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Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis

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  • Ashok Chanabasangouda Patil

    (Symbiosis Institute of Business Management, Pune, Maharashtra 412115, India)

  • Shailesh Rastogi

    (Symbiosis Institute of Business Management, Pune, Maharashtra 412115, India)

Abstract

The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH).

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  • Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
  • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:10:p:248-:d:431596
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    as
    1. Rizvi, Syed Aun R. & Arshad, Shaista, 2017. "Analysis of the efficiency–integration nexus of Japanese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 296-308.
    2. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    3. Jushan Bai, 1994. "Least Squares Estimation Of A Shift In Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 453-472, September.
    4. Pierre Perron & Yohei Yamamoto, 2019. "Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model," Econometrics, MDPI, vol. 7(2), pages 1-11, May.
    5. Di Matteo, T. & Aste, T. & Dacorogna, M.M., 2003. "Scaling behaviors in differently developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 183-188.
    6. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    7. Kristoufek, Ladislav & Vosvrda, Miloslav, 2013. "Measuring capital market efficiency: Global and local correlations structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 184-193.
    8. Dutta, Srimonti & Ghosh, Dipak & Chatterjee, Sucharita, 2016. "Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 188-201.
    9. Suominen, Matti, 2001. "Trading Volume and Information Revelation in Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(4), pages 545-565, December.
    10. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    11. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    12. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    13. Ferreira, Paulo, 2019. "Assessing the relationship between dependence and volume in stock markets: A dynamic analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 90-97.
    14. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    15. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    16. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    17. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    18. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    19. Gozbasi, Onur & Kucukkaplan, Ilhan & Nazlioglu, Saban, 2014. "Re-examining the Turkish stock market efficiency: Evidence from nonlinear unit root tests," Economic Modelling, Elsevier, vol. 38(C), pages 381-384.
    20. Kim, Jae H., 2009. "Automatic variance ratio test under conditional heteroskedasticity," Finance Research Letters, Elsevier, vol. 6(3), pages 179-185, September.
    21. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    22. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    23. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    24. Hasan, Rashid & Mohammed Salim, M., 2017. "Power law cross-correlations between price change and volume change of Indian stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 620-631.
    25. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    26. Copeland, Thomas E, 1976. "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 31(4), pages 1149-1168, September.
    27. Wei-Xing Zhou, 2008. "Multifractal detrended cross-correlation analysis for two nonstationary signals," Papers 0803.2773, arXiv.org.
    28. Chen, Shiu-Sheng, 2012. "Revisiting the empirical linkages between stock returns and trading volume," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1781-1788.
    29. 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.
    30. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    31. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "The influence of trading volume on market efficiency: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 259-265.
    32. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    33. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    34. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    35. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    36. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    37. Arshad, Shaista & Rizvi, Syed Aun R., 2015. "The troika of business cycle, efficiency and volatility. An East Asian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 158-170.
    38. Ruan, Qingsong & Jiang, Wei & Ma, Guofeng, 2016. "Cross-correlations between price and volume in Chinese gold markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 10-22.
    39. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan, 2019. "Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach," Finance Research Letters, Elsevier, vol. 28(C), pages 398-411.
    40. Ferreira, Paulo & Dionísio, Andreia & Movahed, S.M.S., 2017. "Assessment of 48 Stock markets using adaptive multifractal approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 730-750.
    41. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    42. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    43. Hiremath, Gourishankar S & Kumari, Jyoti, 2014. "Stock returns predictability and the adaptive market hypothesis in emerging markets: evidence from India," MPRA Paper 58378, University Library of Munich, Germany.
    44. Karpoff, Jonathan M, 1986. "A Theory of Trading Volume," Journal of Finance, American Finance Association, vol. 41(5), pages 1069-1087, December.
    45. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
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