Boosting Learning Algorithm for Stock Price Forecasting

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.

作者: Chengzhang Wang Xiaoming Bai

作者单位: School of Statistics and Mathematics,Central University of Finance and Economics,Beijing,China Information School,Capital University of Economics and Business,Beijing,China

会议类型: 国际会议

会议名称: 2017 International Symposium on Application of Materials Science and Energy Materials (SAMSE 2017) (2017材料科学应用与能源材料国际研讨会)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2017-12-28（万方平台首次上网日期，不代表论文的发表时间）