Stock Price Forecasting by Hybrid Machine Learning Techniques

Stock investment has become an important investment activity in Taiwan. However, investors usually get loss because of unclear investment objective and blind investment. Therefore, to create a good investment decision support system to assist investors in making good decisions has become an important research problem. Artificial Neural Networks (ANN) can provide relatively good performances in forecasting stock price but it cannot explain the forecasting rules clearly. On the other hand, a decision tree (DT) model can generate some rules to describe the forecasting decisions. This paper focuses on combining ANN and decision trees to create a stock price forecasting model. The experimental result shows that the combined DT+ANN model has 77% accuracy, which is higher than the single ANN and DT models over the electronic industry.