Multiple-World Trader-Company Method for Stock Price Prediction and Evaluation of Robustness for Regime Change

Abstract

In recent years, many investors have been developed quantitative stock prediction models based on machine learning. It is difficult to put a machine learning-based stock price prediction model into practical use due to two challenges: market efficiency and lack of interpretability. Trader-Company (TC) method is a recently developed evolutionary method that finds interpretable temporal rules with high prediction accuracy. However, the TC method does not take into account regime changes, and the regime changes may worsen the prediction accuracy. Therefore, in this study, we propose the Multiple-World Trader-Company (MWTC) method in order to improve high robustness against regime changes. In the MWTC method, the Company model that manages Trader is used as a weak learner, and multiple companies individually learn the training data divided by regime. Empirical analysis using actual market data shows that the MWTC method achieves better prediction accuracy than the baseline method.