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Neural Network Linear Forecasts for Stock Returns

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  • Kanas, Angelos

Abstract

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.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal International Journal of Finance & Economics.

Volume (Year): 6 (2001)
Issue (Month): 3 (July)
Pages: 245-54

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Handle: RePEc:ijf:ijfiec:v:6:y:2001:i:3:p:245-54

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Cited by:
  1. Paresh Kumar Narayan, 2005. "Are the Australian and New Zealand stock prices nonlinear with a unit root?," Applied Economics, Taylor and Francis Journals, vol. 37(18), pages 2161-2166.
  2. Georgios Kouretas & Eleni Constantinou & Robert Georgiades & Avo Kazandjian, 2005. "Regime Switching and Artificial Neural Network Forecasting of the Cyprus Stock Exchange Daily Returns," Money Macro and Finance (MMF) Research Group Conference 2005 46, Money Macro and Finance Research Group.
  3. Shively, Philip A., 2007. "Asymmetric temporary and permanent stock-price innovations," Journal of Empirical Finance, Elsevier, vol. 14(1), pages 120-130, January.
  4. 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.
  5. 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.
  6. 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.
  7. Shively, Philip A., 2003. "The nonlinear dynamics of stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 505-517.

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