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Mathematics
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Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making
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Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1643

Special Issue Editor

Prof. Dr. Kamer-Ainur Aivaz

E-Mail Website
Guest Editor
Department of General Economy, Faculty of Economic Sciences, Ovidius University of Constanta, 900470 Constanta, Romania
Interests: econometrics; sustainability; education; ethical behavior; social sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the essential role of mathematical tools and quantitative methods in understanding and optimizing decision-making processes in key areas of the contemporary economy. We welcome contributions that demonstrate the power of data-driven approaches and mathematical modeling in addressing major economic, financial, and societal challenges.

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

  • Transition to a circular and sustainable economy;
  • The influence of religious and cultural values on economic and managerial decisions;
  • Strategic leadership and financial governance in key economic sectors;
  • Predictive analytics and risk modeling in financial systems;
  • Integration of digital technologies and artificial intelligence in economic analysis and planning;
  • The role of quantitative tools in evaluating economic performance and sustainability.

In a world increasingly shaped by digital transformation, exploring how quantitative analysis can support innovation, efficiency, and resilience in economic systems has become essential. We encourage interdisciplinary research that bridges mathematics, economics, data science, and social sciences to generate practical and impactful insights.

The main objectives of this Special Issue are as follows:

  • Promote interdisciplinary research applying mathematical and quantitative methods in economic and financial contexts;
  • Encourage the development of mathematical models to support strategic decision-making, risk assessment, and performance evaluation;
  • Explore how cultural, ethical, and technological dimensions can be integrated into economic modeling and financial analysis;
  • Foster innovative approaches to data science, big data analytics, and economic forecasting with societal relevance.

We invite submissions of theoretical articles, empirical studies, case analyses, and innovative methodological contributions from researchers working in fields such as those listed below:

  • Applied mathematics;
  • Economics and finance;
  • Data science and artificial intelligence;
  • Sustainability studies;
  • Risk management and financial modeling.

You may choose our Joint Special Issue in Risks.

Prof. Dr. Kamer-Ainur Aivaz
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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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

  • quantitative analysis
  • mathematical modeling
  • decision-making
  • economics
  • finance
  • sustainable economy
  • circular economy
  • risk modeling
  • predictive models
  • data science
  • digital transformation
  • innovation in economics
  • performance evaluation
  • financial governance
  • religious and cultural values
  • big data
  • interdisciplinarity

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 896 KB  
Article
Modelling the Shadow Economy: An Econometric Study of Technology Development and Institutional Quality
by Lavinia Mastac and Anamaria Mișa
Mathematics 2025, 13(24), 3914; https://doi.org/10.3390/math13243914 - 7 Dec 2025
Viewed by 61
Abstract
This econometric analysis models the conditional expectation of Europe’s shadow economy size as a function of technologization and institutional quality indicators, using a balanced panel of 29 countries from 2007 to 2022. Technologization is measured through the four dimensions of Desai’s Technological Achievement [...] Read more.
This econometric analysis models the conditional expectation of Europe’s shadow economy size as a function of technologization and institutional quality indicators, using a balanced panel of 29 countries from 2007 to 2022. Technologization is measured through the four dimensions of Desai’s Technological Achievement Index, and institutional quality via core World Government Indicators variables. Rigorous diagnostics select a random-effects model with year dummies and Driscoll-Kraay standard errors, explaining 61% of the variance in informality. Development of Human Skills delivers the largest individual reduction (−7.2 pp), strongly supporting the core hypothesis. Control of Corruption is also highly significant (−2.8 pp). While New Technology Creation showed a negative association, its statistical significance proved unstable across model specifications. Technology diffusion becomes insignificant when common global time shocks are absorbed, implying that mere adoption has limited independent effect. Furthermore, the impact of human skills intensifies significantly in countries with high New Technology Diffusion (−12,489), revealing a potent synergy between human capital and technological advancement. Policy priorities therefore include anti-corruption enforcement, Research and Development incentives, and sustained investment in skills, especially where technology diffusion is high, to draw hidden activity into the formal sector. Full article
(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)
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33 pages, 1944 KB  
Article
Research on Data Product Operation Strategies Considering Dynamic Data Updates Under Different Power Structures
by Yazhou Liu, Wenxiu Hu, Qinfeng Gao, Zuhui Xia and Yan Shen
Mathematics 2025, 13(23), 3875; https://doi.org/10.3390/math13233875 - 3 Dec 2025
Viewed by 127
Abstract
As data product transactions become increasingly standardized, the operational strategies of data product manufacturers and service providers play a pivotal role in shaping market outcomes. This study develops a game-theoretic framework that incorporates dynamic data updates under alternative power structures to examine the [...] Read more.
As data product transactions become increasingly standardized, the operational strategies of data product manufacturers and service providers play a pivotal role in shaping market outcomes. This study develops a game-theoretic framework that incorporates dynamic data updates under alternative power structures to examine the equilibrium performance of pricing, demand, technological investment, update rates, and promotional effort. The results indicate that optimal prices under Stackelberg leadership exceed those in the Nash game, whereas demand, technological investment, update frequency, and promotion are consistently higher in the Nash setting. The effects of these decisions are moderated by end-user preference heterogeneity: when users exhibit stronger promotion preferences, service-provider leadership generates superior outcomes, while stronger quality preferences favor manufacturer leadership. Demand preferences and cost coefficients significantly influence profitability—enhanced preferences improve the leader’s returns, whereas high technological and promotional costs suppress profits for both parties. Cost savings in dynamic updates and increases in perceived value exert strong positive effects on market competitiveness, while higher update investment and data acquisition costs exert negative effects. Overall, this study deepens the theoretical understanding of how power structures interact with dynamic updating and user preferences, providing analytical insights and decision support for optimizing operational strategies in data product markets. Full article
(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)
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41 pages, 1769 KB  
Article
Introducing AI in Pension Planning: A Comparative Study of Deep Learning and Mamdani Fuzzy Inference Systems for Estimating Replacement Rates
by Pantelis Z. Lappas and Georgios Symeonidis
Mathematics 2025, 13(23), 3737; https://doi.org/10.3390/math13233737 - 21 Nov 2025
Viewed by 428
Abstract
Funded pensions have become a key focus in strategies to ensure supplementary income during retirement. This paper explores two distinct approaches for estimating replacement rates: a deep learning model and a Mamdani Fuzzy Inference System (FIS). Using synthetic datasets for training, the deep [...] Read more.
Funded pensions have become a key focus in strategies to ensure supplementary income during retirement. This paper explores two distinct approaches for estimating replacement rates: a deep learning model and a Mamdani Fuzzy Inference System (FIS). Using synthetic datasets for training, the deep learning model delivered accurate replacement rate predictions when benchmarked against exact solutions. On the other hand, the FIS approach, which leverages expert insights and practical experience, produced encouraging results but revealed opportunities for refining the definitions of intervals and linguistic categories. To bridge the strengths of both approaches, we introduce a conceptual integration using the Analytic Hierarchy Process (AHP), providing a multi-criteria decision-support framework that combines predictive accuracy from neural networks with the interpretability of fuzzy systems. The findings emphasize the potential of artificial intelligence (AI) methods, including neural networks and fuzzy logic, in advancing pension planning. While these techniques remain underutilized in this area, they hold significant promise for developing decision-support systems, particularly in big data contexts. Such systems can offer initial replacement rate estimates, serving as valuable inputs for experts during the decision-making process. Additionally, the paper suggests future research into multi-criteria decision analysis to improve decision-making within multi-pillar pension frameworks. Full article
(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)
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16 pages, 1174 KB  
Article
Valuation of Defaultable Corporate Bonds Under Regime Switching
by Yu-Min Lian and Jun-Home Chen
Mathematics 2025, 13(22), 3628; https://doi.org/10.3390/math13223628 - 12 Nov 2025
Viewed by 572
Abstract
This study investigates the valuation of defaultable corporate bonds using a two-factor model of Markov-modulated stochastic volatility with double exponential jumps (2FMMSVDEJ). This model captures long- and short-term SV and asymmetrical jumps in the underlying asset value. Concurrently, the firm’s debt dynamics are [...] Read more.
This study investigates the valuation of defaultable corporate bonds using a two-factor model of Markov-modulated stochastic volatility with double exponential jumps (2FMMSVDEJ). This model captures long- and short-term SV and asymmetrical jumps in the underlying asset value. Concurrently, the firm’s debt dynamics are governed by a Markov-modulated GBM (MMGBM) model to reflect state transitions. A dynamic measure change technique is employed to determine the pricing kernel, and the resulting credit spreads and default probabilities are analyzed. Full article
(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)
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