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Identifying Business Sectors from Stock Price Fluctuations

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  • Parameswaran Gopikrishnan
  • Bernd Rosenow
  • Vasiliki Plerou
  • H. Eugene Stanley

Abstract

Firms having similar business activities are correlated. We analyze two different cross-correlation matrices C constructed from (i) 30-min price fluctuations of 1000 US stocks for the 2-year period 1994-95 and (ii) 1-day price fluctuations of 422 US stocks for the 35-year period 1962-96. We find that the eigenvectors of C corresponding to the largest eigenvalues allow us to partition the set of all stocks into distinct subsets. These subsets are similar to conventionally-identified business sectors, and are stable for extended periods of time. Using a set of coupled stochastic differential equations, we argue how correlations between stocks might arise. Finally, we demonstrate that the sectors we identify are useful for the practical goal of finding an investment which earns a given return without exposure to unnecessary risk.

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  • Parameswaran Gopikrishnan & Bernd Rosenow & Vasiliki Plerou & H. Eugene Stanley, 2000. "Identifying Business Sectors from Stock Price Fluctuations," Papers cond-mat/0011145, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0011145
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    Cited by:

    1. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets," Papers 2211.06378, arXiv.org.
    2. Tiziana Di Matteo & Tomaso Aste, 2002. "How Does The Eurodollar Interest Rate Behave?," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 107-122.
    3. Ormerod, Paul & Mounfield, Craig, 2002. "The convergence of European business cycles 1978–2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 307(3), pages 494-504.
    4. Ivailo I. Dimov & Petter N. Kolm & Lee Maclin & Dan Y. C. Shiber, 2012. "Hidden noise structure and random matrix models of stock correlations," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 567-572, November.
    5. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "Stock Embeddings: Learning Distributed Representations for Financial Assets," Papers 2202.08968, arXiv.org.

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