Non-linear forecasts of stock returns

Non-linear forecasts of stock returns

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Article ID: iaor20081005
Country: United Kingdom
Volume: 22
Issue: 4
Start Page Number: 299
End Page Number: 315
Publication Date: Jul 2003
Journal: International Journal of Forecasting
Authors:
Keywords: financial
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.

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