Optimization of Stock Multifactor Model based on Machine Learning

Yue WANG, Yu-xue WANG, Xiao-ping REN


In recent years, with the development of the financial market in China, in the background of information and big data, the quantitative products in the domestic market are gradually increasing. Multifactor model is an important stock selection model, its advantage is that it can synthesize a lot of information and get a stock selection result, which has a wide range of applications in the stock market. The purpose of this paper is to find some factors most related to the rate of return by establishing a multifactor model, and to select different weight factors to construct a stock selection model. The article is intended to select the stock combination to make it greater than or equal to the market index in the future, and to obtain the optimal benefit. Compared with the traditional linear multifactor model, machine learning algorithm can find more precise market signals through the nonlinear expression of factors, in order to obtain more robust excess returns.


Machine learning, Multifactor model, Sharp ratio, Fuzzy clustering


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