An artificial intelligence-based rule-induction approach to the analysis of stock market prediction is presented. A single investment analyst was used as the expert for this study. Predicting intermediate fluctuations in the movement of the market for nonconservative investors was selected as the decision to analyze. Commercially available rule-induction software was used to generate rules that predicted the market calls of the market analyst and the actual movements of the market. Rules predicting actual market movement performed better than rules predicting the analyst's calls and better than the analyst himself. Such an approach may prove useful in designing a decision support system for market analysts or in improving the decision-making processes of such analysts. The dynamic nature of the stock market represents a substantially different decision environment than those previously analyzed by learning-from-example (LFE) techniques. This study provides insights into the limits and applications of LFE approaches.