Free Access
| 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 | |
DOI: 10.1051/ejess:2000110
European Journal of Economic and Social Systems 14 N
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,
debodt@fin.ucl.ac.be
3Université catholique de Louvain, CERTI, 3 pl. du Levant, 1348
Louvain-la-Neuve, Belgium,
verleysen@dice.ucl.ac.be.
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
Copyright EDP Sciences 2000