Extracting Stock Multi-order Rules via Employing a Network Structure and Backward Q-Learning

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Alimoradi Mohammad Reza; Hosseinzadeh Kashan ali

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Journal: INVESTMENT KNOWLEDGE Year:2019 | Volume:8 | Issue:30 Page(s): 115-138

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Information Journal Paper

Title

Extracting Stock Multi-order Rules via Employing a Network Structure and Backward Q-Learning

Author(s)

Alimoradi Mohammad Reza | Hosseinzadeh Kashan ali | Issue Writer Certificate

Keywords

League championship algorithmQ1

Multi-order ruleQ1

Technical AnalysisQ1

Reinforcement learningQ1

Abstract

Traders in stock market consider stock information in the past few days as well as the current day information when making decision about selling or buying stock. To imitate stock traders’ style of decision-making, in this article, League championship algorithm (LCA) equipped with teams which have network structure has been introduced to extract Multi-order rules. Multi-order rules would be extracted by LCA which not only contain the current day information, but also information of the previous days. Thus, a memory to store useful information has been created for each rule. To evaluate the model, 20 shares of companies in different industrial parts of Tehran stock exchange are used. In the testing simulation, the proposed model shows higher profits or lower losses than the buy & hold and genetic network programming models.

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APA: Copy

Alimoradi, Mohammad Reza, & Hosseinzadeh Kashan, ali. (2019). Extracting Stock Multi-order Rules via Employing a Network Structure and Backward Q-Learning. INVESTMENT KNOWLEDGE, 8(30 ), 115-138. SID. https://sid.ir/paper/388702/en

Vancouver: Copy

Alimoradi Mohammad Reza, Hosseinzadeh Kashan ali. Extracting Stock Multi-order Rules via Employing a Network Structure and Backward Q-Learning. INVESTMENT KNOWLEDGE[Internet]. 2019;8(30 ):115-138. Available from: https://sid.ir/paper/388702/en

IEEE: Copy

Mohammad Reza Alimoradi, and ali Hosseinzadeh Kashan, “Extracting Stock Multi-order Rules via Employing a Network Structure and Backward Q-Learning,” INVESTMENT KNOWLEDGE, vol. 8, no. 30 , pp. 115–138, 2019, [Online]. Available: https://sid.ir/paper/388702/en

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