Special Issue "Entropy-Based Applications in Economics, Finance, and Management"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 15 May 2022.

Special Issue Editor

Prof. Dr. Joanna Olbryś
E-Mail Website
Guest Editor
Faculty of Computer Science, Bialystok University of Technology, Wiejska Street 45A, 15-351 Bialystok, Poland
Interests: econometrics; statistics; empirical finance; financial economics; operations research in finance; computational economics; stock market microstructure; computing in social science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to be a forum for the presentation of entropy-based applications in economics, finance, and management studies. The concept of entropy originates from thermodynamics, but it is utilized in many research fields to characterize the complexity of a system and to investigate the information content of a probability distribution. Entropy is a general measure, and therefore, many definitions and applications of entropy have been proposed in the literature.

Areas of interest include but are not limited to the following wide range of topics:

  • Entropy-based applications in portfolio selection, asset pricing, and risk management;
  • Entropy measures as indicators for systematic risk;
  • Entropy optimization approach in economics and finance;
  • Entropy-based applications in market microstructure research;
  • Shannon theory in fuzzy multiple criteria decision-making methods (FMCDM) with applications to economic and management problems;
  • Structural entropy in Bayesian network applications in economic, finance, and management.

Theoretical and empirical contributions addressing any of the aforementioned issues are especially welcome.

Prof. Joanna Olbryś
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information entropy
  • probability entropy
  • fuzzy entropy
  • cross-entropy
  • maximum entropy
  • copula entropy
  • structural entropy
  • market microstructure
  • dimensions of market liquidity
  • portfolio selection
  • asset pricing
  • risk management
  • market efficiency
  • macroeconomic systems
  • econophysics

Published Papers (8 papers)

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Research

Article
Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19
Entropy 2022, 24(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/e24010014 - 22 Dec 2021
Viewed by 646
Abstract
The main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of [...] Read more.
The main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of epidemiological risk was carried out using the fuzzy c-means clustering method. We also used the entropy index to measure the degree of fuzziness in the classification and evaluate the uncertainty of epidemiological states. The proposed approach allowed us to identify countries’ epidemic states. Moreover, it also made it possible to determine the time of transition from one state to another, as well as to observe fluctuations during changes of state. Three COVID-19 epidemic states were identified in Europe, i.e., stabilisation, destabilisation, and expansion. The methodology is universal and can also be useful for other countries, as well as the research results being important for governments, politicians and other policy-makers working to mitigate the effects of the COVID-19 pandemic. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
Using Entropy to Evaluate the Impact of Monetary Policy Shocks on Financial Networks
Entropy 2021, 23(11), 1465; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111465 - 06 Nov 2021
Viewed by 354
Abstract
We analyze the changes in the financial network built using the Dow Jones Industrial Average components following monetary policy shocks. Monetary policy shocks are measured through unexpected changes in the federal funds rate in the United States. We determine the changes in the [...] Read more.
We analyze the changes in the financial network built using the Dow Jones Industrial Average components following monetary policy shocks. Monetary policy shocks are measured through unexpected changes in the federal funds rate in the United States. We determine the changes in the financial networks using singular value decomposition entropy and von Neumann entropy. The results indicate that unexpected positive shocks in monetary policy shocks lead to lower entropy. The results are robust to varying the window size used to construct financial networks, though they also depend on the type of entropy used. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
A Novel Extension of the Technique for Order Preference by Similarity to Ideal Solution Method with Objective Criteria Weights for Group Decision Making with Interval Numbers
Entropy 2021, 23(11), 1460; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111460 - 03 Nov 2021
Viewed by 398
Abstract
This paper presents an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with objective criteria weights for Group Decision Making (GDM) with Interval Numbers (INs). The proposed method is an alternative to popular and often used methods [...] Read more.
This paper presents an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with objective criteria weights for Group Decision Making (GDM) with Interval Numbers (INs). The proposed method is an alternative to popular and often used methods that aggregate the decision matrices provided by the decision makers (DMs) into a single group matrix, which is the basis for determining objective criteria weights and ranking the alternatives. It does not use an aggregation operator, but a transformation of the decision matrices into criteria matrices, in the case of determining objective criteria weights, and into alternative matrices, in the case of the ranking of alternatives. This ensures that all the decision makers’ evaluations are taken into account instead of their certain average. The numerical example shows the ease of use of the proposed method, which can be implemented into common data analysis software such as Excel. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
Is Bitcoin Still a King? Relationships between Prices, Volatility and Liquidity of Cryptocurrencies during the Pandemic
Entropy 2021, 23(11), 1386; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111386 - 22 Oct 2021
Viewed by 465
Abstract
We try to establish the commonalities and leadership in the cryptocurrency markets by examining the mutual information and lead-lag relationships between Bitcoin and other cryptocurrencies from January 2019 to June 2021. We examine the transfer entropy between volatility and liquidity of seven highly [...] Read more.
We try to establish the commonalities and leadership in the cryptocurrency markets by examining the mutual information and lead-lag relationships between Bitcoin and other cryptocurrencies from January 2019 to June 2021. We examine the transfer entropy between volatility and liquidity of seven highly capitalized cryptocurrencies in order to determine the potential direction of information flow. We find that cryptocurrencies are strongly interrelated in returns and volatility but less in liquidity. We show that smaller and younger cryptocurrencies (such as Ripple’s XRP or Litecoin) have started to affect the returns of Bitcoin since the beginning of the pandemic. Regarding liquidity, the results of the dynamic time warping algorithm also suggest that the position of Monero has increased. Those outcomes suggest the gradual increase in the role of privacy-oriented cryptocurrencies. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
Coherence and Entropy of Credit Cycles across the Euro Area Candidate Countries
Entropy 2021, 23(9), 1213; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091213 - 14 Sep 2021
Viewed by 468
Abstract
The pattern of financial cycles in the European Union has direct impacts on financial stability and economic sustainability in view of adoption of the euro. The purpose of the article is to identify the degree of coherence of credit cycles in the countries [...] Read more.
The pattern of financial cycles in the European Union has direct impacts on financial stability and economic sustainability in view of adoption of the euro. The purpose of the article is to identify the degree of coherence of credit cycles in the countries potentially seeking to adopt the euro with the credit cycle inside the Eurozone. We first estimate the credit cycles in the selected countries and in the euro area (at the aggregate level) and filter the series with the Hodrick–Prescott filter for the period 1999Q1–2020Q4. Based on these values, we compute the indicators that define the credit cycle similarity and synchronicity in the selected countries and a set of entropy measures (block entropy, entropy rate, Bayesian entropy) to show the high degree of heterogeneity, noting that the manifestation of the global financial crisis has changed the credit cycle patterns in some countries. Our novel approach provides analytical tools to cope with euro adoption decisions, showing how the coherence of credit cycles can be increased among European countries and how the national macroprudential policies can be better coordinated, especially in light of changes caused by the pandemic crisis. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
A Network Approach to the Study of the Dynamics of Risk Spillover in China’s Bond Market
Entropy 2021, 23(7), 920; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070920 - 20 Jul 2021
Cited by 2 | Viewed by 755
Abstract
Since 2018, the bond market has surpassed the stock market, becoming the biggest investment area in China’s security market, and the systemic risks of China’s bond market are of non-negligible importance. Based on daily interest rate data of representative bond categories, this study [...] Read more.
Since 2018, the bond market has surpassed the stock market, becoming the biggest investment area in China’s security market, and the systemic risks of China’s bond market are of non-negligible importance. Based on daily interest rate data of representative bond categories, this study conducted a dynamic analysis based on generalized vector autoregressive volatility spillover variance decomposition, constructed a complex network, and adopted the minimum spanning tree method to clarify and analyze the risk propagation path between different bond types. It is found that the importance of each bond type is positively correlated with liquidity, transaction volume, and credit rating, and the inter-bank market is the most important market in the entire bond market, while interest rate bonds, bank bonds and urban investment bonds are important varieties with great systemic importance. In addition, the long-term trend of the dynamic spillover index of China’s bond market falls in line with the pace of the interest rate adjustments. To hold the bottom line of preventing financial systemic risks of China’s bond market, standard management, strict supervision, and timely regulation of the bond markets are required, and the structural entropy, as a useful indicator, also should be used in the risk management and monitoring. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
An Entropy-Based Approach to Measurement of Stock Market Depth
Entropy 2021, 23(5), 568; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050568 - 03 May 2021
Cited by 1 | Viewed by 775
Abstract
The aim of this study is to investigate market depth as a stock market liquidity dimension. A new methodology for market depth measurement exactly based on Shannon information entropy for high-frequency data is introduced and utilized. The proposed entropy-based market depth indicator is [...] Read more.
The aim of this study is to investigate market depth as a stock market liquidity dimension. A new methodology for market depth measurement exactly based on Shannon information entropy for high-frequency data is introduced and utilized. The proposed entropy-based market depth indicator is supported by an algorithm inferring the initiator of a trade. This new indicator seems to be a promising liquidity measure. Both market entropy and market liquidity can be directly measured by the new indicator. The findings of empirical experiments for real-data with a time stamp rounded to the nearest second from the Warsaw Stock Exchange (WSE) confirm that the new proxy enables us to effectively compare market depth and liquidity for different equities. Robustness tests and statistical analyses are conducted. Furthermore, an intra-day seasonality assessment is provided. Results indicate that the entropy-based approach can be considered as an auspicious market depth and liquidity proxy with an intuitive base for both theoretical and empirical analyses in financial markets. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Article
Crowded Trades, Market Clustering, and Price Instability
Entropy 2021, 23(3), 336; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030336 - 12 Mar 2021
Viewed by 1561
Abstract
Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock, the clustering measure captures the degree of trading overlap among any two investors in [...] Read more.
Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock, the clustering measure captures the degree of trading overlap among any two investors in that stock, based on a comparison with the expected crowding in a null model where trades are maximally random while still respecting the empirical heterogeneity of both stocks and investors. We investigate the effect of crowded trades on stock price stability and present evidence that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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