A rule-based neural stock trading decision support system

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.