tailieunhanh - Independent Component Analysis - Chapter 24: Other Applications

It is tempting to try ICA on financial data. There are many situations in which parallel financial time series are available, such as currency exchange rates or daily returns of stocks, that may have some common underlying factors. ICA might reveal some driving mechanisms that otherwise remain hidden. In a study of a stock portfolio [22], it was found that ICA is a complementary tool to principal component analysis (PCA), allowing the underlying structure of the data to be more readily observed | Independent Component Analysis. Aapo Hyvarinen Juha Karhunen Erkki Oja Copyright 2001 John Wiley Sons Inc. ISBNs 0-471-40540-X Hardback 0-471-22131-7 Electronic 24 Other Applications In this chapter we consider some further applications of independent component analysis ICA including analysis of financial time series and audio signal separation. FINANCIAL APPLICATIONS Finding hidden factors in financial data It is tempting to try ICA on financial data. There are many situations in which parallel financial time series are available such as currency exchange rates or daily returns of stocks that may have some common underlying factors. ICA might reveal some driving mechanisms that otherwise remain hidden. In a study of a stock portfolio 22 it was found that ICA is a complementary tool to principal component analysis PCA allowing the underlying structure of the data to be more readily observed. If one could find the maximally independent mixtures of the original stocks . portfolios this might help in minimizing the risk in the investment strategy. In 245 we applied ICA on a different problem the cashflow of several stores belonging to the same retail chain trying to find the fundamental factors common to all stores that affect the cashflow. Thus the effect of the factors specific to any particular store . the effect of the managerial actions taken at the individual store and in its local environment could be analyzed. In this case the mixtures in the ICA model are parallel financial time series x i with i indexing the individual time series i 1 . m and denoting discrete time. 441 442 OTHERAPPLICATIONS We assume the instantaneous ICA model Xj i J2aySj i 3 for each time series i . Thus the effect of each time-varying underlying factor or independent component Sj t on the measured time series is approximately linear. The assumption of having some underlying independent components in this specific application may not be unrealistic. For example .