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.     

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.     

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.     

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.     

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.     

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.     

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.     

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).     

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.     

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.     

Local institutional investors,information asymmetries,and equity returns

We examine the informational role of geographically proximate institutions in stock markets. We find that both the level of and change in local institutional ownership predict future stock returns, particularly for firms with high information asymmetry; in contrast, such predictive abilities are relatively weak for nonlocal institutional ownership. The local advantage is especially evident for local investment advisors, high local ownership institutions, and high local turnover institutions. We also find that the stocks that local institutional investors hold (trade) earn higher excess returns around future earnings announcements than those that nonlocal institutional investors hold (trade).     

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.     

Mutual fund flows,expected returns,and the real economy

This paper investigates the relation between mutual fund flows and the real economy. The findings of this paper support the theory that the positive co-movement of flows into equity funds and stock market returns is explained by a common response to macroeconomic news. Variables that predict the real economy as well as the equity premium – in particular dividend-price ratio, default spread, relative T-Bill rate and consumption-wealth ratio – are related to fund flows and can account for the correlation of flows and market returns. Furthermore, consistent with the information-response hypothesis, mutual fund flows are forward-looking and predict real economic activity.     

The dynamics of short-term mutual fund flows and returns: A time-series and cross-sectional investigation

This study analyzes the dynamics of daily mutual fund flows. A Vector Auto Regression (VAR) of flows and returns shows that the behavior of fund investors is more consistent with contrarian rather than momentum characteristics. Past fund flows have a positive impact on future fund returns, with the long-term information effect dominating the transient price-pressure effect. Seasonality in daily flows, such as day-of-week and day-of-month patterns are present, and daily flows are generally mean-reverting. Probit regressions indicate that fund investment objective, marketing policy and level of active management explain cross-sectional variation in the behavioral patterns displayed in daily flows. Our results are robust to the different methods of calculating daily flows based on whether or not the day-end TNA figures include the current-day’s flow. Throughout the analysis, we contrast the dynamics of daily flows with established results for monthly fund flows and find important differences between the two.     

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.     

Short-term hedge fund performance

Hedge fund returns are often explained using linear factor models such as Fung and Hsieh (2004). However, since most hedge funds live only for 3 years, these linear regressions are subject to over-parameterization. I improve the out-of-sample accuracy of the linear factor model by combining cross-sectional and time series information for groups of hedge funds with similar investment strategies. The additional cross-sectional information allows more accurate estimates of risk exposures. I also propose a trading strategy based on this methodology for extracting substantially larger risk-adjusted returns.     

Caught on tape: Institutional trading,stock returns,and earnings announcements

Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the “tape”, the Transactions and Quotes database of the New York Stock Exchange, using a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses—possibly reflecting institutional demand for liquidity—but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.     

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.     

Do hedge funds deliver alpha? A Bayesian and bootstrap analysis

Using a robust bootstrap procedure, we find that top hedge fund performance cannot be explained by luck, and hedge fund performance persists at annual horizons. Moreover, we show that Bayesian measures, which help overcome the short-sample problem inherent in hedge fund returns, lead to superior performance predictability. Sorting on Bayesian alphas, relative to OLS alphas, yields a 5.5% per year increase in the alpha of the spread between the top and bottom hedge fund deciles. Our results are robust and relevant to investors as they are neither confined to small funds, nor driven by incubation bias, backfill bias, or serial correlation.     

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%.