Estimation results using the Cox proportional hazards model and the panel logit model show that (1) funds with lower skewness in returns and AUM, (2) funds experiencing instantaneous rapid capital outflows, and (3) funds with a shorter redemption notice period and a higher redemption frequency have significantly higher liquidation probabilities, among others.     

The liquidity risk of liquid hedge funds

This paper evaluates hedge funds that grant favorable redemption terms to investors. Within this group of purportedly liquid funds, high net inflow funds subsequently outperform low net inflow funds by 4.79% per year after adjusting for risk. The return impact of fund flows is stronger when funds embrace liquidity risk, when market liquidity is low, and when funding liquidity, as measured by the Treasury-Eurodollar spread, aggregate hedge fund flows, and prime broker stock returns, is tight. In keeping with an agency explanation, funds with strong incentives to raise capital, low manager option deltas, and no manager capital co-invested are more likely to take on excessive liquidity risk. These results resonate with the theory of funding liquidity by Brunnermeier and Pedersen (2009).     

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.     

Capacity constraints,investor information,and hedge fund returns

To identify capacity constraints in hedge funds and simultaneously gauge how well-informed hedge fund investors are, we need measures of investor demand that do not affect deployed hedge fund assets. Using new data on investor interest from a secondary market for hedge funds, this paper verifies the existence of capacity constraints in hedge funds. There is more mixed evidence on the information available to hedge fund investors. Buy and sell indications arrive following fund outperformance. While buy indications have little incremental power to predict hedge fund performance over and above well-known forecasting variables, sell indications do somewhat better.     

Hedge Fund Regulation and Misreported Returns

This paper introduces a cross‐country law and finance analysis of the misreporting behaviour in the hedge fund industry in terms of smoothing returns so that a fund consistently generates positive returns. We find strong evidence that international differences in hedge fund regulation are significantly associated with the propensity of fund managers to misreport monthly returns. We find a positive association between wrappers and misreporting, particularly for funds that do not have a lockup provision. Also, we find some evidence that misreporting is less common among funds in jurisdictions with minimum capitalisation requirements and restrictions on the location of key service providers. We assess the robustness of our finds to a number of specifications, including, different specifications of misreporting bin widths, subsets of the data by fund type, as well as specifications controlling for collinearity and selection effects and other robustness checks. We show misreporting significantly affects capital allocation, and calculate the wealth transfer effects of misreporting and relate this wealth transfer to differences in hedge fund regulation.