Multiplicative ICA algorithm for interaction analysis in financial markets

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. © 2012 Springer-Verlag Berlin Heidelberg.