Feature extraction using Restricted Boltzmann Machine for stock price prediction - Xianggao Cai; Su Hu; Xiaola Lin (2012 IEEE International Conference on Computer Science and Automation Engineering, 2012)
Recently, many different types of artificial neural networks (ANNs) have been applied to forecast stock price and good performance is obtained. However, most of these models use only a small number of features as input and there may not be enough information to make prediction due to the complexity of stock market. If having a larger number of features, the run time of training would be increased and the generalization performance would be deteriorated due to the curse of dimension. Therefore, an effective tool to extract highly discriminative low-dimensional features from the high-dimensional raw input would be a great help in improving the generalization performance of the regression model. Restricted Boltzmann Machine (RBM) is a new type of machine learning tool with strong power of representation, which has been utilized as the feature extractor in a large variety of classification problems. In this paper, we use the RBM to extract discriminative low-dimensional features from raw data with dimension up to 324, and then use the extracted features as the input of Support Vector Machine (SVM) for regression. Experimental results indicate that our approach for stock price prediction has great improvement in terms of low forecasting errors compared with SVM using raw data.