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Non-linear forecasts of stock returns

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  • Angelos Kanas

    (University of Crete, Greece)

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    Abstract

    Following recent non-linear extensions of the present-value model, this paper examines the out-of-sample forecast performance of two parametric and two non-parametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime switching, whereas the non-parametric are the nearest-neighbour and the artificial neural network models. We focused on the US stock market using annual observations spanning the period 1872-1999. Evaluation of forecasts was based on two criteria, namely forecast accuracy and forecast encompassing. In terms of accuracy, the Markov and the artificial neural network models produce at least as accurate forecasts as the other models. In terms of encompassing, the Markov model outperforms all the others. Overall, both criteria suggest that the Markov regime switching model is the most preferable non-linear empirical extension of the present-value model for out-of-sample stock return forecasting. Copyright © 2003 John Wiley & Sons, Ltd.

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 22 (2003)
    Issue (Month): 4 ()
    Pages: 299-315

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    Handle: RePEc:jof:jforec:v:22:y:2003:i:4:p:299-315

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    3. Reason Lesego Machete, 2011. "Early Warning with Calibrated and Sharper Probabilistic Forecasts," Papers 1112.6390, arXiv.org, revised Jan 2012.
    4. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    5. Bozos, Konstantinos & Nikolopoulos, Konstantinos, 2011. "Forecasting the value effect of seasoned equity offering announcements," European Journal of Operational Research, Elsevier, vol. 214(2), pages 418-427, October.
    6. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    7. Wegener, Christian & von Nitzsch, Rüdiger & Cengiz, Cetin, 2010. "An advanced perspective on the predictability in hedge fund returns," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2694-2708, November.

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