Abstract
Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization; 2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction; 3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.
Highlights
Quantitative stock selection has become a research hotspot in the field of investment decision
From the perspective of value investment, we look for the preliminary financial indicators from six aspects of A-share listed company, including rationality of earnings per share, profitability, leverage level, liquidity, efficiency level and growth ability
random forest algorithm (RF)-quantum genetic algorithm (QGA)-Support Vector Regression (SVR) is used as the quantitative stock selection model
Summary
Quantitative stock selection has become a research hotspot in the field of investment decision. Securities market is a high-dimensional and nonlinear complex system with much noise. Results obtained with the traditional linear-centered financial time series method still lack rules in the high-dimensional nonlinear securities market, and are still random discrete time series. The development of data mining and gradual maturity of artificial intelligence provides a new opportunity to the solving of high-dimensional and nonlinear problem with much noise. These methods oriented by artificial intelligence include text mining, heuristic algorithm, neural network, fuzzy control based on fuzzy mathematics and so on. Support Vector Machine (SVM) based on the statistical learning theory is widely used to predict complex high-dimensional nonlinear system in recent years, and many achievements have been made. The selection of SVM parameters has no good solution; second, feature selection has big impact on the performance of the model
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