Predicting investment behavior: An augmented reinforcement learning model

Authors:
Tetsuya Shimokawa;Kyoko Suzuki;Tadanobu Misawa;Yoshitaka Okano
Affiliations:
The School of Management, Tokyo University of Science, 500 Shimokiyoku, Kuki-shi, Saitama 346-8512, Japan;Graduate School of Economics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;The School of Management, Tokyo University of Science, 500 Shimokiyoku, Kuki-shi, Saitama 346-8512, Japan;The Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
Venue:
Neurocomputing
Year:
2009

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Abstract

The goal of this paper is to augment the ordinal temporal-difference type (TD-type) reinforcement learning model in order to detect the most suitable learning model of the human decision-making process in financial investment tasks. The simplicity and robustness of the TD-type learning model is fascinating. However, the available evidence and our observation suggest the necessity of introducing the nonlinear effect in learning and the possibility that additional factors might play important roles in the investment decision-making process. To extend the ordinal TD-type learning model, we adopt a three-layered perceptron as the basis function and the hierarchical Bayesian method to calibrate the parameter values. The result of the predictive test suggests that the augmented TD-type learning model constructed in this paper can evade the overfitting and can predict people's investment behavior well as compared to other familiar learning models.