Financial stock market forecasting based on intelligent support vector machine regression model

Abstract

 The financial stock market is highly complex, nonlinear and uncertain, which makes it difficult to predict price fluctuation. With the advent of the era of artificial intelligence, a variety of intelligent optimization algorithms are constantly applied to the prediction of the stock market. The purpose of this study is to use a support vector machine regression model optimized by an intelligent fuzzy algorithm to predict the situation of the securities market. In this study, the stock price information of sh600060hisense electric equipment from June 2019 to December 2019 was used as the experimental data. As the input parameters of regression models, the starting price, the maximum price, the lowest price, the stock price, the transaction quantity, and the transaction quantity are taken up, and the fuzzy logic is used to make the sample data fuzzy, and the kernel function and optimization parameter are chosen. Then, the obtained data are trained in MATLAB, and the obtained data are effectively classified, and the stock price prediction of the financial market is obtained. The results show that the optimal parameters of the support vector machine regression model of stock data are C = 100, y = 0.01, ɛ= 0.01, and the accuracy of FSVM is about 0.75, which is higher than that of the SVM model (0.71), the square root mean square error (RMSE) is about 0.12, and the average absolute error (MAE) is about 0.015, According to the data, it can be said that the prediction results of the model are effective for the selected seven stocks one-minute data. It is concluded that the fuzzy support vector machine improves the prediction accuracy of the stock market. This study contributes to the prediction of an intelligent algorithm in the stock market.