Unifying Rule Induction and Rule Refinement—Towards Discovering Anomaly from Granville's Law in a Stock Market Technical Analysis

This paper discusses a framework for refining an initial object‐level rule base with a rule induction to learn meta‐level rules which find a data set applicable to an object‐level rule. A rule induction process such as ID3 tries to learn meta‐level rules and classifies given training data sets into positive data sets and negative ones. The rule refinement process tries to refine an initial object‐level rule base on classified data sets by using four refinement strategies. Unifying these two processes, one can obtain a refined object‐level rule base with high performance where a meta‐level rule selects a data set applicable to it. In order to evaluate the framework, an experiment on real Japanese stock price data shows that a refined object‐level rule base, which comes from the initial object‐level rule base for representing Granville's Law, has a performance beyond that of the average stock price. The performance is difficult for human technical analysts in a stock market to achieve. The result implies that the framework could create an anomaly from Granville's Law in a stock market technical analysis.