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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.

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  • 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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    Cited by:

    1. Devi, Sandhya, 2018. "Financial Portfolios based on Tsallis Relative Entropy as the Risk Measure," MPRA Paper 91614, University Library of Munich, Germany.
    2. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & de Oliveira, Wilson & Stosic, Tatijana, 2016. "Foreign exchange rate entropy evolution during financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 233-239.
    3. Christophe Schinckus, 2011. "What can econophysics contribute to financial economics?," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 58(2), pages 147-163, June.
    4. Brandouy, Olivier & Delahaye, Jean-Paul & Ma, Lin & Zenil, Hector, 2014. "Algorithmic complexity of financial motions," Research in International Business and Finance, Elsevier, vol. 30(C), pages 336-347.
    5. Daniel Chiew & Judy Qiu & Sirimon Treepongkaruna & Jiping Yang & Chenxiao Shi, 2019. "The predictive ability of the expected utility-entropy based fund rating approach: A comparison investigation with Morningstar ratings in US," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-22, April.
    6. Sandhya Devi, 2019. "Financial Portfolios based on Tsallis Relative Entropy as the Risk Measure," Papers 1901.04945, arXiv.org, revised Mar 2019.
    7. Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.
    8. Mihaly Ormos & David Zibriczky, 2015. "Entropy-Based Financial Asset Pricing," Papers 1501.01155, arXiv.org.
    9. Mihály Ormos & Dávid Zibriczky, 2014. "Entropy-Based Financial Asset Pricing," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-21, December.
    10. Nathan Lassance & Frédéric Vrins, 2021. "Minimum Rényi entropy portfolios," Annals of Operations Research, Springer, vol. 299(1), pages 23-46, April.
    11. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    12. Shternshis, Andrey & Mazzarisi, Piero & Marmi, Stefano, 2022. "Measuring market efficiency: The Shannon entropy of high-frequency financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    13. Andrey Shternshis & Stefano Marmi, 2023. "Price predictability at ultra-high frequency: Entropy-based randomness test," Papers 2312.16637, arXiv.org, revised Dec 2023.
    14. Busu, Cristian & Busu, Mihail, 2019. "Modeling the predictive power of the singular value decomposition-based entropy. Empirical evidence from the Dow Jones Global Titans 50 Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    15. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    16. Liang, Yingjie & Chen, Wen, 2015. "A cumulative entropy method for distribution recognition of model error," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 729-735.
    17. Noe Rodriguez-Rodriguez & Octavio Miramontes, 2022. "Shannon entropy: an econophysical approach to cryptocurrency portfolios," Papers 2210.02633, arXiv.org.
    18. Sandhya Devi & Sherman Page, 2022. "Tsallis Relative entropy from asymmetric distributions as a risk measure for financial portfolios," Papers 2205.13625, arXiv.org.
    19. Grilli, Luca & Santoro, Domenico, 2020. "Boltzmann Entropy in Cryptocurrencies: A Statistical Ensemble Based Approach," MPRA Paper 99591, University Library of Munich, Germany.
    20. Wang, Yu & Shang, Pengjian, 2020. "Complexity analysis of time series based on generalized fractional order cumulative residual distribution entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    21. Schinckus, Christophe, 2009. "Economic uncertainty and econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4415-4423.
    22. Ferreira, Paulo, 2015. "Entropy, competitiveness and UEFA football ranking," MPRA Paper 63132, University Library of Munich, Germany.
    23. Dora Almeida & Andreia Dionísio & Isabel Vieira & Paulo Ferreira, 2022. "Uncertainty and Risk in the Cryptocurrency Market," JRFM, MDPI, vol. 15(11), pages 1-17, November.

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