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An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market

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  • Andreia Dionisio
  • Rui Menezes
  • Diana A. Mendes

Abstract

In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena. Among these it should be emphasized the increasing interrelationship between physics and financial theory. In this field the analysis of uncertainty, which is crucial in financial analysis, can be made using measures of physics statistics and information theory, namely the Shannon entropy. One advantage of this approach is that the entropy is a more general measure than the variance, since it accounts for higher order moments of a probability distribution function. An empirical application was made using data collected from the Portuguese Stock Market.

Suggested Citation

  • Andreia Dionisio & Rui Menezes & Diana A. Mendes, 2005. "An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market," Papers physics/0509250, arXiv.org, revised Sep 2005.
  • Handle: RePEc:arx:papers:physics/0509250
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    1. Jean-Philippe Bouchaud & Marc Potters & Jean-Pierre Aguilar, 1997. "Missing Information and Asset Allocation," Papers cond-mat/9707042, arXiv.org.
    2. Zellner, Arnold, 1996. "Models, prior information, and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 75(1), pages 51-68, November.
    3. Kojadinovic, Ivan, 2004. "Agglomerative hierarchical clustering of continuous variables based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 269-294, June.
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