Neural network linear forecasts for stock returns

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. A simple nonparametric test of predictive performance, Journal of Business and Economic Statistics10: 461–465) test, and forecast encompassing, using the Clements and Hendry (1998. Forecasting Economic Time Series. Cambridge University Press: Cambridge, UK) 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.