A neurofuzzy model for stock market trading
This study investigates the forecasting ability of trading strategies based on neurofuzzy models, recurrent neural networks and linear regression models. The performance of the trading strategies was considered upon the prediction of the direction-of-change of the market in case of Nikkei 255 Index returns. The results demonstrate that the profitability of the trading rule based on the neurofuzzy model is consistently higher to that of the other models as well as of a buy and hold strategy during bear market periods.
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): 14 (2007)
Issue (Month): 1 ()
|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.:
- 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.
- Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:14:y:2007:i:1:p:53-57. 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: (Chris Longhurst)
If references are entirely missing, you can add them using this form.