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An advanced perspective on the predictability in hedge fund returns

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  • Wegener, Christian
  • von Nitzsch, Rüdiger
  • Cengiz, Cetin

Abstract

This paper advances the research on the predictability in hedge fund returns, using a broad set of risk factors within a variety of different prediction models. Accounting for the fact that returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive economic risk factors, we advocate a non-parametric backward elimination regression approach. The interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable in terms of the observed direction of impact. Thus, taking into account the specific characteristics of this asset class, we find strong evidence of its return predictability.

Suggested Citation

  • Wegener, Christian & von Nitzsch, Rüdiger & Cengiz, Cetin, 2010. "An advanced perspective on the predictability in hedge fund returns," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2694-2708, November.
  • Handle: RePEc:eee:jbfina:v:34:y:2010:i:11:p:2694-2708
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    3. Giannikis, Dimitrios & Vrontos, Ioannis D., 2011. "A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1399-1414, June.
    4. Massimo Guidolin & Alexei G. Orlov, 2022. "Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-61, September.
    5. Bussière, Matthieu & Hoerova, Marie & Klaus, Benjamin, 2015. "Commonality in hedge fund returns: Driving factors and implications," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 266-280.
    6. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    7. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    8. Todd E. Clark & Michael W. Mccracken, 2014. "Tests Of Equal Forecast Accuracy For Overlapping Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 415-430, April.
    9. Guillermo Baquero & Marno Verbeek, 2015. "Hedge fund flows and performance streaks: How investors weigh information," ESMT Research Working Papers ESMT-15-01, ESMT European School of Management and Technology.
    10. Newton, David & Platanakis, Emmanouil & Stafylas, Dimitrios & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach," The British Accounting Review, Elsevier, vol. 53(5).
    11. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
    12. Harris, Richard D.F. & Mazibas, Murat, 2010. "Dynamic hedge fund portfolio construction," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 351-357, December.

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