JultikaUniversity of Oulu repository

In this study we examine the proportion of false discovery rate exists amongst the individual funds in Hedge Fund Research (HFR) database. Applying the Fung and Hsieh (2004) seven-factor model in a time series regression along with a statistical false discovery rate methodology construct the main framework of this study. False discovery rate helps to measure the proportion of lucky funds among hedge funds that have statistically significant alphas and explains how many percentages of funds with significant alphas would be achieved due to luck compared to skill. Even after adjusting for the backfill bias, the proportion of false discovery rate states that the hedge funds outperform due to skill compared to luck and underperforms due to be unlucky compared to be unskilled. Results for strategies demonstrate that the proportion of false discovery rate in Event Driven, Relative Value, and Multi Strategy is very low in the right tail respectively and the manager has skill compared to luck. In contrast, strategies such as CTA, Relative Value, and Short Bias have the lowest proportion of false discovery rate in the left tail respectively which implies the manager is more unskilled compared to be unlucky in his performance. The proportion of false discovery rate for small funds is greater than large funds in the right tail of the distribution and it implies that for small funds the manager outperforms mostly by luck compared to skill. Contrarily, the proportion of false discovery rate for large funds is greater than small funds in the left tail of the distribution and it implies that for large funds the manager is more unlucky compared to be unskilled to outperform in the market.