IMPROVING ICA ALGORITHMS APPLIED TO PREDICTING STOCK RETURNS

J. M. Górriz, C. G. Puntonet, R. Martín-Clemente

Abstract

In this paper we improve a well known signal processing technique such as independent component analysis (ICA) or blind source separation applied to predicting multivariate financial such as portfolio of stock returns using the Vapnik-Chervonenkis theory. The key idea in ICA algorithms is to linearly map the input space series (stock returns) into a new space which contains statistically independent components. There´s a wide class of ICA algorithms however they usually fail due to their high convergence rates or their limited ability of local search, as the number of observed signals increases.

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Paper Citation


in Harvard Style

M. Górriz J., G. Puntonet C. and Martín-Clemente R. (2004). IMPROVING ICA ALGORITHMS APPLIED TO PREDICTING STOCK RETURNS . In Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE, ISBN 972-8865-15-5, pages 351-355. DOI: 10.5220/0001399803510355


in Bibtex Style

@conference{icete04,
author={J. M. Górriz and C. G. Puntonet and R. Martín-Clemente},
title={IMPROVING ICA ALGORITHMS APPLIED TO PREDICTING STOCK RETURNS},
booktitle={Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE,},
year={2004},
pages={351-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001399803510355},
isbn={972-8865-15-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE,
TI - IMPROVING ICA ALGORITHMS APPLIED TO PREDICTING STOCK RETURNS
SN - 972-8865-15-5
AU - M. Górriz J.
AU - G. Puntonet C.
AU - Martín-Clemente R.
PY - 2004
SP - 351
EP - 355
DO - 10.5220/0001399803510355