A neural network with a case based dynamic window for stock trading prediction

Authors:
Pei-Chann Chang;Chen-Hao Liu;Jun-Lin Lin;Chin-Yuan Fan;Celeste S. P. Ng
Affiliations:
Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, 32026 Taiwan, ROC;Department of Digital Technology, KaiNan University, No. 1 Kainan Road, Luchu, Taoyuan County, 33810 Taiwan, ROC;Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, 32026 Taiwan, ROC;Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, 32026 Taiwan, ROC;Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, 32026 Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Stock forecasting involves complex interactions between market-influencing factors and unknown random processes. In this study, an integrated system, CBDWNN by combining dynamic time windows, case based reasoning (CBR), and neural network for stock trading prediction is developed and it includes three different stages: (1) screening out potential stocks and the important influential factors; (2) using back propagation network (BPN) to predict the buy/sell points (wave peak and wave trough) of stock price and (3) adopting case based dynamic window (CBDW) to further improve the forecasting results from BPN. The system developed in this research is a first attempt in the literature to predict the sell/buy decision points instead of stock price itself. The empirical results show that the CBDW can assist the BPN to reduce the false alarm of buying or selling decisions. Nine different stocks with different trends, i.e., upward, downward and steady, are studied and one individual stock (AUO) will be studied as case example. The rates of return for upward, steady, and downward trend stocks are higher than 93.57%, 37.75%, and 46.62%, respectively. These results are all very promising and better than using CBR or BPN alone.