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Neural network linear forecasts for stock returns
Author(s) -
Kanas Angelos
Publication year - 2001
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.156
Subject(s) - econometrics , stock (firearms) , economics , artificial neural network , linear model , nonparametric statistics , sample (material) , forecast error , stock market , statistics , mathematics , computer science , artificial intelligence , mechanical engineering , chemistry , chromatography , engineering , paleontology , horse , biology
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. A simple nonparametric test of predictive performance, Journal of Business and Economic Statistics 10 : 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. Copyright © 2001 John Wiley & Sons, Ltd.