Non-linear financial time series forecasting - Application to the Bel 20 stock market index

A. Lendasse; E. de Bodt; V. Wertz; M. Verleysen

doi:doi:10.1051/ejess:2000110

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Volume 14 / No 1 (2000)

E.J.E.S.S., 14 1 (2000) 81-91

Abstract

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Issue		E.J.E.S.S. Volume 14, Number 1, 2000 Neural Models in Economy and Management Science


Page(s)		81 - 91
DOI		https://doi.org/10.1051/ejess:2000110

\n This email address is being protected from spambots. You need JavaScript enabled to view it. ), CBLU, University of Leeds * revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan * with significant contributions from: Jens Lippmann, Marek Rouchal, Martin Wilck and others --> DOI: 10.1051/ejess:2000110

European Journal of Economic and Social Systems 14 N $^\circ$ 1 (2000) 81-91

Non-linear financial time series forecasting - Application to the Bel 20 stock market index

A. Lendasse, E. de Bodt, V. Wertz and M. Verleysen

¹Université catholique de Louvain, CESAME-AUTO, 4 av. G. Lemaître, 1348 Louvain-la-Neuve, Belgium, {lendasse, wertz}@auto.ucl.ac.be
²Université de Lille 2, ESA-GERME Université catholique de Louvain, IAG-FIN, Belgium, This email address is being protected from spambots. You need JavaScript enabled to view it.
³Université catholique de Louvain, CERTI, 3 pl. du Levant, 1348 Louvain-la-Neuve, Belgium, This email address is being protected from spambots. You need JavaScript enabled to view it. . Michel Verleysen is a research associate of the belgian FNRS

Abstract:

We developed in this paper a method to predict time series with non-linear tools. The specificity of the method is to use as much information as possible as input to the model (many past values of the series, many exogenous variables), to compress this information (by a non-linear method) in order to obtain a state vector of limited size, facilitating the subsequent regression and the generalization ability of the forecasting algorithm and to fit a non-linear regressor (here a RBF neural network) on the reduced vectors. We show that this method is able to find non-linear relationships in artificial and real-world financial series. On a difficult task, which consists in forecasting the tendency of the Bel 20 stock market index, we show that this method improves the results compared both to linear models and to non-linear ones where the non-linear compression is not used.

Keywords: time series forecasting, neural networks stock index prediction, curvilinear component analysis