On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market
AbstractIn 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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Bibliographic InfoPaper provided by FEDEA in its series Working Papers with number 99-07.
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Technical trading rules; Neural network models; Security markets;
Find related papers by JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-12-14 (All new papers)
- NEP-ETS-1999-12-14 (Econometric Time Series)
- NEP-FIN-1999-12-14 (Finance)
- NEP-IND-1999-12-14 (Industrial Organization)
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