A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market
This 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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 12 (2005)
Issue (Month): 5 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAEL20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAEL20|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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 1731-1764, December.
- Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
- 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,"
Elsevier, vol. 69(1), pages 89-94, October.
- Fernando Fernández-Rodríguez & Christian González-Martel* & Simón Sosvilla-Rivero, "undated". "On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market," Working Papers 99-07, FEDEA.
- Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
- 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.
- 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.
- Gencay, R & Stengos, T, 1996. "Technical Trading Rules and the Size of the Risk Premium in Security Returns," Working Papers 1996-11, University of Guelph, Department of Economics and Finance.
- 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.
- Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
- Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:12:y:2005:i:5:p:303-308. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.