Improving ICA Algorithms Applied to Predicting Stock Returns

In this paper we improve a well known signal processing technique such as independent component analysis (ICA) or blind source separation applied to predicting multivariate financial such as portfolio of stock returns using the Vapnik-Chervonenkis theory. The key idea in ICA algorithms is to linearly map the input space series (stock returns) into a new space which contains statistically independent components. There ́s a wide class of ICA algorithms however they usually fail due to their high convergence rates or their limited ability of local search, as the number of observed signals increases.