An ensemble of neural networks for stock trading decision making

Authors:
Pei-Chann Chang;Chen-Hao Liu;Chin-Yuan Fan;Jun-Lin Lin;Chih-Ming Lai
Affiliations:
Department of Information Management, Yuan Ze University, Taoyuan, Taiwan;Department of Information Management, Kainan University, Taoyuan, Taiwan;Department of Business Innovation and Development, Ming Dao University, Changhua, Taiwan;Department of Information Management, Yuan Ze University, Taoyuan, Taiwan;Department of Information Management, Yuan Ze University, Taoyuan, Taiwan
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

Stock turning signals detection are very interesting subject arising in numerous financial and economic planning problems. In this paper, Ensemble Neural Network system with Intelligent Piecewise Linear Representation for stock turning points detection is presented. The Intelligent piecewise linear representation method is able to generate numerous stocks turning signals from the historic data base, then Ensemble Neural Network system will be applied to train the pattern and retrieve similar stock price patterns from historic data for training. These turning signals represent short-term and long-term trading signals for selling or buying stocks from the market which are applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system can make a significant and constant amount of profit when compared with other approaches using stock data available in the market.