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Efficiency of the Moscow Stock Exchange before 2022

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  • 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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    Cited by:

    1. Andrey Shternshis & Stefano Marmi, 2023. "Price predictability at ultra-high frequency: Entropy-based randomness test," Papers 2312.16637, arXiv.org, revised May 2024.
    2. 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).
    3. 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.
    4. 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.

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