Abstract
By reviewing the relevant literature on machine learning in the field of asset pricing, this paper summarizes the application status, development trend, and existing problems of machine learning asset pricing methods, including commonly used algorithms, commonly used frameworks, and the advantages and disadvantages of different algorithms, comprehensively understanding the development status and trend of this field, and looking forward to the future possible research directions. In general, the machine learning asset pricing method has gradually shifted from manual feature extraction at the very beginning, relying on assumed models to build and solve model parameters, to end-to-end processing, increasing the diversity of data sources, especially with the development of deep reinforcement learning in recent years. This paper will focus on the methods and research progress of machine learning in the field of asset pricing and compares the applicability and limitations of the machine learning method according to the principle of a machine learning algorithm in different application scenarios.
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