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Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

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  • Bekiros, Stelios D.

Abstract

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called "chartists" use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.

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  • Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:1:p:285-293
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    4. Derhami, Shahab & Smith, Alice E., 2017. "An integer programming approach for fuzzy rule-based classification systems," European Journal of Operational Research, Elsevier, vol. 256(3), pages 924-934.
    5. Bekiros, Stelios & Marcellino, Massimiliano, 2013. "The multiscale causal dynamics of foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 282-305.
    6. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    7. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.
    8. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi, November.
    9. Xiao, Haohan & Xu, Meng, 2022. "Modelling bidding behaviors in shared parking auctions considering anticipated regrets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 88-106.
    10. Jie Zou & Jiashu Lou & Baohua Wang & Sixue Liu, 2022. "A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks," Papers 2212.02721, arXiv.org, revised Jul 2023.
    11. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
    12. Jiang, Zhong-Zhong & Fang, Shu-Cherng & Fan, Zhi-Ping & Wang, Dingwei, 2013. "Selecting optimal selling format of a product in B2C online auctions with boundedly rational customers," European Journal of Operational Research, Elsevier, vol. 226(1), pages 139-153.
    13. Stelios Bekiros, 2014. "Detecting nonlinear dependencies in foreign exchange markets: A multistep filtering approach," Working Papers 2014-182, Department of Research, Ipag Business School.
    14. Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.

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