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A,Multiagent approach to Q-learning for daily stock trading

Authors
Lee, Jae WonPark, JonghunO, JangminLee, JongwooHong, Euyseok
Issue Date
Nov-2007
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
financial prediction; intelligent multiagent systems; portfolio management; Q-learning; stock trading
Citation
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, v.37, no.6, pp 864 - 877
Pages
14
Journal Title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
Volume
37
Number
6
Start Page
864
End Page
877
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8328
DOI
10.1109/TSMCA.2007.904825
ISSN
1083-4427
1558-2426
Abstract
The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.
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