Stock Prices Forecasting and Optimization Strategies Based on Support Vector Machines

Abstract

With the global trend of digitization gaining prominence, the usage of machine learning methods such as Support Vector Machines and Reinforcement Learning for stock price prediction is becoming a hot topic. Over the past 40 years, China's economic market has undergone significant changes since the country's reform and opening up. In this study, the closing price and return of China's CSI 300 stock index are used as the database, and various data processing methods such as wavelet domain denoising, RSI screening and various SVM model optimization methods such as grid search and cross-validation are used to predict the upward or downward trend of stocks on the day after. The results of the study are presented by the model evaluation report and the heat map of the confusion matrix, which shows that the model prediction accuracy is 61% with the default parameters, and the accuracy improves to 67% after optimization. The results indicate that support vector machines are effective in stock price prediction, but there is still room for further improvement. This paper offers a potential approach that can increase return on investment and assist investors and financial institutions in making more informed investment decisions.