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Using Sound to Identify Correlations in Market Data

  • Conference paper
Auditory Display (CMMR 2009, ICAD 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5954))

Abstract

Despite intensive study, a comprehensive understanding of the structure of capital market trading data remains elusive. The one known application of audification to market price data reported in 1990 that it was difficult to interpret the results probably because the market does not resonate according to acoustic laws. This paper reports on some techniques for transforming the data so it does resonate; so audification can be used as a means of identifying autocorrelation in capital market trading data. Also reported are some experiments in which the data is sonified using a homomorphic modulation technique. The results obtained indicate that the technique may have a wider application to other similarly structured time-series data.

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Author information

Authors and Affiliations

  1. Sonic Communications Researh Group, University of Canberra,  

    David Worrall

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  1. David Worrall
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Editor information

Editors and Affiliations

  1. CNRS - LMA, 31 Chemin Joseph Aiguier, CNRS-LMA, 31 Chemin Joseph Aiguier, 13402, Marseille Cedex 20, France

    Sølvi Ystad

  2. Université Aix-Marseille, 38 bd. Charles Livon, Marseille, France

    Mitsuko Aramaki

  3. CNRS-LMA, 31, Chemin Joseph Aiguier, 13402, Marseille Cedex 20, France

    Richard Kronland-Martinet

  4. Aalborg University Esbjerg, Niels Bohr Vej 8, 6700, Esbjerg, Denmark

    Kristoffer Jensen

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Worrall, D. (2010). Using Sound to Identify Correlations in Market Data. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K. (eds) Auditory Display. CMMR ICAD 2009 2009. Lecture Notes in Computer Science, vol 5954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12439-6_11

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  • DOI: https://doi.org/10.1007/978-3-642-12439-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12438-9

  • Online ISBN: 978-3-642-12439-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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