Abstract
In this article we present a new method for the analysis of dependencies in case of multivariate time series. In this approach, we assume that the set of time series representing the various financial instruments creates a multidimensional variable. Such a multidimensional variable is decomposed into independent components which enable to analyze the morphology of given financial instruments and to identify the hidden interdependencies. We propose a new multiplicative version of the Natural Gradient ICA algorithm that could be used in automated trading systems or modeling environments. The presented method is tested on real stock markets data.
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© 2012 Springer-Verlag Berlin Heidelberg
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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2012). Multiplicative ICA Algorithm for Interaction Analysis in Financial Markets. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_72
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DOI: https://doi.org/10.1007/978-3-642-29350-4_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
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