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Entropy-Based Applications in Economics, Finance, and Management II

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

Deadline for manuscript submissions: closed (19 April 2024) | Viewed by 5834

Special Issue Editor


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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,

Following the success of the first Special Issue, with this second volume, we aim to provide 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 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 approaches in economics and finance;
  • Entropy-based applications in market microstructure research;
  • Shannon theory in fuzzy multiple criteria decision-making methods (FMCDMs) 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.

To view the first volume of this Special Issue, please see:

https://www.mdpi.com/journal/entropy/special_issues/Finance_Management

Prof. Dr. 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 submissions that pass pre-check are 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 2600 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 (4 papers)

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Research

11 pages, 771 KiB  
Article
Why Does Cross-Sectional Analyst Coverage Incorporate Market-Wide Information?
by Yunfei Hou and Changsheng Hu
Entropy 2024, 26(4), 285; https://doi.org/10.3390/e26040285 - 26 Mar 2024
Viewed by 614
Abstract
This paper shows that the empirical distribution of cross-sectional analyst coverage in China’s stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the [...] Read more.
This paper shows that the empirical distribution of cross-sectional analyst coverage in China’s stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management II)
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18 pages, 3942 KiB  
Article
Vulnerability Analysis Method Based on Network and Copula Entropy
by Mengyuan Chen, Jilan Liu, Ning Zhang and Yichao Zheng
Entropy 2024, 26(3), 192; https://doi.org/10.3390/e26030192 - 23 Feb 2024
Cited by 2 | Viewed by 695
Abstract
With the deepening of the diversification and openness of financial systems, financial vulnerability, as an endogenous attribute of financial systems, becomes an important measurement of financial security. Based on a network analysis, we introduce a network curvature indicator improved by Copula entropy as [...] Read more.
With the deepening of the diversification and openness of financial systems, financial vulnerability, as an endogenous attribute of financial systems, becomes an important measurement of financial security. Based on a network analysis, we introduce a network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with the previous network curvature analysis method, the CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management II)
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16 pages, 3735 KiB  
Article
The Effect of Exit Time and Entropy on Asset Performance Evaluation
by Mohammad Ghasemi Doudkanlou, Prokash Chandro and Shokoofeh Banihashemi
Entropy 2023, 25(9), 1252; https://doi.org/10.3390/e25091252 - 23 Aug 2023
Viewed by 2595
Abstract
The objective of this study is to evaluate assets’ performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This [...] Read more.
The objective of this study is to evaluate assets’ performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This portfolio effectively balances the potential risks and returns, guiding investors to make decisions that are in line with their financial goals. To assess the performance, we used data envelopment analysis (DEA), whereby we utilized the risk measure as an input and the mean return as an output. The stop point probability–CVaR (SPP-CVaR) was the risk measurement used when considering the exit time. We calculated the SPP-CVaR by converting the risk-neutral density to the real-world density, calibrating the parameters, running simulations for price paths, setting the stop-profit points, determining the exit times, and calculating the SPP-CVaR for each stop-profit point. To account for negative data and to incorporate the exit time, we have proposed a model that integrates the mean return and SPP-CVaR, utilizing DEA. The resulting inefficiency scores of this model were compared with those of the mean-CVaR model, which calculates the risk across the entire time horizon and does not take the exit time and Shannon entropy into account. To accomplish this, an analysis was conducted on a portfolio that included a variety of stocks, cryptocurrencies, commodities, and precious metals. The empirical application demonstrated the enhancement of asset selection for both short-term and long-term investments through the combined use of Shannon entropy and the exit time. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management II)
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23 pages, 3223 KiB  
Article
Symbolic Encoding Methods with Entropy-Based Applications to Financial Time Series Analyses
by Joanna Olbryś and Natalia Komar
Entropy 2023, 25(7), 1009; https://doi.org/10.3390/e25071009 - 30 Jun 2023
Cited by 1 | Viewed by 884
Abstract
Symbolic encoding of information is the foundation of Shannon’s mathematical theory of communication. The concept of the informational efficiency of capital markets is closely related to the issue of information processing by equity market participants. Therefore, the aim of this comprehensive research is [...] Read more.
Symbolic encoding of information is the foundation of Shannon’s mathematical theory of communication. The concept of the informational efficiency of capital markets is closely related to the issue of information processing by equity market participants. Therefore, the aim of this comprehensive research is to examine and compare a battery of methods based on symbolic coding with thresholds and the modified Shannon entropy in the context of stock market efficiency. As these methods are especially useful in assessing the market efficiency in terms of sequential regularity in financial time series during extreme events, two turbulent periods are analyzed: (1) the COVID-19 pandemic outbreak and (2) the period of war in Ukraine. Selected European equity markets are investigated. The findings of empirical experiments document that the encoding method with two 5% and 95% quantile thresholds seems to be the most effective and precise procedure in recognizing the dynamic patterns in time series of stock market indices. Moreover, the Shannon entropy results obtained with the use of this symbolic encoding method are homogenous for all investigated markets and unambiguously confirm that the market informational efficiency measured by the entropy of index returns decreases during extreme event periods. Therefore, we can recommend the use of this STSA method for financial time series analyses. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management II)
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