In this paper we consider the application of a signal processing technique, known as Independent Component Analysis (ICA). It is a method for automatically identifying a set of underlying factors in a given data set. This rapidly evolving technique is currently finding applications in several fields of interest and it is a tempting alternative to try ICA on financial data. In this article we discuss the application of ICA to multivariate financial time series such as a portfolio of stock prices. The key idea here is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs) that might reveal some driving mechanisms that otherwise remain hidden. A discussion of the method with respect to Principal Component Analysis is also considered.

Exploratory analysis of financial time series using independent component analysis

Mauro Coli;Riccardo Di Nisio;Luigi Ippoliti
2005-01-01

Abstract

In this paper we consider the application of a signal processing technique, known as Independent Component Analysis (ICA). It is a method for automatically identifying a set of underlying factors in a given data set. This rapidly evolving technique is currently finding applications in several fields of interest and it is a tempting alternative to try ICA on financial data. In this article we discuss the application of ICA to multivariate financial time series such as a portfolio of stock prices. The key idea here is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs) that might reveal some driving mechanisms that otherwise remain hidden. A discussion of the method with respect to Principal Component Analysis is also considered.
2005
953-7138-02-X
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/770604
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