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A multiscale entropy approach for market efficiency

Author

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  • Alvarez-Ramirez, Jose
  • Rodriguez, Eduardo
  • Alvarez, Jesus

Abstract

Motivated by the recently evolutionary economic theories, we propose to study market efficiency from an informational entropy viewpoint. The basic idea is that, rather than being an all-or-none concept as in classic economic theories, market efficiency changes over time and over time horizons. Within this framework, market efficiency is measured in terms of the patterns contained in the price changes sequence relative to the patterns in a random sequence. In line with evolutionary finance ideas, the empirical results for the Dow Jones Index showed that the degree of market efficiency varies over time and is dependent of the time scale. In general, the DJI is more efficient for shorter (about days) than for longer (about months and quarters) time scales. On the other hand, the market efficiency exhibits a cyclic behavior with two dominant periods of about 4.5 and 22years. It is apparent that the 4.5-year cycle is related to inventory (Kitchin-type) effects, while the 22-year cycle to structure inversion (Kondriatev-type) cycles.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:finana:v:21:y:2012:i:c:p:64-69
    DOI: 10.1016/j.irfa.2011.12.001
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    References listed on IDEAS

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    Cited by:

    1. Billio, Monica & Casarin, Roberto & Costola, Michele & Pasqualini, Andrea, 2016. "An entropy-based early warning indicator for systemic risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 42-59.
    2. Alonso-Rivera, Angélica & Cruz-Aké, Salvador & Venegas-Martínez, Francisco, 2014. "Impact of Monetary Policy on Financial Markets Efficiency and Speculative Bubbles: A Non-linear Entropy-based Approach," MPRA Paper 56127, University Library of Munich, Germany.
    3. Gu, Rongbao & Xiong, Wei & Li, Xinjie, 2015. "Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 103-113.
    4. 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.
    5. 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.
    6. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & Stosic, Tatijana, 2016. "Correlations of multiscale entropy in the FX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 52-61.

    More about this item

    Keywords

    Market efficiency; Entropy; Dow Jones Index; Cycle;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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