Risk Management of Enterprise Quantitative Investment Strategies through Data Modeling

Abstract

Quantitative investment strategies have been increasingly used in the capital market. In order to help enterprises manage risks better and enhance the reliability of quantitative investment strategies, this paper designed a quantitative investment data model for Enterprise A. The constituent stocks of the Shanghai and Shenzhen 300 index were regarded as the stock pool. Then, factor screening was performed in the Uqer quantitative factor library. Two data models were established: a scoring model and a regression model. The two models were tested through backtesting. The return and risk were compared between the two models by taking the return rate, net value, Alpha, Beta, Sharpe ratio, maximum retracement and information ratio as the evaluation indicators. The backtest results showed that the data model established by the regression method had a higher return rate, annualized return rate, net value, larger α value, smaller β value, a Sharpe ratio of 0.76, a maximum retracement of 25.34%, and an information ratio of 2.42, which had better balance in return and risk compared with the scoring method. In addition, the larger the number of positions was, the smaller the frequency of position transfer was, and the less effective the model was in quantitative investment. The experimental results verify the reliability of the regression model in the formulation of the quantitative investment strategy of Enterprise A. The investment strategy of Enterprise A can be adjusted and determined by the regression model to promote the balance between enterprise benefits and risks. The research results provide some references for the theoretical research of quantitative investment.

Keywords

• risk management
• data model
• factor screening
• yield rate
• quantitative investment enterprise