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Nonlinear forecast of financial time series through dynamical calendar correction

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  • Alexandros Leontitsis
  • Costas Siriopoulos

Abstract

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.

Suggested Citation

  • Alexandros Leontitsis & Costas Siriopoulos, 2006. "Nonlinear forecast of financial time series through dynamical calendar correction," Applied Financial Economics Letters, Taylor & Francis Journals, vol. 2(5), pages 337-340.
    • Handle: RePEc:taf:raflxx:v:2:y:2006:i:5:p:337-340
    DOI: 10.1080/17446540500461786
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