Title :
A rule-based neural stock trading decision support system
Author :
Chou, Seng-cho Timothy ; Yang, Chau-chen ; Chi-Huang Chan ; Lai, Feipei
Author_Institution :
Dept. of Inf. Manage., Nat. Taiwan Univ., Taipei, Taiwan
Abstract :
We propose an intelligent stock trading decision support system that can forecast the buying and selling signals according to the prediction of short-term and long-term trends using rule-based neural networks. A rule-based neural network allows us to use domain knowledge in the form of inference rules to set up the initial structure of the neural network, and to extract refined domain knowledge from the trained network. With this information, users can understand why and how a decision is made by the system without the need to trust the output of the network blindly. The performance of the proposed system was evaluated by trading the TSEWPI (Taiwan Stock Exchange Weighted Price Index) from 1992 to 1995, and the result was encouraging
Keywords :
decision support systems; financial data processing; forecasting theory; inference mechanisms; knowledge acquisition; knowledge based systems; neural nets; stock markets; TSEWPI trading; Taiwan Stock Exchange Weighted Price Index; buying signal forecasting; domain knowledge; inference rules; intelligent stock trading decision support system; long-term trend prediction; refined domain knowledge extraction; rule-based neural networks; rule-based neural stock trading decision support system; selling signal forecasting; short-term trend prediction; trained network; Computer science; Data mining; Decision support systems; Finance; Information management; Intelligent networks; Load forecasting; Neural networks; Power system modeling; Stock markets;
Conference_Titel :
Computational Intelligence for Financial Engineering, 1996., Proceedings of the IEEE/IAFE 1996 Conference on
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-3236-9
DOI :
10.1109/CIFER.1996.501839
