Abstract

The most important aspect of stock investment is the ability to select good stocks, which can bring investors good excess returns before the market becomes strong and efficient. With the rapid development of computer technology and big data application, the multi-factor stock selection theory in quantitative investment is becoming increasingly sophisticated, and has become a popular and stable stock selection method. Based on the "RiceQuant" quantitative investment analysis platform, this study adjusts the parameters of the profitability factors (return on net assets and gross profit margin) in the fundamental multi-factor stock selection model using data from the constituents of the Shanghai and Shenzhen 300 Index from 2017 to 2020, selects the optimal parameters based on backtesting returns and risk screening, and further optimizes the model by including both profitability factors. These results reflect the screening ability of different profitability factors for stocks and can provide reference for the strategy design of multi-factor models.

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