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Mathematics
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Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition
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Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5239

Special Issue Editors

Prof. Dr. Oana-Ramona Lobonț

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Guest Editor
Finance, Business Information Systems and Modelling Department, Faculty of Economics and Business Administration, West University of Timisoara, 300223 Timișoara, Romania
Interests: public sector governance; environmental and energy economics; empirical finance; sustainable public policy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chi-Wei Su

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Guest Editor
School of Economics, Qingdao University, Qingdao 266071, China
Interests: energy economics; international finance; applied econometrics; mathematics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Noja Grațiela Georgiana

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Guest Editor
Faculty of Economics and Business Administration, West University of Timisoara, 300223 Timisoara, Romania
Interests: international economics; econometric modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Weike Zhang

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Guest Editor
School of Public Administration, Sichuan University, Chengdu 610064, China
Interests: digital divide; digital technology; artificial intelligence; mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the call for papers for the Special Issue on “Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition”. Building on the success of the first edition, this Special Issue continues to focus on the intersection of mathematics, economics, and finance, emphasizing the importance of mathematical tools and techniques in advancing our understanding of economic systems and financial markets.

Mathematical economics and financial modelling are rapidly evolving interdisciplinary fields that leverage advanced mathematical methods to analyze and solve complex problems in finance and economics. This Special Issue aims to expand the body of knowledge on these topics and provide practical insights for both academics and industry professionals.

With recent global challenges such as pandemics, shifting monetary policies, and geopolitical conflicts, the financial landscape has seen significant volatility. In this context, mathematical economics plays a crucial role in the following:

  • Modeling the volatility of asset prices and forecasting their future trends;
  • Developing pricing models and optimal asset allocation strategies to manage financial risks effectively;
  • Using advanced econometric and statistical tools to understand economic phenomena and predict market behaviour.

We encourage contributions that explore the applications of mathematical models in various economic and financial contexts, with an emphasis on both theoretical advancements and practical applications.

We welcome papers on a broad range of topics related to mathematical economics and financial modelling, including, but not limited to, the following:

  • Financial engineering challenges and solutions;
  • Statistical and computational methods in finance and economics;
  • Pricing theory and the application of securities, derivatives, and portfolios;
  • Machine learning and AI techniques for asset price prediction;
  • Stochastic optimization and dynamic control in financial models;
  • Mathematical exploration of economic behaviour and phenomena;
  • Advanced econometric models for macro and microeconomics;
  • Other innovative topics in mathematical economics and financial modelling.

We invite researchers, practitioners, and innovators to submit their cutting-edge research for this Special Issue, as we continue to explore new mathematical approaches for understanding and solving the complex problems of modern economics and finance.

Prof. Dr. Oana-Ramona Lobonț
Prof. Dr. Chi-Wei Su
Prof. Dr. Noja Grațiela Georgiana
Dr. Weike Zhang
Guest Editors

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

  • mathematical economics
  • financial modelling
  • financial engineering
  • structural equation modelling
  • spatial statistics and econometrics
  • stochastic optimization
  • measurement, network analysis and sampling techniques

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

Published Papers (6 papers)

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Research

23 pages, 378 KB  
Article
An Innovation of the Zero-Inflated Binary Classification in Credit Scoring Using Two-Stage Algorithms
by Chenlu Zheng, Yuhlong Lio and Tzong-Ru Tsai
Mathematics 2026, 14(5), 800; https://doi.org/10.3390/math14050800 - 27 Feb 2026
Viewed by 424
Abstract
Zero-inflated and class-imbalanced data present significant challenges in credit scoring. Zero-Inflated Bernoulli Distribution (ZIBD) models help handle excess zeros. However, the S-shaped function and the neglect of misclassification costs may degrade the ZIBD model’s classification performance. To address these challenges, this paper proposes [...] Read more.
Zero-inflated and class-imbalanced data present significant challenges in credit scoring. Zero-Inflated Bernoulli Distribution (ZIBD) models help handle excess zeros. However, the S-shaped function and the neglect of misclassification costs may degrade the ZIBD model’s classification performance. To address these challenges, this paper proposes a novel two-stage algorithm that integrates an optimized ZIBD model with Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), respectively. Specifically, we develop a new loss function that incorporates cross-entropy and example-dependent cost-sensitive to optimize the ZIBD model, thereby minimizing cost risks. Subsequently, we suggest integrating baseline models to compensate for the ZIBD model’s classification deficiencies. This hybrid approach effectively mitigates the impact of structural zeros in imbalanced data while enhancing model robustness. The performance of the proposed method is validated using two real-world banking datasets. Experimental results demonstrate that the proposed two-stage algorithm significantly outperforms its competitors across both machine-learning metrics and savings. Hence, the proposed novel two-stage algorithm offers a more effective solution for zero-inflated banking data. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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15 pages, 510 KB  
Article
Experience Goods and Delayed Price Discovery: Evidence from Information Frictions in Game Releases
by Sujin Pyo and Minsu Cho
Mathematics 2026, 14(5), 755; https://doi.org/10.3390/math14050755 - 24 Feb 2026
Viewed by 463
Abstract
This study investigates whether financial markets efficiently incorporate information related to new product releases in industries where fundamental signals become available only after consumer engagement. Analyzing 49 commercial game launches by 13 publicly listed publishers in South Korea from 2001 to 2024, the [...] Read more.
This study investigates whether financial markets efficiently incorporate information related to new product releases in industries where fundamental signals become available only after consumer engagement. Analyzing 49 commercial game launches by 13 publicly listed publishers in South Korea from 2001 to 2024, the research examines short-term return and volatility patterns around the official release date. In contrast to the pre-announcement drift in macroeconomic contexts, there is no evidence of abnormal price or volatility movements prior to launch, which is consistent with the limited informativeness of pre-release marketing for experience goods. Instead, stock prices display a significant negative return and a marked increase in volatility on the day following the launch, rather than on the launch day itself. This pattern corresponds to the delayed emergence of verifiable performance indicators, such as app store revenue rankings and early user-generated content, which typically appear only after consumer interaction with the product. These results indicate that price discovery for digital experience goods is influenced by industry-specific information frictions, which leads to delayed and discontinuous market adjustments. The study contributes to the literature by showing that ex-ante price discovery does not generalize across industries and by emphasizing the critical role of post-release signal timing in shaping event-driven asset price dynamics. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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18 pages, 373 KB  
Article
The Robust LM Test for Spatial Effects in a Common-Factor Scenario: A Review and Monte Carlo Results
by Giovanni Millo
Mathematics 2026, 14(4), 591; https://doi.org/10.3390/math14040591 - 8 Feb 2026
Viewed by 372
Abstract
I address the empirical properties of the popular robust LM tests of Anselin et al. (1996) for the specification of spatial models when employed in a scenario characterized by unobserved common factors with idiosyncratic loadings. I describe the small-sample behavior by way of [...] Read more.
I address the empirical properties of the popular robust LM tests of Anselin et al. (1996) for the specification of spatial models when employed in a scenario characterized by unobserved common factors with idiosyncratic loadings. I describe the small-sample behavior by way of simulation, without deriving any analytical results. I build upon the analysis in Millo (2025), extending it from homogeneous time effects to common factors with heterogeneous loadings, a very common setting, e.g., in empirical macroeconometrics. As in the former paper, I document severe distortions in the empirical size and power of the spatial tests when omitting the common factors. Then, I evaluate the strategy of controlling for the heterogeneity by augmentation, including simple (TFE) or interactive fixed effects (IFE) in the test specification. Unlike the homogeneous cases, I find that the correction to the test power may come at a non-negligible cost in terms of size distortion: for some combinations of sample sizes, in particular for short panels, IFE-corrected tests can be severely over-rejecting. This is traced back to a well-known incidental parameter problem. TFE-corrected tests can instead suffer from low power. Nevertheless, either form of augmentation is preferable to ignoring time effects when potentially present. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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24 pages, 2977 KB  
Article
Linear Equation Systems Under Uncertainty: Applications to Multiproduct Market Equilibrium
by Vicente Liern, Sandra E. Parada-Rico and Luis A. Conde-Solano
Mathematics 2025, 13(16), 2566; https://doi.org/10.3390/math13162566 - 11 Aug 2025
Cited by 1 | Viewed by 1152
Abstract
Market equilibrium models are essential tools within classical economic theory for analyzing the interaction between supply and demand. However, traditional formulations are often based on deterministic relationships and assume the existence of perfect information, an assumption that diverges from real-world conditions, which are [...] Read more.
Market equilibrium models are essential tools within classical economic theory for analyzing the interaction between supply and demand. However, traditional formulations are often based on deterministic relationships and assume the existence of perfect information, an assumption that diverges from real-world conditions, which are characterized by ambiguity and uncertainty. This article addresses the modeling of multiproduct supply and demand equilibrium under uncertainty, using systems of linear equations with fuzzy coefficients and/or variables. By applying fuzzy set theory, the model incorporates the inherent vagueness of supply and demand functions, enabling a more flexible and realistic representation of market behavior. The proposed methodology involves reformulating the equilibrium conditions through fuzzy arithmetic and examining the existence and nature of fuzzy solutions. The theoretical proposals are illustrated through a simplified real-world case involving a Colombian multinational company, demonstrating their applicability and effectiveness. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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15 pages, 2244 KB  
Article
A Dynamic Analysis of Banks’ Behaviour Towards Corporate Social Responsibility Reporting
by Liliana Donath, Gabriela Mircea, Mihaela Neamțu, Grațiela Georgiana Noja and Nicoleta Sîrghi
Mathematics 2025, 13(16), 2554; https://doi.org/10.3390/math13162554 - 9 Aug 2025
Viewed by 895
Abstract
Corporate Social Responsibility (CSR) actively enhances social, economic, and environmental well-being, increasingly impacting society. It plays a vital role in building a trustworthy and transparent image for the banking system’s relationship with the community. In this context, the paper aims to analyse the [...] Read more.
Corporate Social Responsibility (CSR) actively enhances social, economic, and environmental well-being, increasingly impacting society. It plays a vital role in building a trustworthy and transparent image for the banking system’s relationship with the community. In this context, the paper aims to analyse the effects of delayed adaptation by the banking system to reporting requirements, as well as the reasons that may cause oscillating behaviour on their part. Accordingly, three scenarios are developed to describe the behaviour of banks that experience regular fluctuations in the level of external sustainability reporting requirements, meaning the pressure to comply with these requirements may vary over time. The research method employed involves a dynamic analysis, utilising a mathematical model described by a nonlinear system with time delay. The goal of the research is to identify the equilibrium point of the system and analyse its asymptotic stability. Moreover, the critical time delay is provided, beyond which banks’ responses become oscillatory rather than stable. Numerical simulations illustrate the theoretical findings and reveal a critical delay value under which banks can stabilise their resources to meet sustainability requirements. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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13 pages, 771 KB  
Article
Valuation of Euro-Convertible Bonds in a Markov-Modulated, Cox–Ingersoll–Ross Economy
by Yu-Min Lian, Jun-Home Chen and Szu-Lang Liao
Mathematics 2025, 13(13), 2075; https://doi.org/10.3390/math13132075 - 23 Jun 2025
Cited by 1 | Viewed by 955
Abstract
This study investigates the valuation of Euro-convertible bonds (ECBs) using a novel Markov-modulated cojump-diffusion (MMCJD) model, which effectively captures the dynamics of stochastic volatility and simultaneous jumps (cojumps) in both the underlying stock prices and foreign exchange (FX) rates. Furthermore, we introduce a [...] Read more.
This study investigates the valuation of Euro-convertible bonds (ECBs) using a novel Markov-modulated cojump-diffusion (MMCJD) model, which effectively captures the dynamics of stochastic volatility and simultaneous jumps (cojumps) in both the underlying stock prices and foreign exchange (FX) rates. Furthermore, we introduce a Markov-modulated Cox–Ingersoll–Ross (MMCIR) framework to accurately model domestic and foreign instantaneous interest rates within a regime-switching environment. To manage computational complexity, the least-squares Monte Carlo (LSMC) approach is employed for estimating ECB values. Numerical analyses demonstrate that explicitly incorporating stochastic volatilities and cojumps significantly enhances the realism of ECB pricing, underscoring the novelty and contribution of our integrated modeling approach. Full article
(This article belongs to the Special Issue Advanced Research in Mathematical Economics and Financial Modelling, 2nd Edition)
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