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A neurofuzzy model for stock market trading


  • Stelios Bekiros


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

    1. 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.
    2. 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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