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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
