A statistical test of market efficiency based on information theory

Brouty, Xavier; Garcin, Matthieu

doi:10.1080/14697688.2023.2211108

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…Majority of Information Theory content falls into two major categories. Aldridge, Johnson and Scarlett (2019), Brouty and Garcin (2023), Chanda et al, (2020), Gallistel, Craig and Shahan (2019), Hao et al, (2021), Kumar and Gupta (2020), Mourtzis et al, (2019), Perdigã o et al, (2020, Ramos (2019), Rocchini et al, (2021), Sane, Fox-Kemper and Ullman (2023), Tschannerl et al, (2019), Zu et al, (2023) subscribe to Shannon's -Information Theory‖ of entropy and communication; Enßlin (2019), Foley (2020), Hirche, Rouzé and Franç a (2023), Radicchi et al, (2020), Stout (2021), Weijs and Ruddell (2020), Yu et al, (2020) subscribe to -Information Theory‖ of entropy, statistics and probability analysis; Merker, Williford and Rudrauf (2022), Mørch (2019), Pautz (2019) subscribe to -Integrated Information Theory‖ of consciousness which is a ‗fundamental theory of consciousness' (quoted in Zimmer, 2010); IIT is primarily a theory of the ‗level' or ‗amount' of consciousness in a system; And Dayan (2023) subscribes to metacognitive analysis of the mind. Albeit the name Information Theory, the locus of analysis is not on information per se, nor the antecedent and consequence of information.…”

Section: Critical Analysismentioning

confidence: 99%

Information Theory: Critical Analysis and Theory Construction

Liew,

Chen

2023

AJSSS

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Section: Critical Analysismentioning

confidence: 99%

Information Theory: Critical Analysis and Theory Construction

Liew,

Chen

2023

AJSSS

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“…EMH suggests that stock prices fully reflect all available information, including information about climate-related risks and events. Climate-related information, such as the potential impacts of extreme weather events on a company's operations, could lead to changes in investor perceptions and stock prices (Brouty and Garcin, 2023). In last four decades, the worldwide concern for climate change has significantly highlighted in various industries, strategies, and markets (Ullah and Asghar, 2023).…”

Section: Introductionmentioning

confidence: 99%

Climate Change Effects and Stock Market Returns

Luqman,

Farag,

Khakwani

et al. 2024

RAMSS

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To capture climate change risk at the business level, use records of performance briefings from different enterprises. This examination discovers that chances of worst climate change, the negative impact of business environment risk on market stock returns. Study also conducts a broad assessment of the empirical and theoretical literature on the influence of climate change related risks on financial market. The main aim of this analysis is to enhance our knowledge of the estimation significances of climate change risk in financial markets. It is initiated by discussing the theoretical connections between market asset pricing and climate change related risks, and then propose a hypothesis of how climate change risk drivers convey costs to enterprises and cause stock returns variations. It studies the historical climate change related events, which indicate that both climate physical effects and transition dynamics can cause a stock return volatility. Finally, the results imply that disclosures of climate change related information can support the stock market in more efficiently as pricing climate risk.

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Variance of entropy for testing time-varying regimes with an application to meme stocks

Shternshis,

Mazzarisi

2024

Decisions Econ Finan

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Shannon entropy is the most common metric for assessing the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems are typically non-stationary, leading to entropy values fluctuating over time. This paper proposes a hypothesis testing procedure to test the null hypothesis of constant Shannon entropy in time series data. The alternative hypothesis is a significant variation in entropy between successive periods. To this end, we derive an unbiased sample entropy variance, accurate up to the order $$O(n^{-4})$$ O ( n - 4 ) with n the sample size. To characterize the variance of the sample entropy, we first provide explicit formulas for the central moments of both binomial and multinomial distributions describing the distribution of the sample entropy. Second, we identify the optimal rolling window length to estimate time-varying Shannon entropy. We optimize this choice using a novel self-consistent criterion based on counting significant entropy variations over time. We corroborate our findings using the novel methodology to assess time-varying regimes of entropy for stock price dynamics by presenting a comparative analysis between meme and IT stocks in 2020 and 2021. We show that low entropy values correspond to periods when profitable trading strategies can be devised starting from the symbolic dynamics used for entropy computation, namely periods of market inefficiency.

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A statistical test of market efficiency based on information theory

Cited by 10 publications

References 51 publications

Information Theory: Critical Analysis and Theory Construction

Information Theory: Critical Analysis and Theory Construction

Climate Change Effects and Stock Market Returns

Variance of entropy for testing time-varying regimes with an application to meme stocks

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