Nonlinear forecast of financial time series through dynamical calendar correction
A 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.
Volume (Year): 2 (2006)
Issue (Month): 5 (September)
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFL20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/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.
- Josef Lakonishok, Seymour Smidt, 1988. "Are Seasonal Anomalies Real? A Ninety-Year Perspective," Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 403-425.
When requesting a correction, please mention this item's handle: RePEc:taf:apfelt:v:2:y:2006:i:5:p:337-340. 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 references are entirely missing, you can add them using this form.