Artificial neural network has been widely applied to predict financial market during the past decades. However, two major defects have limited the development of artificial neural network, the lack of explanation of causal relationship and the scarcity of the integration of expert knowledge. In this study, evolutionary genetic algorithm and rule-based neural network are combined to provide a decision model with explanation. Through the explanation, investors can understand the causal relationship of the prediction result. It can be used to recommend the proper time to buy or sell stocks. An example based on the Taiwan stock market is utilized to evaluate the profit of the proposed decision model. We also compare its performance with those of buy-and-hold method and back-propagation neural network. The experimental results show that the proposed decision model outperforms the other methods.