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Efficiency of the Moscow Stock Exchange before 2022

Author

Listed:
  • Andrey Shternshis
  • Piero Mazzarisi
  • Stefano Marmi
Abstract
This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We have developed a simple method for estimating volatility and price staleness in empirical data, in order to filter out such regularity patterns from return time series. The resulting financial time series of stocks' returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback-Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector it belongs.

Suggested Citation

  • Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.
    • Handle: RePEc:arx:papers:2207.10476
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    References listed on IDEAS

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    1. Kim, Jae H. & Shamsuddin, Abul, 2008. "Are Asian stock markets efficient? Evidence from new multiple variance ratio tests," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 518-532, June.
    2. 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.
    3. Alexandru Mandes, 2016. "Algorithmic and High-Frequency Trading Strategies: A Literature Review," MAGKS Papers on Economics 201625, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Lucio Maria Calcagnile & Fulvio Corsi & Stefano Marmi, 2020. "Entropy and Efficiency of the ETF Market," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 143-184, January.
    5. Brownlees, C.T. & Gallo, G.M., 2006. "Financial econometric analysis at ultra-high frequency: Data handling concerns," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2232-2245, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Bernard Bollen, 2015. "What should the value of lambda be in the exponentially weighted moving average volatility model?," Applied Economics, Taylor & Francis Journals, vol. 47(8), pages 853-860, February.
    8. A. Dionisio & R. Menezes & D. A. Mendes, 2006. "An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 161-164, March.
    9. Linton, Oliver & Smetanina, Ekaterina, 2016. "Testing the martingale hypothesis for gross returns," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 664-689.
    10. 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.
    11. Genaro Sucarrat & Alvaro Escribano, 2018. "Estimation of log-GARCH models in the presence of zero returns," The European Journal of Finance, Taylor & Francis Journals, vol. 24(10), pages 809-827, July.
    12. Wiston Adrian Risso, 2009. "The informational efficiency: the emerging markets versus the developed markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(5), pages 485-487.
    13. Kolokolov, Aleksey & Livieri, Giulia & Pirino, Davide, 2020. "Statistical inferences for price staleness," Journal of Econometrics, Elsevier, vol. 218(1), pages 32-81.
    14. K. Ahn & D. Lee & S. Sohn & B. Yang, 2019. "Stock market uncertainty and economic fundamentals: an entropy-based approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1151-1163, July.
    15. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
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    Cited by:

    1. Andrey Shternshis & Piero Mazzarisi, 2022. "Variance of entropy for testing time-varying regimes with an application to meme stocks," Papers 2211.05415, arXiv.org, revised Jun 2023.
    2. Andrey Shternshis & Piero Mazzarisi, 2024. "Variance of entropy for testing time-varying regimes with an application to meme stocks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 47(1), pages 215-258, June.
    3. Andrey Shternshis & Stefano Marmi, 2023. "Price predictability at ultra-high frequency: Entropy-based randomness test," Papers 2312.16637, arXiv.org, revised May 2024.
    4. Lavín, Jaime F. & Valle, Mauricio A. & Magner, Nicolás S., 2024. "Stock market pattern recognition using symbol entropy analysis," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).

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