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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.

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File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850500425717&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 14 (2007)
Issue (Month): 1 ()
Pages: 53-57

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Handle: RePEc:taf:apeclt:v:14:y:2007:i:1:p:53-57

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  1. 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.
  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.
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