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Financial Machine Learning

Author

Listed:
  • Bryan T. Kelly
  • Dacheng Xiu

Abstract

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

Suggested Citation

  • Bryan T. Kelly & Dacheng Xiu, 2023. "Financial Machine Learning," NBER Working Papers 31502, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31502
    Note: AP
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    Citations

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    Cited by:

    1. Zhao, Xin & Guo, Yanhong & Liu, Chuanren, 2024. "Leveraging corporate governance characteristics for stock crash risk assessment," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    2. Uluc Aysun & Melanie Guldi, 2026. "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers 2026-01, University of Central Florida, Department of Economics.
    3. Aldasoro, I. & Gambacorta, L. & Korinek, A. & Shreeti, V. & Stein, M., 2025. "Intelligent financial system: How AI is transforming finance," Journal of Financial Stability, Elsevier, vol. 81(C).
    4. Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).
    5. Ledoit, Olivier & Wolf, Michael, 2025. "Markowitz portfolios under transaction costs," The Quarterly Review of Economics and Finance, Elsevier, vol. 100(C).
    6. Doron Avramov & Xin He, 2026. "Stochastic Discount Factors with Cross-Asset Spillovers," Papers 2602.20856, arXiv.org.
    7. Jing Liu & Maria Grith & Xiaowen Dong & Mihai Cucuringu, 2026. "A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting," Papers 2603.10559, arXiv.org, revised Apr 2026.
    8. Liu, Yanchu & Zhou, Heyang & Yang, Haisheng, 2025. "Latent factor models for the Chinese commodity futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    9. Huo, Da & Shi, Yongdong & Wang, Chao & Wang, Lihan & Xing, Weize & Yang, Mo & Zhao, Jingjing, 2025. "Measuring systemic risk in China: A new hybrid approach incorporating ensemble learning and risk spillover networks," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    10. Zhao, Yuchen & Bi, Xiaogang & Ma, Qing-Ping, 2025. "Predicting mergers & acquisitions: A machine learning-based approach," International Review of Financial Analysis, Elsevier, vol. 99(C).
    11. Matteo Aquilina & Douglas Kiarelly Godoy de Araujo & Gaston Gelos & Taejin Park & Fernando Perez-Cruz, 2025. "Harnessing artificial intelligence for monitoring financial markets," BIS Working Papers 1291, Bank for International Settlements.
    12. Li, Bin & Rossi, Alberto G. & Yan, Xuemin (Sterling) & Zheng, Lingling, 2025. "Machine learning from a “Universe” of signals: The role of feature engineering," Journal of Financial Economics, Elsevier, vol. 172(C).
    13. van Cappelle, Tjeerd & Pokidin, Dmytro & Zwinkels, Remco C.J., 2025. "The cross section of stock returns in an artificial stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 239(C).

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G1 - Financial Economics - - General Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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