A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market
AbstractThis paper studies predictability and profitability of using neural networks (NN) in the Spanish security market. This is carried out through a hybrid approximation which entails evolving a genetic algorithm in order to obtain an optimal NN's architecture. To that end, (NNs) forecasts are transformed into a simple trading strategy, whose profitability is evaluated against a simple buy-and-hold strategy.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Economics Letters.
Volume (Year): 12 (2005)
Issue (Month): 5 ()
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- Gencay, R & Stengos, T, 1996.
"Technical Trading Rules and the Size of the Risk Premium in Security Returns,"
1996-11, University of Guelph, Department of Economics and Finance.
- Gencay Ramazan & Stengos Thanasis, 1997. "Technical Trading Rules and the Size of the Risk Premium in Security Returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(2), pages 1-14, July.
- 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.
- Brock, W. & Lakonishok, J. & Lebaron, B., 1991.
"Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns,"
90-22, Wisconsin Madison - Social Systems.
- Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
- McNelis, Paul & McAdam, Peter, 2004.
"Forecasting inflation with thick models and neural networks,"
Working Paper Series
0352, European Central Bank.
- McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
- Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
- Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
- Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
- Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
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