Boosting Learning Algorithm for Stock Price Forecasting

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Wang, Chengzhang^1 ; Bai, Xiaoming^1,2

School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China^1

Information School, Capital University of Economics and Business, Beijing, China^2

关键词: ANN (artificial neural network);    ANN modeling;    Input variables;    Market behavior;    Stock price;    Stock price forecasting;    Technical factors;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/5/052053/pdf
DOI  :  10.1088/1757-899X/322/5/052053

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.