Customizable and committee data mining framework for stock trading

Abstract

This study proposes a customizable and committee data mining (DM) framework for stock trading. The underlying conjecture is that different stocks may have different trading properties. A well-performing DM algorithm for a particular stock may not work well for other stocks. Therefore, the trading method designed for a particular stock should be customizable. To systematically obtain a customizable solution, we propose a research framework that can accommodate a large number of trading classifiers. Then, we select the well-performing ones to form a committee to make trading decisions for a particular stock. A committee decision is made because the best-performing trading classifier in a particular scenario (called validation phase) may not be the best one in another scenario (called testing phase). Therefore, some other well-performing classifiers afford their own advantages. We therefore extend the ensemble concept to form a customizable committee of well-performing classifiers that makes a trading decision by voting. To justify the idea of a customizable committee classifier, we slightly modify the framework and develop another trading method called customizable champion classifier in which only the best-performing classifier in the validation phase is used for stock trading. Using 12 stocks listed in the Taiwan Stock Exchange (TWSE) as test examples, the proposed customizable committee method outperforms the customizable champion method as well as six benchmarks methods (the buy-and-hold strategy and five popular technical analysis methods). To fairly compare a stock-trading method against the buy-and-hold strategy, two issues that have been rarely noted in literature are addressed in this study.