关键词: 常微分方程, 基因表达式编程, 多指标正则化, 模糊粗糙集, 股价预测

Abstract: The prediction of stock price has always been a hot issue in the financial field, but the volatility and uncertainty of relevant index data make it difficult. Therefore, for a nonlinear and multi-factor-influencing stock system, the traditional forecasting method cannot accurately express the law of stock price changes, and the forecasting effect is poor. Aiming at the complex stock price forecasting problem, this paper establishes a high-order ordinary differential equation model based on Multi-Factor Regularization GEP（MFR-GEP） algorithm, and uses numerical difference to fit the stock price data, and adds other indexes as the regular terms. Here, the index correlation is used to determine the regular term weight parameters, and the principle of fuzzy rough sets is used to determine the sub-function mapping. The model can describe the trend of stock price changes and the data fluctuations. In addition, the regular items allow the model to be predicted based on multiple indicators, avoiding problems such as low accuracy due to single indicator prediction. Finally, the standard GEP algorithm and the traditional prediction algorithm are selected for comparison experiments. The results fully verify the effectiveness and accuracy of the proposed algorithm.

Key words: ordinary differential equation, gene expression programming, multi-index regularization, fuzzy rough set, stock price prediction