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Machine learning the performance of hedge fund

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  • Ma, Tian
  • Wang, Wanwan
  • Jiang, Fuwei

Abstract

This study utilizes generative AI to predict and classify the performance of hedge funds based on groups of fund characteristics. Compared to commonly used machine learning methods, our method can successfully distinguish high- and low-performing funds across various investment strategies, with the return spread being the highest in the equity hedge strategy at 3.16 % monthly. The results are robust in risk-adjusted return prediction. Trend-based features are the most important predictors of future fund performance. Returns of predictive long-short portfolios are higher following periods of low narrative attention and favorable macroeconomic conditions. The asset allocation exercise highlights the significant economic value of machine learning. Our study enriches the burgeoning field of machine learning and artificial intelligence for finance by applying big data techniques to fund selection and allocation.

Suggested Citation

  • Ma, Tian & Wang, Wanwan & Jiang, Fuwei, 2025. "Machine learning the performance of hedge fund," Journal of International Money and Finance, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:jimfin:v:155:y:2025:i:c:s0261560625000671
    DOI: 10.1016/j.jimonfin.2025.103332
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    1. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    2. Ma, Tian & Li, Ganghui & Zhang, Huajing, 2024. "Stock return predictability using economic narrative: Evidence from energy sectors," Journal of Commodity Markets, Elsevier, vol. 35(C).
    3. Joenväärä, Juha & Kauppila, Mikko & Kahra, Hannu, 2021. "Hedge fund portfolio selection with fund characteristics," Journal of Banking & Finance, Elsevier, vol. 132(C).
    4. Stefano Giglio & Bryan Kelly & Dacheng Xiu, 2022. "Factor Models, Machine Learning, and Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 337-368, November.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    6. Leland Bybee & Bryan T. Kelly & Asaf Manela & Dacheng Xiu, 2021. "Business News and Business Cycles," NBER Working Papers 29344, National Bureau of Economic Research, Inc.
    7. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    8. 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.
    9. Jonathan Ingersoll & Ivo Welch, 2007. "Portfolio Performance Manipulation and Manipulation-proof Performance Measures," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1503-1546, 2007 17.
    10. Marcin Kacperczyk & Clemens Sialm & Lu Zheng, 2008. "Unobserved Actions of Mutual Funds," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2379-2416, November.
    11. Ma, Tian & Wang, Wanwan & Chen, Yu, 2023. "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
    12. repec:bla:jfinan:v:55:y:2000:i:4:p:1655-1703 is not listed on IDEAS
    13. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    14. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    15. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    16. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa Onur, 2012. "Systematic risk and the cross section of hedge fund returns," Journal of Financial Economics, Elsevier, vol. 106(1), pages 114-131.
    17. Russ Wermers, 2000. "Mutual Fund Performance: An Empirical Decomposition into Stock‐Picking Talent, Style, Transactions Costs, and Expenses," Journal of Finance, American Finance Association, vol. 55(4), pages 1655-1695, August.
    18. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    19. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    20. Olivier Coibion & Yuriy Gorodnichenko & Michael Weber, 2022. "Monetary Policy Communications and Their Effects on Household Inflation Expectations," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1537-1584.
    21. Sheridan Titman & Cristian Tiu, 2011. "Do the Best Hedge Funds Hedge?," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 123-168.
    22. Kosowski, Robert & Naik, Narayan Y. & Teo, Melvyn, 2007. "Do hedge funds deliver alpha? A Bayesian and bootstrap analysis," Journal of Financial Economics, Elsevier, vol. 84(1), pages 229-264, April.
    23. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2014. "Macroeconomic risk and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 114(1), pages 1-19.
    24. Ro'ee Levy, 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment," American Economic Review, American Economic Association, vol. 111(3), pages 831-870, March.
    25. Roger G. Ibbotson & Peng Chen & Kevin X. Zhu, 2011. "The ABCs of Hedge Funds: Alphas, Betas, and Costs," Financial Analysts Journal, Taylor & Francis Journals, vol. 67(1), pages 15-25, January.
    26. 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.
    27. William Fung & David A. Hsieh & Narayan Y. Naik & Tarun Ramadorai, 2008. "Hedge Funds: Performance, Risk, and Capital Formation," Journal of Finance, American Finance Association, vol. 63(4), pages 1777-1803, August.
    28. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    29. Slavutskaya, Anna, 2013. "Short-term hedge fund performance," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4404-4431.
    30. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    31. William Fung & David A. Hsieh, 2004. "Hedge Fund Benchmarks: A Risk-Based Approach," Financial Analysts Journal, Taylor & Francis Journals, vol. 60(5), pages 65-80, September.
    32. Cao, Charles & Chen, Yong & Liang, Bing & Lo, Andrew W., 2013. "Can hedge funds time market liquidity?," Journal of Financial Economics, Elsevier, vol. 109(2), pages 493-516.
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