The persistence of hedge fund strategies in different economic periods: A support vector machine approach

Abstract

This article researches two issues that are related to the hedge fund industry. The first is the statistical methodology used in the evaluation and prediction of hedge fund performance. As the returns of hedge funds are non-normal (suffer from fat tails), ordinary least squares (OLS) and other commonly used statistical methods may not reflect optimal results. Hence, this article introduces the use of support vector machines (SVM) to test and hence predict the performance of hedge fund strategies within different economic periods. The article also compares the SVM results with feedforward neural networks (NN) and OLS. The second issue is to investigate the ability of a specific hedge fund strategy to always outperform the average market during different economic periods. The results show that SVM has outperformed the NN analysis in its prediction accuracy. Moreover, those preliminary results show that the importance of hedge fund strategies varies (non-persistent) during different economic periods in affecting the monthly returns. However, the emerging markets trading strategy shows more persistence than other hedge fund strategies.