Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 276-286.doi: 10.11896/jsjkx.210900127

• Artificial Intelligence • Previous Articles     Next Articles

Application of Machine Learning in Financial Asset Pricing:A Review

XU Jie1, ZHU Yu-kun1, XING Chun-xiao2   

  1. 1 PBC School of Finance,Tsinghua University,Beijing 100084,China
    2 Beijing National Research Center for Information Science and Technology(BNRist),Tsinghua University,Beijing 100084,China
  • Received:2021-09-15 Revised:2021-12-05 Online:2022-06-15 Published:2022-06-08
  • About author:XU Jie,born in 1986,Ph.D.His main research interests include machine learning,asset pricing and quantitative trading.
    XING Chun-xiao,born in 1967,Ph.D supervisor.His main research interests include deep learning,big data and knowledge engineering,and fintech.
  • Supported by:
    Key research and Development Plan of Ministry of Science and Technology:Research on the Theory and Techno-logy of Modern Service Trusted Transaction(2018YFB1402701).

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Abstract: The key problem of financial asset allocation is asset price.Asset pricing is the core content of modern finance,which indicates that asset pricing law has always been one of the hot topics of financial research.This paper reviews the methods used by machine learning in the field of asset pricing and research progresses,classifies machine learning asset pricing method into machine learning method based on the characteristics processing and deep learning method based on end-to-end processing,compares the differences between different algorithms in principle and application scenarios,points out the applicability and limitations of the two kinds of machine learning methods,prospects the research direction on machine learning asset pricing in the future.

Key words: Asset pricing, Deep learning, Machine learning, Portfolio, Price forecasting

CLC Number: 

  • TP181
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