《2016A Multiagent Approach to Q-Learning for Daily Stock Trading》.pdf

《2016A Multiagent Approach to Q-Learning for Daily Stock Trading》.pdf

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《2016A Multiagent Approach to Q-Learning for Daily Stock Trading》.pdf

864 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 37, NO. 6, NOVEMBER 2007 A Multiagent Approach to Q-Learning for Daily Stock Trading Jae Won Lee, Jonghun Park, Member, IEEE, Jangmin O, Jongwoo Lee, and Euyseok Hong Abstract—The portfolio management for trading in the stock economics over a 40-year period without definitive findings, market poses a challenging stochastic control problem of signif- states that no investment system can consistently yield average icant commercial interests to finance industry. To date, many returns exceeding the average returns of a market as a whole. researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial deci- Throughout many years, finance theoreticians argue for EMH sions for daily stock trading. Many promising results have been as a basis of denouncing the techniques that attempt to find reported from the supervised learning community on the possibil- useful information about the future behavior of stock prices by ity of building a profitable trading system. More recently, several using historical data [2]. studies have shown that even the problem of integrating stock However, the assumptions underlying this hypothesis turns price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Moti- out to be unrealistic in many cases [3], and in particular, vated by this, we present a new stock trading framework that most approaches taken to testing the hypothesis were based attempts to further enhance the performance of reinforcement

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