IDEAS home Printed from https://ideas.repec.org/a/spr/jqecon/v20y2022i2d10.1007_s40953-022-00295-x.html
   My bibliography  Save this article

An Entropy Approach to Measure the Dynamic Stock Market Efficiency

Author

Listed:
  • Subhamitra Patra

    (VIT Business School, Vellore Institute of Technology)

  • Gourishankar S. Hiremath

    (Indian Institute of Technology Kharagpur)

Abstract

We measure stock market efficiency by drawing the comprehensive sample from Asia, Europe, Africa, North–South America, and Pacific Ocean regions and rank the cross-regional stock markets according to their level of informational efficiency. The study period spans from January 1, 1994, to August 3, 2017. We employ the approximate entropy approach and find that stock market efficiency evolves over the period. The degree and nature of evolution vary across regions and the development stage of the markets. The global, regional, domestic economic, and non-economic factors influence the adaptive nature of the stock markets. The emerging stock markets have improved efficiency by financial liberalization policy but are adversely affected by global shocks. The estimates validate the relevance of the adaptive market framework to describe the rejection of random walk without excess returns. The results suggest the growing presence of technical analysis and active portfolio managers. The emerging markets in Asia hold policy lessons for their peers. The findings suggest that global investors need to overcome the homogeneity bias as returns opportunities exist within the region and types of markets.

Suggested Citation

  • Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    • Handle: RePEc:spr:jqecon:v:20:y:2022:i:2:d:10.1007_s40953-022-00295-x
    DOI: 10.1007/s40953-022-00295-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40953-022-00295-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40953-022-00295-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    2. Ladislav Kristoufek & Miloslav Vosvrda, 2014. "Measuring capital market efficiency: long-term memory, fractal dimension and approximate entropy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(7), pages 1-9, July.
    3. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    4. 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.
    5. Canarella, Giorgio & Miller, Stephen M. & Nourayi, Mahmoud M., 2013. "Firm profitability: Mean-reverting or random-walk behavior?," Journal of Economics and Business, Elsevier, vol. 66(C), pages 76-97.
    6. Hasbrouck, Joel, 2007. "Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading," OUP Catalogue, Oxford University Press, number 9780195301649.
    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. Charfeddine, Lanouar & Khediri, Karim Ben, 2016. "Time varying market efficiency of the GCC stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 487-504.
    9. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    10. Hiremath, Gourishankar S. & Kattuman, Paul, 2017. "Foreign portfolio flows and emerging stock market: Is the midnight bell ringing in India?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 544-558.
    11. Stoian, Andreea & Iorgulescu, Filip, 2020. "Fiscal policy and stock market efficiency: An ARDL Bounds Testing approach," Economic Modelling, Elsevier, vol. 90(C), pages 406-416.
    12. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    13. Zalewska-Mitura, Anna & Hall, Stephen G., 1999. "Examining the first stages of market performance: a test for evolving market efficiency," Economics Letters, Elsevier, vol. 64(1), pages 1-12, July.
    14. Ito, Mikio & Sugiyama, Shunsuke, 2009. "Measuring the degree of time varying market inefficiency," Economics Letters, Elsevier, vol. 103(1), pages 62-64, April.
    15. Gourishankar S Hiremath & Bandi Kamaiah, 2010. "Nonlinear Dependence in Stock Returns: Evidences from India," Journal of Quantitative Economics, The Indian Econometric Society, vol. 8(1), pages 69-85, January.
    16. 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.
    17. Gourishankar S. Hiremath, 2018. "Macroeconomics and Markets in Developing and Emerging Economies by Ashima Goyal," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 309-312, March.
    18. Hiremath, Gourishankar S. & Narayan, Seema, 2016. "Testing the adaptive market hypothesis and its determinants for the Indian stock markets," Finance Research Letters, Elsevier, vol. 19(C), pages 173-180.
    19. Lim, Kian-Ping, 2007. "Ranking market efficiency for stock markets: A nonlinear perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 445-454.
    20. Gourishankar S. Hiremath, 2014. "Indian Stock Market," SpringerBriefs in Economics, Springer, edition 127, number 978-81-322-1590-5, June.
    21. Graciela L. Kaminsky & Richard K. Lyons & Sergio L. Schmukler, 2001. "Mutual Fund Investment in Emerging Markets: An Overview," The World Bank Economic Review, World Bank, vol. 15(2), pages 315-31-340.
    22. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    23. Verheyden, Tim & De Moor, Lieven & Van den Bossche, Filip, 2015. "Towards a new framework on efficient markets," Research in International Business and Finance, Elsevier, vol. 34(C), pages 294-308.
    24. Bae, Kee-Hong & Ozoguz, Arzu & Tan, Hongping & Wirjanto, Tony S., 2012. "Do foreigners facilitate information transmission in emerging markets?," Journal of Financial Economics, Elsevier, vol. 105(1), pages 209-227.
    25. Darbellay, Georges A & Wuertz, Diethelm, 2000. "The entropy as a tool for analysing statistical dependences in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 429-439.
    26. Les Gulko, 1999. "The Entropic Market Hypothesis," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 293-329.
    27. Lin, Hai & Lo, Ingrid & Qiao, Rui, 2021. "Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market," Journal of Banking & Finance, Elsevier, vol. 133(C).
    28. Ben Rejeb, Aymen & Boughrara, Adel, 2013. "Financial liberalization and stock markets efficiency: New evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 17(C), pages 186-208.
    29. Hooy, Chee-Wooi & Lim, Kian-Ping, 2013. "Is market integration associated with informational efficiency of stock markets?," Journal of Policy Modeling, Elsevier, vol. 35(1), pages 29-44.
    30. 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.
    31. Yang, Baochen & Xue, Fangzhan & Su, Yunpeng & Yan, Cheng, 2019. "Is informational inefficiency priced in stock markets? A comparison between the U.S. and Chinese cases," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 222-238.
    32. Jian Zhou & Jin Man Lee, 2013. "Adaptive market hypothesis: evidence from the REIT market," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1649-1662, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samuel Tabot ENOW, 2023. "A Non-linear Dependency Test for Market Efficiency: Evidence from International Stock Markets," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(1), pages 1-12.
    2. Nurhuda Nizar & Ahmad Danial Zainudin & Ali Albada & Chua Mei Shan, 2024. "Forecasting Short-Term FTSE Bursa Malaysia Using WEKA," Information Management and Business Review, AMH International, vol. 16(2), pages 104-114.
    3. Naeem, Muhammad Abubakr & Chatziantoniou, Ioannis & Gabauer, David & Karim, Sitara, 2024. "Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Radhika Prosad Datta, 2023. "Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns," Papers 2312.12788, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Biswabhusan Bhuyan & Subhamitra Patra & Ranjan Kumar Bhuian, 2020. "Market Adaptability and Evolving Predictability of Stock Returns: An Evidence from India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(4), pages 605-619, December.
    2. Pınar Evrim Mandacı & F. Dilvin Taskın & Zeliha Can Ergun, 2019. "Adaptive Market Hypothesis," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 84-101.
    3. Jasman Tuyon & Zamri Ahmada, 2016. "Behavioural finance perspectives on Malaysian stock market efficiency," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 16(1), pages 43-61, March.
    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. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    6. Boya, Christophe M., 2019. "From efficient markets to adaptive markets: Evidence from the French stock exchange," Research in International Business and Finance, Elsevier, vol. 49(C), pages 156-165.
    7. Urquhart, Andrew & McGroarty, Frank, 2016. "Are stock markets really efficient? Evidence of the adaptive market hypothesis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 39-49.
    8. Xiong, Xiong & Meng, Yongqiang & Li, Xiao & Shen, Dehua, 2019. "An empirical analysis of the Adaptive Market Hypothesis with calendar effects:Evidence from China," Finance Research Letters, Elsevier, vol. 31(C).
    9. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    10. Semei Coronado-Ram'irez & Pedro Celso-Arellano & Omar Rojas, 2014. "Adaptive Market Efficiency of Agricultural Commodity Futures Contracts," Papers 1412.8017, arXiv.org, revised Mar 2015.
    11. Navaz Naghavi & Muhammad Shujaat Mubarik & Devinder Kaur, 2018. "Financial Liberalization And Stock Market Efficiency: Measuring The Threshold Effects Of Governance," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-24, December.
    12. Shahzad, Syed Jawad Hussain & Hernandez, Jose Areola & Hanif, Waqas & Kayani, Ghulam Mujtaba, 2018. "Intraday return inefficiency and long memory in the volatilities of forex markets and the role of trading volume," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 433-450.
    13. Assaf, Ata & Kristoufek, Ladislav & Demir, Ender & Kumar Mitra, Subrata, 2021. "Market efficiency in the art markets using a combination of long memory, fractal dimension, and approximate entropy measures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    14. Kristoufek, Ladislav & Vosvrda, Miloslav, 2016. "Gold, currencies and market efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 27-34.
    15. Hiremath, Gourishankar S. & Kattuman, Paul, 2017. "Foreign portfolio flows and emerging stock market: Is the midnight bell ringing in India?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 544-558.
    16. 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.
    17. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "Efficiency of Thai stock markets: Detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 204-209.
    18. Al-Khazali, Osamah & Mirzaei, Ali, 2017. "Stock market anomalies, market efficiency and the adaptive market hypothesis: Evidence from Islamic stock indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 190-208.
    19. Ghazani, Majid Mirzaee & Ebrahimi, Seyed Babak, 2019. "Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices," Finance Research Letters, Elsevier, vol. 30(C), pages 60-68.
    20. 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.

    More about this item

    Keywords

    EMH; Entropy; AMH; Adaptive markets; Financial crises; Portfolio management;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G4 - Financial Economics - - Behavioral Finance
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G01 - Financial Economics - - General - - - Financial Crises

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jqecon:v:20:y:2022:i:2:d:10.1007_s40953-022-00295-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.