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Decision Technologies for Computational Finance pp 309–322Cite as

Discovering Structure in Finance Using Independent Component Analysis

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Part of the book series: Advances in Computational Management Science ((AICM,volume 2))

Abstract

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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Author information

Authors and Affiliations

  1. Brain Science Institute, The Institute of Physical and Chemical Research (RIKEN), 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan

    Andrew D. Back

  2. Department of Information Systems Leonard N. Stern School of Business, New York University, 44 West Fourth Street MEC 9-74, New York, NY, 10012, USA

    Andreas S. Weigend

Authors
  1. Andrew D. Back
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  2. Andreas S. Weigend
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Editor information

Editors and Affiliations

  1. London Business School, UK

    Apostolos-Paul N. Refenes

  2. London Business School, UK

    Andrew N. Burgess

  3. Oregon Graduate Institute, Portland, USA

    John E. Moody

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© 1998 Springer Science+Business Media Dordrecht

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Back, A.D., Weigend, A.S. (1998). Discovering Structure in Finance Using Independent Component Analysis. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_24

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  • DOI: https://doi.org/10.1007/978-1-4615-5625-1_24

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-8309-3

  • Online ISBN: 978-1-4615-5625-1

  • eBook Packages: Springer Book Archive

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