This paper uses the SVM (support vector machine) method to model the multi-factor stock selection and conducts research in Chinese Stock Market. The CSI 300 Index accounts for about 60% of the market value of Chinese Stock Market, we uses the principal component analysis for dimensionality reduction, reducing the number of original factors to 13, and the cumulative contribution rate reached 78.5372%, which reduced the complexity of SVM classification. In terms of model building, since the linear SVM method cannot be reasonably classified, this paper uses the radial basic kernel function and then classifies them, and obtains a stock selection model with strong effectiveness, which can beat the benchmark in all test sets. In terms of stock selection, we sort the stocks according to the sample values generated by the prediction of the model, and the reliability of the result obtained was high, which is the innovation of this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.