Evidence map›Paper›PMID 36141070›Full record

ArticleEntropy (Basel, Switzerland)2022

Efficiency of the Moscow Stock Exchange before 2022.

Andrey Shternshis, Piero Mazzarisi, Stefano Marmi

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Andrey ShternshisQuantitative Finance Research Group, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.ORCID 0000-0002-9299-369X
Piero MazzarisiQuantitative Finance Research Group, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.ORCID 0000-0002-7198-513X
Stefano MarmiQuantitative Finance Research Group, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.ORCID 0000-0001-5105-8876

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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 stock 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 that it belongs to.

Indexed as

Kullback–Leibler divergencemarket efficiencyprice stalenessShannon entropystock market clusteringvolatility estimation

Identifiers

PMID36141070
PMCPMC9497593

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.