Discovering Structure in Finance Using Independent Component Analysis

Independent component analysis is a new signal processing technique. In this paper we apply it to a portfolio of Japanese stock price returns over three years of daily data and compare the results obtained using principal component analysis. The results indicate that the independent components 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). The small number of major shocks indicate turning points in the time series and when used to reconstruct the stock prices, give good results in terms of morphology. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price does not show the same results at all. Independent component analysis is shown to be a potentially powerful method of analysing and understanding driving mechanisms in financial time series.

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