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

Fernando Fernández-Rodríguez, Christian Gonzalez-Martel () and Simon Sosvilla-Rivero

No 99-07, Working Papers from FEDEA

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.

Keywords: Technical trading rules; Neural network models; Security markets (search for similar items in EconPapers)
JEL-codes: C53 G10 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-fin and nep-ind
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Journal Article: On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:fda:fdaddt:99-07

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