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Detecting correlation in stock market

Author

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  • Wichard, Jörg D.
  • Merkwirth, Christian
  • Ogorzałek, Maciej

Abstract

We present a new method for detecting dependencies in the stock market. In order to find hidden correlations in the daily returns, we build cross prediction models and use the normalized modeling error as a generalized correlation measure that extends the concept of the classical correlation matrix.

Suggested Citation

  • Wichard, Jörg D. & Merkwirth, Christian & Ogorzałek, Maciej, 2004. "Detecting correlation in stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 308-311.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:308-311
    DOI: 10.1016/j.physa.2004.06.140
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
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    Cited by:

    1. Xi Zhang & Jiawei Shi & Di Wang & Binxing Fang, 2018. "Exploiting Investors Social Network for Stock Prediction in China's Market," Papers 1801.00597, arXiv.org.

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