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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.

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Bibliographic Info

Article provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.

Volume (Year): 2 (2006)
Issue (Month): 5 (September)
Pages: 337-340

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Handle: RePEc:taf:apfelt:v:2:y:2006:i:5:p:337-340

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  1. 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.
  2. 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.
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Cited by:
  1. Julijana Angelovska, 2013. "An Econometric Analysis of Market Anomaly - Day of the Week Effect on a Small Emerging Market," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(1), pages 314-322, January.

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