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The Entropic Market Hypothesis

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  • LES GULKO

    (287 Hamilton Avenue, #3C, Stamford, CT 06902, USA)

Abstract

Information theory teaches that entropy is the fundamental limit for data compression, and electrical engineers routinely use entropy as a criterion for efficient storage and transmission of information. Since modern financial theory teaches that competitive market prices store and transmit information with some efficiency, should financial economists be concerned with entropy? This paper presents a market model in which entropy emerges endogenously as a condition for the operational efficiency of price discovery while entropy maximization emerges as a condition for the informational efficiency of market prices. The maximum-entropy formalism makes the efficient market hypothesis operational and testable. This formalism is used to establish that entropic markets admit no arbitrage and support both the Ross arbitrage pricing theory and the Black–Scholes stock option pricing model.

Suggested Citation

  • Les Gulko, 1999. "The Entropic Market Hypothesis," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 293-329.
    • Handle: RePEc:wsi:ijtafx:v:02:y:1999:i:03:n:s0219024999000170
    DOI: 10.1142/S0219024999000170
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    Cited by:

    1. Liu, Jian & Jiang, Ting & Ye, Ze, 2021. "Information efficiency research of China's carbon markets," Finance Research Letters, Elsevier, vol. 38(C).
    2. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    3. Wang, Jingjing & Wang, Xiaoyang, 2021. "COVID-19 and financial market efficiency: Evidence from an entropy-based analysis," Finance Research Letters, Elsevier, vol. 42(C).
    4. Ebenezer Asem & Vishaal Baulkaran & Rossitsa Yalamova & Xiaofei Zhang, 2017. "Internal Market Efficiency, Market Co-movement, and Cross-Market Efficiency: The Case of Hong Kong and Shanghai Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(4), pages 253-267, December.
    5. Zunino, Luciano & Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Monitoring the informational efficiency of European corporate bond markets with dynamical permutation min-entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 1-9.
    6. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
    7. Romain Bocher, 2023. "Causal Entropic Forces, Narratives and Self-organisation of Capital Markets," Journal of Interdisciplinary Economics, , vol. 35(2), pages 172-190, July.
    8. Cassio Neri & Lorenz Schneider, 2011. "A Family of Maximum Entropy Densities Matching Call Option Prices," Papers 1102.0224, arXiv.org.
    9. Kunal Saha & Vinodh Madhavan & G. R. Chandrashekhar, 2022. "Effect of COVID-19 on ETF and index efficiency: evidence from an entropy-based analysis," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 347-359, April.
    10. Wang, Xiaoyang, 2022. "Efficient markets are more connected: An entropy-based analysis of the energy, industrial metal and financial markets," Energy Economics, Elsevier, vol. 111(C).
    11. C. Neri & L. Schneider, 2012. "The Impact of the Prior Density on a Minimum Relative Entropy Density: A Case Study with SPX Option Data," Papers 1201.2616, arXiv.org, revised Sep 2013.
    12. Fan, Xinghua & Li, Shasha & Tian, Lixin, 2016. "Complexity of carbon market from multi-scale entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 79-85.
    13. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Alvarez, Jesus, 2012. "A multiscale entropy approach for market efficiency," International Review of Financial Analysis, Elsevier, vol. 21(C), pages 64-69.
    14. Cassio Neri & Lorenz Schneider, 2012. "Maximum entropy distributions inferred from option portfolios on an asset," Finance and Stochastics, Springer, vol. 16(2), pages 293-318, April.
    15. Andrey Shternshis & Piero Mazzarisi, 2024. "Variance of entropy for testing time-varying regimes with an application to meme stocks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 47(1), pages 215-258, June.
    16. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.

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