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The relative efficiency of stockmarkets

Author

Listed:
  • Sergio Da Silva

    (Department of Economics, Federal University of Santa Catarina, Brazil)

  • Raul Matsushita

    (Department of Statistics, University of Brasilia, Brazil)

  • Ricardo Giglio

    (Department of Economics, Federal University of Santa Catarina, Brazil)

Abstract

Financial economists usually assess market efficiency in absolute terms. This is a shortcoming. One way of dealing with the relative efficiency of markets is to resort to the efficiency interpretation provided by algorithmic complexity theory. This paper employs such an approach in order to rank 36 stock exchanges and 37 individual company stocks in terms of their relative efficiency.

Suggested Citation

  • Sergio Da Silva & Raul Matsushita & Ricardo Giglio, 2008. "The relative efficiency of stockmarkets," Economics Bulletin, AccessEcon, vol. 7(6), pages 1-12.
    • Handle: RePEc:ebl:ecbull:eb-08g10001
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    References listed on IDEAS

    as
    1. Meredith Beechey & David Gruen & James Vickery, 2000. "The Efficient Market Hypothesis: A Survey," RBA Research Discussion Papers rdp2000-01, Reserve Bank of Australia.
    2. Ching-Wei Tan, 1999. "Estimating the Complexity Function of Financial Time Series: An Estimation Based on Predictive Stochastic Complexity," Computing in Economics and Finance 1999 1143, Society for Computational Economics.
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    Citations

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    Cited by:

    1. Da Silva, Sergio, 2015. "Financial Market Efficiency Should be Gauged in Relative Rather than Absolute Terms," MPRA Paper 64497, University Library of Munich, Germany.
    2. Yi, Ronghua & Chang, Yu-Wei & Xing, Wen & Chen, Jun, 2019. "Comparing relative valuation efficiency between two stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 159-167.
    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. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Espinosa-Paredes, Gilberto, 2012. "Is the US stock market becoming weakly efficient over time? Evidence from 80-year-long data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5643-5647.
    5. Kei Takeuchi & Akimichi Takemura & Masayuki Kumon, 2011. "New Procedures for Testing Whether Stock Price Processes are Martingales," Computational Economics, Springer;Society for Computational Economics, vol. 37(1), pages 67-88, January.
    6. Olivier Brandouy & Jean-Paul Delahaye & Lin Ma, 2015. "Estimating the Algorithmic Complexity of Stock Markets," Papers 1504.04296, arXiv.org.
    7. Apartsin, Yevgenia & Maymon, Yafit & Cohen, Yuval & Singer, Gonen, 2013. "Nationality and risk attitude: Testing differences and similarities of investors' behavior in selected financial markets," Global Finance Journal, Elsevier, vol. 24(2), pages 114-118.
    8. 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.
    9. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    10. 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.
    11. Giglio, Ricardo & Da Silva, Sergio, 2009. "Ranking the stocks listed on Bovespa according to their relative efficiency," MPRA Paper 22720, University Library of Munich, Germany.
    12. 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.

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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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