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Hedge Fund Return Predictability Under the Magnifying Glass

Author

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  • Avramov, Doron
  • Barras, Laurent
  • Kosowski, Robert

Abstract

This paper develops a unified approach to comprehensively analyze individual hedge fund return predictability, both in and out of sample. In sample, we find that variation in hedge fund performance across changing market conditions is widespread and economically significant. The predictability pattern is consistent with economic rationale, and largely reflects differences in key hedge fund characteristics, such as leverage or capacity constraints. Out of sample, we show that a simple strategy that combines the funds’ return forecasts obtained from individual predictors delivers superior performance. We exploit this simplicity to highlight the drivers of this performance, and find that in- and out-of-sample predictability are closely related.

Suggested Citation

  • Avramov, Doron & Barras, Laurent & Kosowski, Robert, 2013. "Hedge Fund Return Predictability Under the Magnifying Glass," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1057-1083, August.
  • Handle: RePEc:cup:jfinqa:v:48:y:2013:i:04:p:1057-1083_00
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    Citations

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    Cited by:

    1. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    2. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    3. 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.
    4. Samuel M. Hartzmark, 2016. "Economic Uncertainty and Interest Rates," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 6(2), pages 179-220.
    5. Stafylas, Dimitrios & Andrikopoulos, Athanasios & Tolikas, Konstantinos, 2023. "Hedge fund performance persistence under different business cycles and stock market regimes," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Barras, Laurent, 2019. "A large-scale approach for evaluating asset pricing models," Journal of Financial Economics, Elsevier, vol. 134(3), pages 549-569.
    7. Laurent Barras & Patrick Gagliardini & O. Scaillet, 2018. "The Cross-Sectional Distribution of Fund Skill Measures," Swiss Finance Institute Research Paper Series 18-66, Swiss Finance Institute.
    8. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2022. "Hedge Fund Performance: A Quantitative Survey," Working Papers IES 2022/15, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jun 2022.
    9. Narongdech Thakerngkiat & Hung T. Nguyen & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2021. "Do accounting information and market environment matter for cross‐asset predictability?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4389-4434, September.
    10. Agarwal, Vikas & Green, T. Clifton & Ren, Honglin, 2018. "Alpha or beta in the eye of the beholder: What drives hedge fund flows?," Journal of Financial Economics, Elsevier, vol. 127(3), pages 417-434.
    11. Andrea J. Heuson & Mark C. Hutchinson & Alok Kumar, 2020. "Predicting hedge fund performance when fund returns are skewed," Financial Management, Financial Management Association International, vol. 49(4), pages 877-896, December.
    12. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    13. Stafylas, Dimitrios & Anderson, Keith & Uddin, Moshfique, 2017. "Recent advances in explaining hedge fund returns: Implicit factors and exposures," Global Finance Journal, Elsevier, vol. 33(C), pages 69-87.
    14. Stafylas, Dimitrios & Anderson, Keith & Uddin, Moshfique, 2018. "Hedge fund performance attribution under various market conditions," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 221-237.
    15. Daniel Thomson & Gary van Vuuren, 2018. "Attribution of hedge fund returns using a Kalman filter," Applied Economics, Taylor & Francis Journals, vol. 50(9), pages 1043-1058, February.
    16. 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.
    17. Guillermo Baquero & Marno Verbeek, 2022. "Hedge Fund Flows and Performance Streaks: How Investors Weigh Information," Management Science, INFORMS, vol. 68(6), pages 4151-4172, June.
    18. Marat Molyboga & Seungho Baek & John F. O. Bilson, 2017. "Assessing hedge fund performance with institutional constraints: evidence from CTA funds," Journal of Asset Management, Palgrave Macmillan, vol. 18(7), pages 547-565, December.
    19. Jun Duanmu & Yongjia Li & Alexey Malakhov, 2020. "Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs," The Financial Review, Eastern Finance Association, vol. 55(3), pages 405-431, August.
    20. Joni Kokkonen & Matti Suominen, 2015. "Hedge Funds and Stock Market Efficiency," Management Science, INFORMS, vol. 61(12), pages 2890-2904, December.

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