This paper examines the use of derivatives and its relation with risk taking in the hedge fund industry. In a large sample of hedge funds, 71% of the funds trade derivatives. After controlling for fund strategies and characteristics, derivatives users on average exhibit lower fund risks (e.g., market risk, downside risk, and event risk), such risk reduction is especially pronounced for directional-style funds. Further, derivatives users engage less in risk shifting and are less likely to liquidate in a poor market state. However, the flow-performance relation suggests that investors do not differentiate derivatives users when making investing decisions.

We develop an estimation approach based on a modified expectation-maximization (EM) algorithm and a mixture of normal distributions associated with skill groups to assess performance in hedge funds. By allowing luck to affect both skilled and unskilled funds, we estimate the number of skill groups, the fraction of funds from each group, and the mean and variability of skill within each group. For each individual fund, we propose a performance measure combining the fund’s estimated alpha with the cross-sectional distribution of fund skill. In out-of-sample tests, an investment strategy using our performance measure outperforms those using estimated alpha and t -statistic.

This paper examines whether self-described market timing hedge funds have the ability to time the U.S. equity market. We propose a new measure for timing return and volatility jointly that relates fund returns to the squared Sharpe ratio of the market portfolio. Using a sample of 221 market timing funds during 1994–2005, we find evidence of timing ability at both the aggregate and fund levels. Timing ability appears relatively strong in bear and volatile market conditions. Our findings are robust to other explanations, including public information-based strategies, options trading, and illiquid holdings. Bootstrap analysis shows that the evidence is unlikely to be attributed to luck.

We identify a parsimonious set of factors from a large pool of candidates for explaining hedge fund returns, ranging from equity market, anomaly, and trend-following factors to macroeconomic factors. The resulting 9-factor model, including five anomaly factors, outperforms existing hedge fund models both in sample and out of sample, with a significant reduction in alphas while showing substantial cross sectional performance heterogeneity. Further analysis based on fund holdings confirms the model’s ability to capture returns from arbitrage trading. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and improve fund performance evaluation.