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Extracting meaningful information from financial data

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  • Rajković, Milan

Abstract

A method for extracting information carrying eigenvalues of the correlation matrix is presented based on the topological transformation of the manifold defined by the data matrix itself. The transformation, performed with the use of the minimum spanning tree and the barycentric transformation, linearizes the topological manifold and the singular value decomposition is performed on the final data matrix corresponding to the linearized hypersurface. It is shown that the results of this procedure are superior to the results of the random matrix theory as applied to the financial data. The method may be used independently or in conjunction with the random matrix theory. Other possible uses of the method are mentioned.

Suggested Citation

  • Rajković, Milan, 2000. "Extracting meaningful information from financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 383-395.
    • Handle: RePEc:eee:phsmap:v:287:y:2000:i:3:p:383-395
    DOI: 10.1016/S0378-4371(00)00377-0
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    References listed on IDEAS

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    1. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
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    Cited by:

    1. Mateos, Diego M. & Zozor, Steeve & Olivares, Felipe, 2020. "Contrasting stochasticity with chaos in a permutation Lempel–Ziv complexity — Shannon entropy plane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Zozor, S. & Ravier, P. & Buttelli, O., 2005. "On Lempel–Ziv complexity for multidimensional data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 285-302.
    3. Stephan Süss, 2012. "The pricing of idiosyncratic risk: evidence from the implied volatility distribution," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(2), pages 247-267, June.

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