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A statistical test of market efficiency based on information theory

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Xavier Brouty, Matthieu Garcin

Statistical Finance · Submitted 25 Aug 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We apply it to a real dataset of stock indices, single stock, and cryptocurrency, for which we are able to determine at each date whether the efficient market hypothesis is to be rejected, with respect to a given confidence level.

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arXiv ID2208.11976 (version 1)
Pith ID6GEBEQGL, Pith's permanent record of this paper and its review
CitationMatthieu Garcin, Xavier Brouty. "A statistical test of market efficiency based on information theory." arXiv:2208.11976 (2022). https://pith.science/paper/6GEBEQGL
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,028 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.07 · human participants 0.02 · controlled comparison 0.17 · randomized assignment 0.01 · states quantitative result 0.13 · compares with prior results 0.07 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; .

We determine the amount of information contained in a time series of price returns at a given time scale, by using a widespread tool of the information theory, namely the Shannon entropy, applied to a symbolic representation of this time series. By deriving the exact and the asymptotic distribution of this market information indicator in the case where the efficient market hypothesis holds, we develop a statistical test of market efficiency. We apply it to a real dataset of stock indices, single stock, and cryptocurrency, for which we are able to determine at each date whether the efficient market hypothesis is to be rejected, with respect to a given confidence level.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We determine the amount of information contained in a time series of price returns at a given time scale, by using a widespread tool of the information theory, namely the Shannon entropy, applied to a symbolic representation of this time series. method 0.98 0.90 0.04 asserted 1.00
s1 By deriving the exact and the asymptotic distribution of this market information indicator in the case where the efficient market hypothesis holds, we develop a statistical test of market efficiency. contribution 0.51 0.95 0.25 asserted 1.00
s2 We apply it to a real dataset of stock indices, single stock, and cryptocurrency, for which we are able to determine at each date whether the efficient market hypothesis is to be rejected, with respect to a given confidence level. contribution 0.48 0.96 0.39 asserted 0.99

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from analysis of existing data 0.97.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We determine the amount of information contained in a time series of price returns at a given time scale, by using a widespread tool of the information theory, namely the Shannon entropy, applied to a symbolic representation of this time series. By deriving the exact and the asymptotic distribution of this market information indicator in the case where the efficient market hypothesis holds, we develop a statistical test of market efficiency. We apply it to a real dataset of stock indices, single stock, and cryptocurrency, for which we are able to determine at each date whether the efficient market hypothesis is to be rejected, with respect to a given confidence level.

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