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The informational efficiency: the emerging markets versus the developed markets

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  • Wiston Adrian Risso

Abstract

The purpose of the article is to study the difference between the informational efficiency levels between the emerging and developed markets. We applied the symbolic time series analysis approach and the Shannon entropy in order to measure and rank the informational efficiency of 20 stock markets from 1 July 1997 to 14 December 2007. Three Asian markets take the first positions as the most efficient (Taiwan, Japan and Singapore). The last positions are taken by the ex-socialist countries as the most inefficient markets. The latter could be due to the lack of experience of these markets.

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  • Wiston Adrian Risso, 2009. "The informational efficiency: the emerging markets versus the developed markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(5), pages 485-487.
    • Handle: RePEc:taf:apeclt:v:16:y:2009:i:5:p:485-487
    DOI: 10.1080/17446540802216219
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    Cited by:

    1. Basu, Anup K. & Huang-Jones, Jason, 2015. "The performance of diversified emerging market equity funds," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 35(C), pages 116-131.
    2. Kugiumtzis, Dimitris & Tsimpiris, Alkiviadis, 2010. "Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i05).
    3. 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.
    4. Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.
    5. Aurelio F. Bariviera & Luciano Zunino & M. Belen Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "Efficiency and credit ratings: a permutation-information-theory analysis," Papers 1509.01839, arXiv.org.
    6. Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Libor at crossroads: Stochastic switching detection using information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 172-182.
    7. repec:jss:jstsof:33:i05 is not listed on IDEAS
    8. 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.
    9. Kumari, Vineeta & Kumar, Gaurav & Pandey, Dharen Kumar, 2023. "Are the European Union stock markets vulnerable to the Russia–Ukraine war?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    10. Alexandru Todea & Dorina Lazar, 2012. "Global Crisis and Relative Efficiency: Empirical Evidence from Central and Eastern European Stock Markets," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 4(1), pages 045-053, June.
    11. Park, Jin Suk & Newaz, Mohammad Khaleq, 2021. "Liquidity and short-run predictability: Evidence from international stock markets," Global Finance Journal, Elsevier, vol. 50(C).
    12. Lucio Maria Calcagnile & Fulvio Corsi & Stefano Marmi, 2016. "Entropy and efficiency of the ETF market," Papers 1609.04199, arXiv.org.
    13. Weili Huang & Guangqi Ma & Xuemeng Chen, 2019. "Does the Level of Environmental Uncertainty Matter in the Effect of Returnee CEO on Innovation? Evidence from Panel Threshold Analysis," Sustainability, MDPI, vol. 11(9), pages 1-16, May.
    14. Boubaker, Sabri & Goodell, John W. & Pandey, Dharen Kumar & Kumari, Vineeta, 2022. "Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 48(C).
    15. 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).
    16. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2016. "Crude Oil Market And Geopolitical Events: An Analysis Based On Information-Theory-Based Quantifiers," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 21(1), pages 41-51, May.
    17. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
    18. Lucio Maria Calcagnile & Fulvio Corsi & Stefano Marmi, 2020. "Entropy and Efficiency of the ETF Market," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 143-184, January.
    19. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    20. Liu, Jian & Jiang, Ting & Ye, Ze, 2021. "Information efficiency research of China's carbon markets," Finance Research Letters, Elsevier, vol. 38(C).
    21. 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.
    22. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2015. "Time-dependent scaling patterns in high frequency financial data," Papers 1508.07428, arXiv.org, revised Dec 2015.

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