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.     

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.     

Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach

Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward–backward algorithm. Existing methods of testing for structural breaks are also used for comparison. We investigate the presence of structural breaks in several hedge fund indices; our results are consistent with market events and episodes that caused substantial volatility in hedge fund returns during the last decade.     

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.     

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.     

A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies

This paper proposes a model that allows for nonlinear risk exposures of hedge funds to various risk factors. We introduce a flexible threshold regression model and develop a Bayesian approach for model selection and estimation of the thresholds and their unknown number. In particular, we present a computationally flexible Markov chain Monte Carlo stochastic search algorithm which identifies relevant risk factors and/or threshold values. Our analysis of several hedge fund returns reveals that different strategies exhibit nonlinear relations to different risk factors, and that the proposed threshold regression model improves our ability to evaluate hedge fund performance.     

Forecasting the performance of hedge fund styles

This article predicts the relative performance of hedge fund investment styles using time-varying conditional stochastic dominance tests. These tests allow for the construction of dynamic trading strategies based on nonparametric density forecasts of hedge fund returns. During the recent financial turmoil, our tests predict a superior performance for the Global Macro investment style compared with the other strategies of ‘Directional Traders’. The Dedicated Short Bias investment style is stochastically dominated by the other directional styles. These results are confirmed by simple nonparametric tests constructed from realized excess returns. Further, by utilizing a cross-validation method for optimal bandwidth parameter selection, we discover the factors that have predictive power regarding the density of hedge fund returns. We observe that different factors have forecasting power for different regions of the returns distribution and, more importantly, that the Fung and Hsieh factors have power not only for describing the risk premium but also, if appropriately exploited, for density forecasting.     

The alpha and omega of fund of hedge fund added value

In spite of a somewhat disappointing performance throughout the crisis, investors are showing interest in hedge funds. Still, funds of hedge funds keep on experiencing outflows. Can this phenomenon be explained by the failure of fund of hedge fund managers to deliver on their promise to add value through active management, or is it symptomatic of a move toward greater disintermediation in the hedge fund industry? We introduce a return-based attribution model allowing for a full decomposition of fund of hedge fund performance. The results of our empirical study suggest that funds of hedge funds are funds of funds like others. Strategic allocation turns out to be a crucial step in the investment process, in that it not only adds value over the long-term, but most importantly, it brings resilience precisely when investors need it the most. Fund picking, on the other hand, turns out to be a double-edged sword.     

Predicting hedge fund performance when fund returns are skewed

We show that fund-specific return skewness is associated with managerial skill and future hedge fund performance. Specifically, skewness in fund returns reflects managerial skill in avoiding large drawdowns. Using a new measure of investment skill that accounts for this managerial ability, we demonstrate that traditional performance measures underestimate (overestimate) managerial performance when returns exhibit positive (negative) fund-specific skewness. Our new measure is particularly valuable during periods of economic crisis, when the annual risk-adjusted outperformance is 5.5%.     

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

This paper examines the cross-sectional relationship between downside risk (Value at Risk) and expected returns in a sample of 1370 emerging market hedge funds (EMHF). We find that downside risk significantly drives expected returns for these funds, particularly before the global financial crisis, commanding an annual risk premium of over 12%. While EMHF differ from their advanced market counterparts in risk/return patterns, we show that the global financial crisis of 2008 has caused a structural shift in that pattern. Finally, we show that the risk premium associated with downside risk is predictable by the global financial cycle, even after we control for emerging market systematic risk factors.     

Idiosyncratic volatility and equity returns: UK evidence

The proposition that idiosyncratic volatility may matter in asset pricing is currently a topic of research and controversy. Using data from the UK market we examine the predictive ability of various measures of idiosyncratic risk and provide evidence which suggests that: (a) it is the idiosyncratic volatility of small capitalization stocks that matters for asset pricing and (b) that small stocks idiosyncratic volatility predicts the small capitalization premium component of market returns and is unrelated to either the market or the value premium. The predictive power of the aggregate idiosyncratic volatility of small stocks remains intact even after we control for the possible proxying effects of business cycle fluctuations and liquidity and is robust across time and different econometric specifications.     

World market risk,country-specific risk and expected returns in international stock markets

This paper determines whether the world market risk, country-specific total risk, and country-specific idiosyncratic risk are priced in an international capital asset pricing model (ICAPM). Portfolio-level analyses, country-level cross-sectional regressions, stacked time-series, and pooled panel regressions indicate that the world market risk is not, but country-specific total and idiosyncratic risks are significantly priced in an ICAPM framework with partial integration. This result is robust to different methods for estimating risk measures, different investment horizons, and after controlling for the countries’ aggregate dividend yield, earnings-to-price ratios, inflation risk, exchange rate uncertainties, aggregate volatility risk, and past return characteristics. The main findings turn out to be insensitive to the choice of one-factor vs. multifactor models used to estimate systematic and idiosyncratic risk measures.     

An advanced perspective on the predictability in hedge fund returns

This paper advances the research on the predictability in hedge fund returns, using a broad set of risk factors within a variety of different prediction models. Accounting for the fact that returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive economic risk factors, we advocate a non-parametric backward elimination regression approach. The interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable in terms of the observed direction of impact. Thus, taking into account the specific characteristics of this asset class, we find strong evidence of its return predictability.     

Exploring uncharted territories of the hedge fund Industry: Empirical characteristics of mega hedge fund firms

This paper investigates mega hedge fund management companies that collectively manage over 50% of the industry's assets, incorporating previously unavailable data from those that do not report to commercial databases. We find similarities among mega firms that report performance to commercial databases compared with those that do not. We show that the largest divergences between the performance of reporting and nonreporting mega firms can be traced to differential exposure to credit markets. Thus, the performance of hard-to-observe mega firms can be inferred from observable data. This conclusion is robust to delisting bias and the presence of serially correlated returns.     

Dynamic hedge fund portfolio construction: A semi-parametric approach

In this article, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. We compare the standard mean–variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, which is based on extreme value theory, copula and Monte Carlo simulation. We compare the semi-parametric approach with the standard, non-parametric approach, used to compute CVaR, CDaR and Omega, and the benchmark parametric approach, based on both static and dynamic mean–variance optimization. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the parametric mean–variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.     

The cross section of conditional mutual fund performance in European stock markets

This paper implements strategies that use macroeconomic variables to select European equity mutual funds, including Pan-European, country, and sector funds. We find that several macro-variables are useful in locating funds with future outperformance and that country-specific mutual funds provide the best opportunities for fund rotation strategies using macroeconomic information. Specifically, our baseline long-only strategies that exploit time-varying predictability provide four-factor alphas of 12–13% per year over the 1993–2008 period. Our study provides new evidence on the skills of local versus Pan-European asset managers, as well as how macroeconomic information can be used to locate and time these local fund manager skills.     

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.     

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.     

Mutual fund flows and investor returns: An empirical examination of fund investor timing ability

We examine the timing ability of mutual fund investors using cash flow data at the individual fund level. Over 1991–2004 equity fund investor timing decisions reduce fund investor average returns by 1.56% annually. Underperformance due to poor timing is greater in load funds and funds with relatively large risk-adjusted returns. In particular, the magnitude of investor underperformance due to poor timing largely offsets the risk-adjusted alpha gains offered by good-performing funds. Investors in both actively managed funds and index funds exhibit poor investment timing. We demonstrate that our empirical results are consistent with investor return-chasing behavior.     

Conditionally fitted Sharpe performance with an application to hedge fund rating

We define a battery of Sharpe performance measures, which differ by the information taken into account in their computation, but also by the potential use of the fund by the investor. Four advantages of Sharpe performance based rating are especially important for the investor. First, the performance measures correspond to the standard measures used for mutual funds and known by retail investors. Second, we can compare the numerical results, even if they are obtained with different assumptions. Third, the rankings are based on regression analysis and easy to compute. Fourth, we can easily use these performance measures in the design of an optimal basket of hedge funds. Finally, we can use the performance measures to partition the set of funds into homogenous segments.