Ju-Bong Kim, Joo-Seong Heo, Hyun-Kyo Lim, Do-Hyung Kwon, Youn-Hee Han

KIPS Transactions on Computer and Communication Systems, Vol. 8, No. 1, pp. 17-28, Jan. 2019
https://doi.org/10.3745/KTCCS.2019.8.1.17,   PDF Download:   Full Text
Keywords: Reinforcement Learning, Financial Portfolio Management, A3C, Cryptocurrency, Investment Engineering
Abstract

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.

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