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Limitations of scaling and universality in stock market data

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  • Janos Kertesz
  • Zoltan Eisler

Abstract

We present evidence, that if a large enough set of high resolution stock market data is analyzed, certain analogies with physics -- such as scaling and universality -- fail to capture the full complexity of such data. Despite earlier expectations, the mean value per trade, the mean number of trades per minute and the mean trading activity do not show scaling with company capitalization, there is only a non-trivial monotonous dependence. The strength of correlations present in the time series of traded value is found to be non-universal: The Hurst exponent increases logarithmically with capitalization. A similar trend is displayed by intertrade time intervals. This is a clear indication that stylized facts need not be fully universal, but can instead have a well-defined dependence on company size.

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  • Janos Kertesz & Zoltan Eisler, 2005. "Limitations of scaling and universality in stock market data," Papers physics/0512193, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0512193
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    File URL: http://arxiv.org/pdf/physics/0512193
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    Cited by:

    1. Qing Cai & Hai-Chuan Xu & Wei-Xing Zhou, 2016. "Taylor's Law of temporal fluctuation scaling in stock illiquidity," Papers 1610.01149, arXiv.org.

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