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Are Spanish Ibex35 stock future index returns forecasted with non-linear models?

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  • Jorge Perez-Rodriguez
  • Salvador Torra
  • Julian Andrada-Felix

Abstract

This study employs different nonlinear models (smooth transition autoregressive models (STAR), artificial neural networks (ANN) and nearest neighbours (NN)) to study the predictability of one-step-ahead forecast returns for the Ibex35 stock future index at a one year forecast horizon. It is found that the STAR, ANN and NN models beat the random walk (RW) and linear autoregressive (AR) models in out-of-sample forecast statistical accuracy, and also when economic criteria were used in a simple trading strategy including the impact of transaction costs on trading strategy profits. Finally, the overall results suggest that the nonlinear models (particularly ANN and NN) considered for the Ibex35 stock future index appear to provide a reasonable description of asset price movements in improving returns forecasts for the chosen horizon.

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Bibliographic Info

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

Volume (Year): 15 (2005)
Issue (Month): 14 ()
Pages: 963-975

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Handle: RePEc:taf:apfiec:v:15:y:2005:i:14:p:963-975

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Cited by:
  1. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 149-171.
  2. Rico Belda, Paz, 2013. "No linealidad y asimetría en el proceso generador del Índice Ibex35/Nonlinearity and Asymmetry in the Generator Process of Ibex35 Index," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 31, pages 555-576, Septiembr.

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