Summary
Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Korn (1999) proposed five neural network model specification strategies based on different statistical procedures. In this paper, we use and adapt the Anders-Korn framework to find appropriate neural network models for financial time series prediction. The most important new issue in this context is the specification of the dynamic structure of the models, i.e. the selection of the lagged values of the input time series. A linear model is built with full dynamic structure, then its possible nonlinear extensions are tested using a statistical procedure inspired by the Anders-Korn approach. Promising results are obtained with an application to predict the monthly time series of mortgage loans purchased in The Netherlands.
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References
Anders, U. and Korn, O. (1999), Model selection in neural networks, Neural Networks, 12, 309–323.
Bishop, C.M. (1995), Neural networks for pattern recognition, Oxford University Press.
Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994), Time series analysis, forecasting and control, Prentice Hall.
Diebold F. (1998), Elements of forecasting, International Thomson Publishing. Faraway, J. and Chatfield, C. (1998), Time series forecasting with neural networks: a comparative study using the airline data, Applied Statistics 47, 231–50.
Franses, P.H. (1998), Time series models for business and economic forecasting, Cambridge University Press.
Gardin, F. and Virili, F. (1995), Nonlinear modelling of the Dutch mortgage market, Economic & Financial Computing 5(2), 131–145.
Granger, C.W.J. and Teräsvirta, T. (1993), Modelling nonlinear economic relationships, Oxford University Press.
Granger, C.W.J., Newbold, P. (1986), Forecasting economic time series, Academic Press.
Hornik, K., Stinchcombe, M. and White, H. (1989), Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–366.
Kearns, M., Mansour, Y., Ng, A.Y. and Ron, D. (1997), An experimental and theoretical comparison of model selection methods, Machine Learning, 27, 7–50.
Kennedy, P. (1997), A guide to econometrics, MIT Press.
Moody, J.E. (1994), Prediction risk and architecture selection for neural networks, in Cherkassky et al., eds., From Statistics to Neural Networks: Theory and Pattern Recognition Applications, Springer.
Refenes, A.-P. ed. (1995), Neural networks in the capital markets, Wiley.
Refenes, A.-P., Zapranis, A.D. and Utans, J. (1996), Neural model identification, variable selection and model adequacy, in Weigend et al. eds., Proceedings of NNCM 96, World Scientific Publishing.
Ripley B.D. (1995), Statistical ideas for selecting neural networks, in Kappen, B. and Gielen S. eds., Neural Networks: Artificial Intelligence and Industrial Applications, Springer, 183–190.
Ripley B.D. (1993), Statistical aspects of neural networks, in Barndorff-Nielsen, O.E., Jensen, J.L., Kendall, W.S. eds., Chaos and Networks: Statistical and Probabilistic Aspects, Chapman and Hall, 40–123.
Sarle S. (1995), Stopped training and other remedies for overfitting, in Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, 352–360.
Sarle S. (1994), Neural networks and statistical models, in Proceedings of the 19th Annual SAS Users Group International Conference, Cary (NC), SAS Institute, 1538–1550.
SAS Institute inc. (1996), SAS/ETS user’s guide ver. 6.12, SAS Institute inc. Stone M. (1974), Cross-validation choice and assessment of statistical predictions, Journal of the Royal Statistical Society, 111–147.
Stone, M. (1977), An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion cross-validation, Journal of the Royal Statistical Society, 44–47.
Swanson, N.R. and White, H. (1997a) A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks, Review of Economics and Statistics, 79, 540–550.
Swanson, N.R. and White, H. (1997b), Forecasting economic time series using adaptive versus nonadaptive and linear versus nonlinear econometric models, International Journal of Forecasting, 13, 439–461.
Teräsvirta, T., Lin, C.-F. and Granger, C.W.J. (1993), Power of the neural network linearity test, Journal of Time Series Analysis, 14(2), 209–220.
Utans, J. and Moody, J.E. (1991), Selecting neural network architectures vie the prediction risk: application to corporate bond rating prediction, in Proceedings of the First International Conference on AI Applications on Wall Street, IEEE Computer Society Press.
Venables, W.N. and Ripley, B.D. (1997), Modern applied statistics with S-PLUS, Springer.
Virili, F. and Freisleben, B. (2000), Nonstationarity and data preprocessing for neural network predictions of an economic time series, Amari, S.-I., Lee Giles, C., Gori, M., Piuri, V. eds., Proceedings of IJCNN 2000, Como, Vol. 5, 129–136.
Virili, F. and Freisleben, B. (1999), Preprocessing seasonal time series for improving neural network predictions, Boethe et al., eds., Proceedings of CIMA 99, Rochester (NY), ICSC Academic Press, 622–628.
White H. (1989), An additional hidden unit test for neglected nonlinearity in multilayer feedforward networks, in Proceedings of IJCNN, SOS Printing, 451–455.
Zapranis, A.D. and Refenes, A.-P. (1999), Principles of neural model identification, selection and adequacy, Springer.
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Virili, F., Freisleben, B. Neural Network Model Selection for Financial Time Series Prediction. Computational Statistics 16, 451–463 (2001). https://doi.org/10.1007/s001800100078
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DOI: https://doi.org/10.1007/s001800100078