Research on multi factor stock selection model based on LightGBM and Bayesian Optimization

Abstract

With the development of the stock market, the number of individual investors has become more and more. Because of emotional and irrational factors, the risk and instability of stock market in China have been greatly increased. Therefore, it is necessary to introduce the idea of quantitative investment into the financial field. In this paper, firstly we use random forest, XGBoost and LightGBM to conduct rolling tests on multiple factors. After parameter adjustment based on Bayesian Optimization, we find that LightGBM-Bayes has the best effect. Finally, this paper uses the multi factor stock selection model based on LightGBM-Bayes to conduct a rolling back test on CSI300 Constituent Stocks, and the back test results are better than the benchmark CSI300 index.