Study on Stock Price Prediction Based on BP Neural Network

摘要：

In this paper,two kinds of methods, namely additional momentum method and self-adaptive learning rate adjustment method, are used to improve the BP algorithm. Considering the diversity of factors which affect stock prices, Single-input and Multi-input Prediction Model (SIPM and MIPM) are established respectively to implement short-term forecasts for SDIC Electric Power (600886) shares and Bank of China (601988) shares in 2009. Experiments indicate that the improved BP model has superior performance to the basic BP model, and MIPM is also better than SIPM. However, the best performance is obtained by using MIPM and improved prediction model cohesively.

关键词： Stock prediction BP algorithm Neural network MSE

作者: Weimin Ma Yingying Wang Ningfang Dong

作者单位: School of economics and management Tongji University Shanghai, China School of economics and management Tongji University Shanghai,China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Emergency Management and Management Sciences(2010 IEEE应急管理与管理科学国际会议 ICEMMS)

会议地点: 北京

会议语种:英文

页码: 57-60

在线出版日期: 2010-08-08（万方平台首次上网日期，不代表论文的发表时间）