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Shannon entropy: an econophysical approach to cryptocurrency portfolios

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  • Noe Rodriguez-Rodriguez
  • Octavio Miramontes

Abstract

Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide online availability, increasing capitalization and potential profits. In the econophysics tradition we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions but heavy--tailed distributions instead. Entropy measures are also applied showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.

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  • Noe Rodriguez-Rodriguez & Octavio Miramontes, 2022. "Shannon entropy: an econophysical approach to cryptocurrency portfolios," Papers 2210.02633, arXiv.org.
    • Handle: RePEc:arx:papers:2210.02633
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    1. Imen Mahmoud & Kamel Naoui, 2017. "Measuring Systematic and Specific Risk: Approach Mean-Entropy," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 7(3), pages 42-60.
    2. Benoit Mandelbrot, 1967. "The Variation of Some Other Speculative Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 393-393.
    3. A. Dionisio & R. Menezes & D. A. Mendes, 2006. "An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 161-164, March.
    4. Galina Deeva, 2017. "Comparing Entropy and Beta as Measures of Risk in Asset Pricing," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(6), pages 1889-1894.
    5. Mihály Ormos & Dávid Zibriczky, 2014. "Entropy-Based Financial Asset Pricing," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-21, December.
    6. Imen Mahmoud & Kamel Naoui, 2017. "Measuring systematic and specific risk: Approach mean-entropy," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 7(3), pages 42-60, March.
    7. Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
    8. Smith, Eric & Foley, Duncan K., 2008. "Classical thermodynamics and economic general equilibrium theory," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 7-65, January.
    9. J. Barkley Rosser, Jr., 2021. "Foundations and Applications of Complexity Economics," Springer Books, Springer, number 978-3-030-70668-5, March.
    10. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "A note on "Modelling exchange rate returns: which flexible distribution to use?"," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1777-1785, November.
    11. Ebrahimi, Nader & Maasoumi, Esfandiar & Soofi, Ehsan S., 1999. "Ordering univariate distributions by entropy and variance," Journal of Econometrics, Elsevier, vol. 90(2), pages 317-336, June.
    12. Constantino Tsallis & Celia Anteneodo & Lisa Borland & Roberto Osorio, 2003. "Nonextensive statistical mechanics and economics," Papers cond-mat/0301307, arXiv.org.
    13. Tsallis, Constantino & Anteneodo, Celia & Borland, Lisa & Osorio, Roberto, 2003. "Nonextensive statistical mechanics and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 89-100.
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