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|Title: ||A First Application of Independent Component Analysis to Extracting Structure from Stock Returns|
|Authors: ||Back, Andrew D.|
Weigend, Andreas S.
|Issue Date: ||Nov-1997|
|Publisher: ||Stern School of Business, New York University|
|Series/Report no.: ||IS-97-22|
|Abstract: ||This paper discusses the application of a modern signal processing technique known as independent
component analysis (ICA) or blind source separation to multivariate financial time series such as a
portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new
space of statistically independent components (ICs). This can be viewed as a factorization of the portfolio
since joint probabilities become simple products in the coordinate system of the ICs.
We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with
those obtained using principal component analysis. The 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 smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall
stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs.
In contrast, when using shocks derived from principal components instead of independent components, the
reconstructed price is less similar to the original one. Independent component analysis is a potentially powerful
method of analyzing and understanding driving mechanisms in financial markets. There are further
promising applications to risk management since ICA focuses on higher-order statistics.|
|Appears in Collections:||IOMS: Information Systems Working Papers|
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