Three Essays Exploring Hedge Fund Dynamics

Hedge fund managers are largely free to pursue dynamic trading strategies and standard static performance appraisal is no longer accurate for evaluating hedge funds. Accordingly, chapter 1 presents some new ways of analyzing hedge fund strategies following a dynamic linear regression model. Chapter 2 examines hedge fund asset allocation dynamics through conducting optimal changepoint test on an asset class factor model. Based on the average F-test and the Bayesian Information Criterion (BIC), we find that dynamic hedge funds have significantly better quality than non-dynamic funds, signaled by lower volatility in returns, stricter share restrictions, and high water mark provision. In particular, a higher degree of dynamics is shown to be associated with better risk-adjusted performance at the individual fund level. Sub-period analysis suggests that the superiority of asset allocation dynamics is mostly driven by earlier time periods before the peak of the technology bubble. Flow analysis suggests that returns in the hedge fund industry are diminishing as capital flows in and arbitrage opportunities are not infinitely exploitable. Chapter 3 examines both the self-reported classification and return-based classification on a sample of hedge funds over the period of 2005 to 2010. Using seven versions of Lipper/TASS data, we are able to track self-reported styles year by year; return-based classification follows a clustering algorithm called Partitioning Around Medoids (PAM). We show non-negligible style dynamics in both classifications, suggest that static hedge fund classification is inappropriate. Although a few self-reported categories, e.g. managed futures, appears to be consistent with the return-based grouping. We show that a hedge fund\u27s attractiveness relative to its peers differ by different classifications. We construct a disagreement measure, quantifying how much a fund\u27s performance percentile differs by its self-claimed classification and by its return-based classification; it is found that right tail funds in the disagreement measure perform significantly better than the left tail funds. Moreover, we find that fund flow is positively related to the disagreement measure controlling for performance and fund characteristics. The results are robust to alternative disagreement measure or extended sample period