The persistence of hedge fund strategies in different economic periods

A support vector machine approach

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)2-15
Number of pages14
JournalJournal of Derivatives and Hedge Funds
Volume17
Issue number1
DOIs
StatePublished - May 1 2011

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Support vector machine
Economics
Hedge funds
Persistence
Ordinary least squares
Prediction
Trading strategies
Emerging markets
Network analysis
Hedge fund performance
Industry
Statistical methods
Neural networks
Methodology
Feedforward neural networks
Prediction accuracy
Fat tails
Evaluation

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Cite this

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