An evolutionary approach to inferring decision rules from stock price index predictions of experts

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price