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Linear Regression versus Backpropagation Networks to Predict Quarterly Stock Market Excess Returns

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  • Hiemstra, Ypke

Abstract

This paper compares a linear model to predict quarterly stock market excess returns to several backpropagation networks. Research findings suggest that quarterly stock market returns are to some extent predictable but only marginal attention has been paid to possible nonlinearities in the return-generating process. The paper discusses input selection, elaborates on how to generate out-of-sample predictions to estimate generalization performance, motivates the choice for a particular network, examines backpropagation training, and evaluates network performance. The out-of-sample predictions are used to calculate several performance metrics and to determine added value when applying a straightforward tactical asset allocation policy. A nonparametric test is selected to evaluate generalization behavior and sensitivity analysis examines the selected network's qualitative behavior. Strong nonlinear effects appear to be absent, but the proposed backpropagation network generates an asset allocation policy that outperforms the linear model. Citation Copyright 1996 by Kluwer Academic Publishers.

Suggested Citation

  • Hiemstra, Ypke, 1996. "Linear Regression versus Backpropagation Networks to Predict Quarterly Stock Market Excess Returns," Computational Economics, Springer;Society for Computational Economics, vol. 9(1), pages 67-76, February.
    • Handle: RePEc:kap:compec:v:9:y:1996:i:1:p:67-76
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    Cited by:

    1. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    3. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    4. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.

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