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On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market

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  • Fernando Fernández-Rodríguez
  • Christian González-Martel*
  • Simón Sosvilla-Rivero

Abstract

In this paper we investigate the profitability of a simple technical trading rule based on Artificial Neural Networks (ANNs). Our results, based on applying this investment strategy to the General Index of the Madrid Stock Market, suggest that, in absence of trading costs, the technical trading rule is always superior to a buy-and-hold strategy for both "bear" market and "stable" market episodes. On the other hand, we find that the buy-and-hold strategy generates higher returns than the trading rule based on ANN only for a "bull" market subperiod.

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Paper provided by FEDEA in its series Working Papers with number 99-07.

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Handle: RePEc:fda:fdaddt:99-07

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Keywords: Technical trading rules; Neural network models; Security markets;

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  1. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers, California Los Angeles - Applied Econometrics 29, California Los Angeles - Applied Econometrics.
  2. Fernando Fernandez-Rodriguez & Simon Sosvilla-Rivero & Maria Dolores Garcia-Artiles, 1997. "Using nearest neighbour predictors to forecast the Spanish stock market," Investigaciones Economicas, Fundación SEPI, Fundación SEPI, vol. 21(1), pages 75-91, January.
  3. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
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