What drives stock returns?-an independent component analysis

The paper discusses the application of a signal processing technique known as independent component analysis (ICA), also called blind source separation, to multivariate financial time series. The key idea of ICA is to linearly map observed multivariate time series (such as a portfolio of stocks) into a new space of components that are statistically independent. The authors apply ICA to daily returns of the 28 largest Japanese stocks and compare the ICA results to principal component analysis. Their results indicate that the estimated ICs fall into two categories, (i) infrequent but large shocks (responsible for the major changes in the stock prices), and (ii) frequent but rather small fluctuations (contributing little to the overall level of the stocks). They show that the overall stock price can be reconstructed surprisingly well by thresholding the weighted ICs and using, on average, only one such shock per quarter. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price does not resemble the original one. The technique of ICA is shown to be a potentially powerful method to analyze and understand driving mechanisms in financial time series