This study aims to explore whether a regularly updated portfolio of outperforming hedge funds can consistently beat the corresponding hedge fund dataset index. If yes, moreover, the second question concerns whether portfolio optimization approaches can lead to an even better performance than the naïve equal-weighting method. The dataset spans the January-1994to August-2008 period and is classified into four main categories - Macro, Equity Hedge, Relative Value and Event Driven. Based on a seven-factor model, this study applies the Step-SPA test to each category of funds and examines the statisticalsignificance of the studentized fund alpha over the selection period of 3-7 years in length. A 'winner' portfolio of funds,namely, consisting of funds with statistically significant, positive studentized alpha, can be formed at the end of the selection period and held for 1 up to 3 years. We find that the winner portfolio tends to beat the dataset indexes during the holding period, irrespective of the time span for the selection and the holding periods investigated. Moreover, two of the three optimization approaches employed, the Probabilistic Global Search Lausanne and the Genetic Algorithm, prove to further enhance the performance of the equal-weighted winning portfolio.
|Number of pages||10|
|Journal||Asia Pacific Management Review|
|Publication status||Published - 2015 Dec 1|
All Science Journal Classification (ASJC) codes
- Business and International Management
- Strategy and Management