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Neural Network Linear Forecasts for Stock Returns


  • Kanas, Angelos


We examine the out-of-sample performance of monthly returns forecasts for the Dow Jones and the FT, using a linear and an artificial neural network (ANN) model. The comparison of out-of-sample forecasts is done on the basis of directional accuracy, using the Pesaran and Timmermann (1992) test, and forecast encompassing, using the Clements and Hendry (1998) approach. While both models perform badly in terms of predicting the directional change of the two indices, the ANN forecasts can explain the forecast errors of the linear model while the linear model cannot explain the forecast errors of the ANN for both indices. Thus, the ANN forecasts are preferable to linear forecasts, indicating that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the underlying relation between stock returns and fundamentals is nonlinear. Copyright @ 2001 by John Wiley & Sons, Ltd. All rights reserved.

Suggested Citation

  • Kanas, Angelos, 2001. "Neural Network Linear Forecasts for Stock Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 245-254, July.
  • Handle: RePEc:ijf:ijfiec:v:6:y:2001:i:3:p:245-54

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    References listed on IDEAS

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    Cited by:

    1. Edirisinghe, N.C.P. & Zhang, X., 2007. "Generalized DEA model of fundamental analysis and its application to portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3311-3335, November.
    2. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
    3. Shively, Philip A., 2003. "The nonlinear dynamics of stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 505-517.
    4. Cai, Charlie X. & Kyaw, Khine & Zhang, Qi, 2012. "Stock index return forecasting: The information of the constituents," Economics Letters, Elsevier, vol. 116(1), pages 72-74.
    5. Shively, Philip A., 2007. "Asymmetric temporary and permanent stock-price innovations," Journal of Empirical Finance, Elsevier, vol. 14(1), pages 120-130, January.
    6. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    7. Lee, Chien-Chiang & Lee, Jun-De & Lee, Chi-Chuan, 2010. "Stock prices and the efficient market hypothesis: Evidence from a panel stationary test with structural breaks," Japan and the World Economy, Elsevier, vol. 22(1), pages 49-58, January.
    8. Paresh Kumar Narayan, 2005. "Are the Australian and New Zealand stock prices nonlinear with a unit root?," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2161-2166.
    9. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.

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