Nonlinear forecast of financial time series through dynamical calendar correction
AbstractA method is presented that takes into account the day-of-the-week and the turn-of-the-month effect and the holiday effect and embodies them to neural network forecasting. It adjusts the time series in order to make its dynamics less distorted. After a predicted value is calculated by the network, the inverse adjustment is made to obtain the final predicted value. If there are no calendar effects on the time series this method has approximately the same performance as its classic counterpart. Empirical results are presented, based on NASDAQ Composite, and TSE 300 Composite indices using daily returns form 1984 to 2003.
Download InfoIf 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.
Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.
Volume (Year): 2 (2006)
Issue (Month): 5 (September)
Contact details of provider:
Web page: http://www.tandfonline.com/RAFL20
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.:
- T. C. Mills & C. Siriopoulos & R. N. Markellos & D. Harizanis, 2000. "Seasonality in the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 137-142.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.