Compact Representations of Market Securities Using Smooth Component Extraction

Independent Component Analysis (ICA) is a statistical method for expressing an observed set of random vectors as a linear combination of statistically independent components. This paper tackles the task of comparing two ICA algorithms, in terms of their efficiency for compact representation of market securities. A recently developed sequential blind signal extraction algorithm, SmoothICA, is contrasted to a classical implementation of ICA, FastICA. SmoothICA uses an additional 2nd order constraint aiming at identifying temporally smooth components in the data set. This paper demonstrates the superiority of this novel smooth component extraction algorithm in terms of global and local approximation capability, applied to a portfolio of 60 NASDAQ securities, by utilizing common ordering algorithms for financial signals.