Stock market uncertainty and economic fundamentals: an entropy-based approach

Ahn, Kwangwon; Lee, Daeyong; Sohn, Sungbin; Yang, Biao

doi:10.1080/14697688.2019.1579922

Cited by 40 publications

(25 citation statements)

References 34 publications

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“…The third category of studies, and perhaps the one that draws the nearest to our analysis, is addressing systemic risk explicitly and using various types of entropy frameworks to assess it. Ahn et al [ 26 ] study the effects of stock market uncertainty -quantified through the Shannon entropy- on systemic risk and economic activities in China. Their study provides evidence that uncertainty shocks in stock markets determine an increase in systemic risk, and have a negative impact on economic fundamentals.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

Lupu

Călin

Zeldea

et al. 2020

Entropy

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We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects of the entropy indicator for the systemic risk measures computed on the 24 sectors that compose the STOXX 600 index. Our results show that several sectors have a high proclivity for generating spillovers. In general, the largest influences are delivered by Capital Goods, Banks, Diversified Financials, Insurance, and Real Estate. We also bring detailed evidence on the sectors that are the most pregnable to spillovers and on those that represent the main contributors of spillovers.

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Section: Literature Reviewmentioning

confidence: 99%

A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

Lupu

Călin

Zeldea

et al. 2020

Entropy

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“…Next, we also considered symbolic time series analysis (STSA) as an alternative since it is common in various research fields, such as physics, information theory, and finance [27,28]. Based on the time-varying fluctuations in return series, STSA converts a real value into a series of symbols.…”

Section: Transfer Entropymentioning

confidence: 99%

“…Subsequently, the binary numbers were transformed to a series of sequence bundles. Following Ahn et al [28], we defined the size of a rolling window to quantify the sequence of binary numbers and then converted all of the corresponding sequence bundles into a new series of decimal numbers. Finally, transfer entropy could be obtained from the two decimal series.…”

Section: Transfer Entropymentioning

confidence: 99%

Information Flow between Bitcoin and Other Investment Assets

Jang

Kim

et al. 2019

Entropy

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This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.

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“…Other approaches such as the generalized Hurst exponent 20 , Kolmogorov complexity 21 or LZ complexity 22 have been applied to gain information on the stock markets which is different from the knowledge acquired by mainstream econometrics. Similarly, the entropy concept has been used in several of its conceptions to provide a different perspective from moment statistics or classical modeling of the stock markets 23–25 and finances in general 26 . All those approaches provide different information and cannot be compared directly, albeit complementarily, they provide useful insights on the behavior of the markets.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the randomness of the stock markets

Delgado-Bonal

2019

Sci Rep

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Randomness has been mathematically defined and quantified in time series using algorithms such as Approximate Entropy (ApEn). Even though ApEn is independent of any model and can be used with any time series, as the markets have different statistical values, it cannot be applied directly to make comparisons between series of financial data. In this paper, we develop further the use of Approximate Entropy to quantify the existence of patterns in evolving data series, defining a measure to allow comparisons between time series and epochs using a maximum entropy approach. We apply the methodology to the stock markets as an example of its application, showing that the number of patterns changed for the six analyzed markets depending on the economic situation, in agreement with the Adaptive Markets Hypothesis.

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Stock market uncertainty and economic fundamentals: an entropy-based approach

Cited by 40 publications

References 34 publications

A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

Information Flow between Bitcoin and Other Investment Assets

Quantifying the randomness of the stock markets

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