Application of Support Vector Regression Method in Stock Market Forecasting

Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the stock market forecasting problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with three-layer fully connected backpropagation neural networks (BNN). The experiment results show that SVM outperforms the BNN.