Volatility Prediton based on Multiple-factor Model

Abstract

Generally, due to unpredictable and vulnerable features of the stock market, traditional statistical methods with single factor cannot explain the movements of stock prices well. In this paper, multi-factor model of quantitative stock selection is applied to predict stock price index to perceive the fluctuation of stock in multiple aspects and improve the accuracy of prediction. This study also combines machine learning measurements with historical data fitting effects. The data used for analysis is all collected from Tushare website, and the stock data are from the 2015 to 2022 data of Shanghai Pudong Development Bank and China Merchants Bank, and the selected factors include price-to-earnings ratio, price-to-book ratio, volume ratio, total equity and so forth. Ordinary least square linear regression and random forest nonlinear approaches are utilized to predict the stock price. According to the analysis, the accuracy of random forest is higher than OLS in stock price prediction. Among all the factors, the opening price, the highest price and the lowest price have relatively large impacts on the closing price of the next day. However, when selecting different factors, stocks and train-testing periods, the obtained regression models are different. Therefore, the determination of the correlation coefficient is not invariable and needs to be analyzed on a case-by-case basis. Overall, these results shed light on the superiority of machine learning model and the significant contribution of some factors on stock price prediction.