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The informational efficiency and the financial crashes

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  • Risso, Wiston Adrián

Abstract

The evolution of the daily informational efficiency is measured for different stock market indices (Japanese, Malaysian, Russian, Mexican, and the US markets) by using the local entropy and the symbolic time series analysis. There is some evidence that for different stock markets, the probability of having a crash increases as the informational efficiency decreases. Further results suggest that the latter probability also increases for jumping to a less efficient market. In addition, the US stock market seems to be the most structurally efficient and the Russian is the most inefficient, maybe because is a young market, recently established in 1995.

Suggested Citation

  • Risso, Wiston Adrián, 2008. "The informational efficiency and the financial crashes," Research in International Business and Finance, Elsevier, vol. 22(3), pages 396-408, September.
  • Handle: RePEc:eee:riibaf:v:22:y:2008:i:3:p:396-408
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    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Grech, D & Mazur, Z, 2004. "Can one make any crash prediction in finance using the local Hurst exponent idea?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 133-145.
    3. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    4. Shiller, Robert J & Kon-Ya, Fumiko & Tsutsui, Yoshiro, 1996. "Why Did the Nikkei Crash? Expanding the Scope of Expectations Data Collection," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 156-164, February.
    5. Blake LeBaron, 1994. "Chaos and Nonlinear Forecastability in Economics and Finance," Finance 9411001, EconWPA.
    6. Christian Schittenkopf & Peter Tino & Georg Dorffner, 2002. "The benefit of information reduction for trading strategies," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 917-930.
    7. Bassler, Kevin E. & Gunaratne, Gemunu H. & McCauley, Joseph L., 2006. "Markov processes, Hurst exponents, and nonlinear diffusion equations: With application to finance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 343-353.
    8. McCauley, Joseph L. & Gunaratne, Gemunu H. & Bassler, Kevin E., 2007. "Hurst exponents, Markov processes, and fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 1-9.
    9. Chowdhry, Bhagwan & Goyal, Amit, 2000. "Understanding the financial crisis in Asia," Pacific-Basin Finance Journal, Elsevier, vol. 8(2), pages 135-152, May.
    10. Kevin E. Bassler & Gemunu H. Gunaratne & Joseph L. McCauley, 2006. "Markov Processes, Hurst Exponents, and Nonlinear Diffusion Equations with application to finance," Papers cond-mat/0602316, arXiv.org.
    11. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
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    1. 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.
    2. 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.
    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. 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.
    5. Alvarez-Ramirez, J. & Rodriguez, E. & Espinosa-Paredes, G., 2012. "A partisan effect in the efficiency of the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4923-4932.
    6. Parker, Edgar, 2017. "The Entropic Linkage between Equity and Bond Market Dynamics," MPRA Paper 80036, University Library of Munich, Germany.
    7. 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.
    8. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2009. "Forbidden patterns, permutation entropy and stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2854-2864.
    9. Habib, Ahsan & Hasan, Mostafa Monzur, 2017. "Business strategy, overvalued equities, and stock price crash risk," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 389-405.
    10. 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.
    11. Jamaani, Fouad & Roca, Eduardo, 2015. "Are the regional Gulf stock markets weak-form efficient as single stock markets and as a regional stock market?," Research in International Business and Finance, Elsevier, vol. 33(C), pages 221-246.
    12. Mensi, Walid & Tiwari, Aviral Kumar & Yoon, Seong-Min, 2017. "Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 135-146.
    13. 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.
    14. 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.
    15. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    16. 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.
    17. repec:eee:phsmap:v:499:y:2018:i:c:p:266-275 is not listed on IDEAS
    18. Gu, Rongbao & Chen, Xi & Li, Xinjie, 2014. "Chaos recognition and fractal analysis in the term structure of Shanghai Interbank Offered Rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 101-112.

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