Hedge fund strategies typically generate option-like returns. Linear-factor models using benchmark asset indices have difficulty explaining them. Following the suggestions in Glosten and Jagannathan (1994), this article shows how to model hedge fund returns by focusing on the popular "trend-following" strategy. We use lookback straddles to model trend-following strategies, and show that they can explain trend-following funds' returns better than standard asset indices. Though standard straddles lead to similar empirical results, lookback straddles are theoretically closer to the concept of trend following. Our model should be useful in the design of performance benchmarks for trend-following funds.

This article presents some new results on an unexplored dataset on hedge fund performance. The results indicate that hedge funds follow strategies that are dramatically different from mutual funds, and support the claim that these strategies are highly dynamic. The article finds five dominant investment styles in hedge funds, which when added to Sharpe's (1992) asset class factor model can provide an integrated framework for style analysis of both buy-and-hold and dynamic trading strategies.

William Fung, David A. Hsieh, Performance Characteristics of Hedge Funds and Commodity Funds: Natural vs. Spurious Biases, The Journal of Financial and Quantitative Analysis, Vol. 35, No. 3 (Sep., 2000), pp. 291-307

Hedge fund strategies typically generate option-like returns. Linear-factor models using benchmark asset indices have difficulty explaining them. Following the suggestions in Glosten and Jagannathan (1994), this article shows how to model hedge fund returns by focusing on the popular “trend-following” strategy. We use lookback straddles to model trend-following strategies, and show that they can explain trend-following funds’ returns better than standard asset indices. Though standard straddles lead to similar empirical results, lookback straddles are theoretically closer to the concept of trend following. Our model should be useful in the design of performance benchmarks for trend-following funds.

This article presents some new results on an unexplored dataset on hedge fund performance. The results indicate that hedge funds follow strategies that are dramatically different from mutual funds, and support the claim that these strategies are highly dynamic. The article finds five dominant investment styles in hedge funds, which when added to Sharpe’s (1992) asset class factor model can provide an integrated framework for style analysis of both buy-and-hold and dynamic trading strategies.

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.

We use a comprehensive data set of funds‐of‐funds to investigate performance, risk, and capital formation in the hedge fund industry from 1995 to 2004. While the average fund‐of‐funds delivers alpha only in the period between October 1998 and March 2000, a subset of funds‐of‐funds consistently delivers alpha. The alpha‐producing funds are not as likely to liquidate as those that do not deliver alpha, and experience far greater and steadier capital inflows than their less fortunate counterparts. These capital inflows attenuate the ability of the alpha producers to continue to deliver alpha in the future.