In recent years, people decide the investment trading behavior by quantitative means, through the combination of different stocks and trading time to select the optimal strategy for trading, therefore, the analysis of stocks and how to accurately predict the price of stocks has become the key to make investment decisions. This paper mainly carries on the support vector machine (SVM) algorithm price forecast model research and implementation. In this paper, the SVM model is used to model the stock price changes and conduct the corresponding strategy analysis, so as to play its advantages in small samples and nonlinearity. From the experimental results, it can be seen that the mean square error of the prediction result of SVM is far less than that of the time series model. This shows that the performance of SVM in stock price prediction is very excellent.

Shulan Li. Implementation of Price Prediction Model Based on Support Vector Machine Algorithm. Machine Learning Theory and Practice (2022), Vol. 3, Issue 2: 24-31. https://doi.org/10.38007/ML.2022.030203.

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