In this paper we propose to do portfolio management using reinforcement learning (RL) and independent factor model. Factors in independent factor model are mutually independent and exhibit better predictability. RL is applied to each factor to capture temporal dependence and provide investment suggestion on factor. Optimal weights on factors are found by portfolio optimization method subject to the investment suggestions and general portfolio constraints. Experimental results and analysis are given to show that the proposed method has better performance when compare to two alternative portfolio management systems. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Li, J., Zhang, K., & Chan, L. (2007). Independent factor reinforcement learning for portfolio management. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4881 LNCS, pp. 1020–1031). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_102
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