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A neurofuzzy model for stock market trading

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  • Stelios Bekiros

Abstract

This study investigates the forecasting ability of trading strategies based on neurofuzzy models, recurrent neural networks and linear regression models. The performance of the trading strategies was considered upon the prediction of the direction-of-change of the market in case of Nikkei 255 Index returns. The results demonstrate that the profitability of the trading rule based on the neurofuzzy model is consistently higher to that of the other models as well as of a buy and hold strategy during bear market periods.

Suggested Citation

  • Stelios Bekiros, 2007. "A neurofuzzy model for stock market trading," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 53-57.
    • Handle: RePEc:taf:apeclt:v:14:y:2007:i:1:p:53-57
    DOI: 10.1080/13504850500425717
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    References listed on IDEAS

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    1. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-533, October.
    2. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    3. Allen, Helen & Taylor, Mark P, 1990. "Charts, Noise and Fundamentals in the London Foreign Exchange Market," Economic Journal, Royal Economic Society, vol. 100(400), pages 49-59, Supplemen.
    4. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
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    Cited by:

    1. Ritika Chopra & Gagan Deep Sharma, 2021. "Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    2. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    3. Aurthur Vimalachandran Thomas Jayachandran, 2022. "The financial crash of 2020 and the retail trader’s boon: a correlation between sentiment and technical analysis," SN Business & Economics, Springer, vol. 2(6), pages 1-8, June.

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