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Information Journal Paper

Title

PREDICT THE TREND OF STOCK PRICES USING XCS BASED ON GENETIC ALGORITHMS AND REINFORCEMENT LEARNING

Pages

  39-54

Keywords

XCS (EXTENDED CLASSIFIER SYSTEMS)Q2

Abstract

 Developments for investigation in the area of artificial intelligence and machine learning, especially in the field of evolutionary computation not only enabled us for having more effective analysis of data, but also providing the ability to use it for understanding any underlying model of financial markets. Economists, statisticians, and finance teachers were always interested in the development and experiment of STOCK PRICE behavioral models. XCS is a compound system of genetic algorithm and reinforcement learning, which has on-line interaction with the environment and the ability of learning from its own experience. In this study we will provide a model which predicts the movements of next day‘s STOCK PRICE on one of the corporations in Tehran stock exchange based on historical data and different technical indicators by using XCS. Then, efficiency of the proposed model was measured in comparison with the random walk model. Results showed that the proposed model has more predicting accuracy in comparison with that random walk model.

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    Cite

    APA: Copy

    PAKRAEI, AHMAD REZA. (2017). PREDICT THE TREND OF STOCK PRICES USING XCS BASED ON GENETIC ALGORITHMS AND REINFORCEMENT LEARNING. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), 10(34), 39-54. SID. https://sid.ir/paper/200312/en

    Vancouver: Copy

    PAKRAEI AHMAD REZA. PREDICT THE TREND OF STOCK PRICES USING XCS BASED ON GENETIC ALGORITHMS AND REINFORCEMENT LEARNING. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)[Internet]. 2017;10(34):39-54. Available from: https://sid.ir/paper/200312/en

    IEEE: Copy

    AHMAD REZA PAKRAEI, “PREDICT THE TREND OF STOCK PRICES USING XCS BASED ON GENETIC ALGORITHMS AND REINFORCEMENT LEARNING,” FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), vol. 10, no. 34, pp. 39–54, 2017, [Online]. Available: https://sid.ir/paper/200312/en

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