An automated quantitative investment model of stock selection and market timing based on industry information

Abstract

As a growth asset, many listed companies have distinct characteristics of their respective industries. The listed companies in the same industry have a link effect, which can provide a wealth of information for stock operations. In this paper, a complete automated quantitative investment model is developed based on statistical models that use industry information to select stocks and time the market, thereby bringing out the synergistic effect of the system and ensuring maximum returns on investment. Risk control is taken into consideration in our model, a risk control factor is designed by measuring volatility of stock prices to determine the buying volume and the timing of stop loss, effectively safeguarding capital security. The latest industry classification results published by the China Securities Regulatory Commission are used as the basis for the industry classification. After data preprocessing, there are 70 sub-categories in 18 major categories of industry. We take the stock price of the 70 sub-categories from January 1, 2012 to January 1, 2022 as our research data. The back testing results show that positive returns are obtained in all industries except for six in our model. The average annualized rate of return is 11.10 %, which is higher than the stock indexes of the same period and far higher than the investment model of bank savings. Additionally, in accordance with a real trading system, the experiment simulates the inclusion of all transaction fees in the trading process, demonstrating the practical application value of our expert system.