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	Stock Trading System Based on Portfolio Beta and Evolutionary Algorithms
	Chen, Yan
	2012
页码	372-379
英文摘要	This paper proposed a new evolutionary algorithm for generating trading rules on stock market, which is called Probabilistic Genetic Network Programming (P-GNP). P-GNP represents its solutions using graph structures based on probability. It has been clarified that P-GNP works well especially in dynamic environments. In the proposed hybrid stock trading model, P-GNP is applied to generating stock trading rules using variance of fitness values and probability. The unique point is that the generalization ability of P-GNP is improved by considering the robust fitness function and the Q value of the branch obtained by Sarsa Learning. Generally speaking, the hybrid intelligent system consists of three steps, the priority selection by portfolio beta, the optimization by Genetic Relation Algorithm (GRA) and stock trading by P-GNP. In the simulations, the stock trading system is trained using the stock prices of 10 brands selected from the Nikkei 500 Index, then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed P-GNP model obtain much higher profits than the traditional methods and its effectiveness has been confirmed.
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS记录号	WOS:000310365100053
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3090]
专题	上海财经大学
作者单位	Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yan. Stock Trading System Based on Portfolio Beta and Evolutionary Algorithms[C]. 见:.

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