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Entropy
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Entropy-Based Applications in Economics, Finance and Management, 3rd Edition
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Entropy-Based Applications in Economics, Finance and Management, 3rd Edition

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1720

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,

Following the success of the first and second volumes of this Special Issues, with this third 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 systems and to investigate the information content of probability distribution. Entropy is a general measure, and therefore many definitions and applications have been proposed in the literature.

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

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

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

To view the 1st and 2nd volumes of this Special Issue, please visit the following links:

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

https://www.mdpi.com/journal/entropy/special_issues/9776C98NUE

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
  • fuzzy entropy
  • maximum entropy
  • copula entropy
  • structural entropy
  • transfer entropy
  • permutation entropy
  • slope entropy
  • approximate entropy
  • sample entropy

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Published Papers (2 papers)

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Research

11 pages, 2017 KiB  
Article
Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis
by Daniel Papla and Rafał Siedlecki
Entropy 2024, 26(12), 1079; https://doi.org/10.3390/e26121079 - 11 Dec 2024
Viewed by 456
Abstract
In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007–2023 (2008–2009, U.S. [...] Read more.
In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007–2023 (2008–2009, U.S. financial sector crises; 2020–2021, Pandemic period; and the 2022–2023 period of Russia’s attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance and Management, 3rd Edition)
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21 pages, 2958 KiB  
Article
Research on Credit Default Prediction Model Based on TabNet-Stacking
by Shijie Wang and Xueyong Zhang
Entropy 2024, 26(10), 861; https://doi.org/10.3390/e26100861 - 13 Oct 2024
Viewed by 916
Abstract
With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. First, use the PyTorch deep learning framework to construct an [...] Read more.
With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. First, use the PyTorch deep learning framework to construct an improved TabNet structure. The multi-population genetic algorithm is used to optimize the Attention Transformer automatic feature selection module. The particle swarm algorithm is used to optimize the hyperparameter selection and achieve automatic parameter search. Finally, Stacking ensemble learning is used, and the improved TabNet is used to extract features. XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Category Boosting), KNN (K-NearestNeighbor), and SVM (Support Vector Machine) are selected as the first-layer base learners, and XGBoost is used as the second-layer meta-learner. The experimental results show that compared with original models, the credit default prediction model proposed in this manuscript outperforms the comparison models in terms of accuracy, precision, recall, F1 score, and AUC (Area Under the Curve) of credit default prediction results. Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance and Management, 3rd Edition)
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