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Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods,...
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Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 8460

Special Issue Editors

Dr. Alessandro Mazzoccoli

E-Mail Website
Guest Editor
Department of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, Italy
Interests: risk analysis; wavelet analysis; risk management; time-series predictability; cyber insurance; investment in cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Mattera

E-Mail Website
Guest Editor
Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Italy
Interests: time series analysis; spatial statistics; clustering; forecasting; financial econometrics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. J.E. Trinidad-Segovia

E-Mail Website
Guest Editor
Department of Economics and Business, University of Almería, 04120 Almería, Spain
Interests: long memory; portfolio theory; fractal dimension; financial markets; econophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

In the field of applied mathematics, the utilization of mathematical models holds significant promise in unraveling the intricacies of economic phenomena. This introductory Special Issue will take readers on a journey through the nexus of mathematics and economics, where mathematical frameworks serve as potent tools for understanding, analyzing, and forecasting various facets of economic activity. By leveraging mathematical methodologies, researchers and practitioners alike will endeavor to glean actionable insights, inform decision-making processes, and contribute to the advancement of economic theory and practice. Keywords: mathematical models; mathematical models applied to economics and finance; time-series analysis; forecasting

The Special Issue is focused on (but not limited to) these fields:

Economic Forecasting Models: Exploring mathematical methods for predicting economic indicators. Financial Data Analysis: Delving into mathematical techniques for analyzing financial markets, including asset-pricing models, portfolio optimization, risk management, etc. Computational Econometrics: Examining the use of computational methods for economic data analysis, including machine learning algorithms, big data analysis, etc. Economic Modeling: Discussing mathematical models for complex economic phenomena such as business cycles, consumer behavior, investment decisions, etc. Practical Applications: Exploring case studies and real-world applications of the mathematical analysis of economic data in sectors such as finance, business economics, economic policy, etc.

Dr. Alessandro Mazzoccoli
Dr. Raffaele Mattera
Prof. Dr. J.E. Trinidad-Segovia
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 models
  • applied mathematics
  • mathematical models for economics
  • mathematical models for finance
  • data analysis
  • forecasting
  • differential equations
  • stochastic differential equations
  • dynamical systems

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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

26 pages, 2702 KB  
Article
DWARFB: A Dynamic Weight-Adjusted Random Forest Boost for Predicting Financial Distress in Chinese Listed Companies
by Guodong Hou, Dong Ling Tong, Soung Yue Liew and Peng Yin Choo
Mathematics 2026, 14(6), 955; https://doi.org/10.3390/math14060955 - 11 Mar 2026
Viewed by 394
Abstract
Two key challenges in financial distress prediction are pronounced class imbalance between majority and minority classes and the persistent misclassification of hard-to-learn samples. To tackle these issues, this study proposes an ensemble framework called Dynamic Weight-Adjusted Random Forest Boost (DWARFB). The proposed method [...] Read more.
Two key challenges in financial distress prediction are pronounced class imbalance between majority and minority classes and the persistent misclassification of hard-to-learn samples. To tackle these issues, this study proposes an ensemble framework called Dynamic Weight-Adjusted Random Forest Boost (DWARFB). The proposed method incorporates a dynamic sample-weighting mechanism that leverages cumulative misclassification information, adaptive minority–majority class ratios to address class imbalance issue, and a real-time performance-driven strategy to integrate models’ prediction results. The effectiveness of DWARFB is evaluated using a financial dataset from the China Stock Market & Accounting Research (CSMAR) database. Comparative experiments against eight benchmark Random Forest (RF) approaches show that DWARFB delivers superior balanced performance, and a stable precision–recall trade-off, which effectively reduces both false negatives and false positives in the prediction. Moreover, a loss-based feature contribution metric provides economically meaningful insights into the key financial determinants of distress, enhancing model interpretability. Overall, DWARFB demonstrates strong reliability and adaptability and offers a practical solution for early financial distress warning in imbalanced and dynamic financial environments. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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24 pages, 717 KB  
Article
Changing Wage Effects of Educational Mismatch in China: Evidence from Threshold IV–Selection Models
by Lulu Jiang, Woraphon Yamaka and Paravee Maneejuk
Mathematics 2026, 14(5), 921; https://doi.org/10.3390/math14050921 - 9 Mar 2026
Viewed by 458
Abstract
This study examines the wage effects of educational mismatch in China by jointly addressing sample selection, endogeneity, and nonlinear career-stage heterogeneity within a unified econometric framework. Although educational mismatch has been widely studied, existing evidence largely relies on linear models that overlook experience-dependent [...] Read more.
This study examines the wage effects of educational mismatch in China by jointly addressing sample selection, endogeneity, and nonlinear career-stage heterogeneity within a unified econometric framework. Although educational mismatch has been widely studied, existing evidence largely relies on linear models that overlook experience-dependent wage dynamics and potential selection and endogeneity biases. Using data from the 2020 wave of the China Family Panel Studies (CFPS), this study extends the Duncan–Hoffman model by integrating a sample-selection-corrected threshold regression estimated via instrumental variables. This approach allows the identification of experience thresholds at which the wage effects of overeducation and undereducation differ across regimes. The results reveal pronounced nonlinearities in mismatch-related wage differentials. Overeducation is associated with wage penalties at early career stages, but these penalties weaken and, in some cases, disappear once workers surpass the estimated experience threshold. In contrast, undereducation yields modest wage premiums early in the career but becomes increasingly penalized at higher experience levels. Substantial gender heterogeneity is also observed: male workers are better able to use accumulated experience to offset educational shortfalls, whereas female workers face more persistent penalties, particularly at later career stages. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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29 pages, 1140 KB  
Article
Nonlinear and Spatial Effects of Housing Prices on Urban–Rural Income Inequality: Evidence from Dynamic Spatial Threshold Models in Mainland China
by Mingyang Li, Woraphon Yamaka and Paravee Maneejuk
Mathematics 2025, 13(24), 3960; https://doi.org/10.3390/math13243960 - 12 Dec 2025
Cited by 1 | Viewed by 863
Abstract
This study investigates how housing prices influence urban–rural income inequality (URG) in mainland China by explicitly incorporating spatial interdependence and nonlinear adjustment mechanisms, features often neglected in previous research. Using a balanced panel of 31 provinces from 2005 to 2023, we develop a [...] Read more.
This study investigates how housing prices influence urban–rural income inequality (URG) in mainland China by explicitly incorporating spatial interdependence and nonlinear adjustment mechanisms, features often neglected in previous research. Using a balanced panel of 31 provinces from 2005 to 2023, we develop a dynamic spatial panel threshold model that jointly accounts for temporal persistence, spatial spillovers, and regime-dependent estimation. This framework enables a full decomposition of housing price effects into direct, indirect (spillover), and total impacts across distinct market regimes. The results reveal three major insights. First, URG in mainland China displays strong temporal persistence, suggesting that income disparities evolve gradually over time. Second, rising housing prices significantly widen the urban–rural income gap, both within provinces and through interprovincial transmission, underscoring the amplifying role of spatial spillovers. Third, threshold estimation identifies a critical housing price level of ln(HP) = 8.4843 (approximately 4838.21 RMB/m2), revealing that the inequality-enhancing effect of housing prices is stronger in low-price regions but diminishes as markets mature and affordability constraints intensify. These findings provide new empirical evidence that the housing market functions as a nonlinear and asymmetric driver of regional inequality in mainland China, with implications for housing policy and inclusive growth. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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31 pages, 2197 KB  
Article
A Case Study of a Transportation Company Modeled as a Scheduling Problem
by Cristina Tobar-Fernández, Ana Dolores López-Sánchez and Jesús Sánchez-Oro
Mathematics 2025, 13(21), 3547; https://doi.org/10.3390/math13213547 - 5 Nov 2025
Viewed by 1566
Abstract
This case study tackles a real-world problem of a transportation company that is modeled as a scheduling optimization problem. The main goal of the considered problem is to schedule the maximum number of jobs that must be performed by vehicles over a specific [...] Read more.
This case study tackles a real-world problem of a transportation company that is modeled as a scheduling optimization problem. The main goal of the considered problem is to schedule the maximum number of jobs that must be performed by vehicles over a specific planning horizon in order to minimize the total operational costs. Here, each customer request corresponds to a job composed of multiple operations, such as loading, unloading, and mandatory jobs, each associated with a specific location and time window. Once a job is allocated to a vehicle, all its operations must be executed by that same vehicle within their designated time constraints. Due to the imposed limitations, not every job can feasibly be scheduled. To address this challenge, two distinct methodologies are proposed. The first, a Holistic approach, solves the entire problem formulation using a black-box optimizer, serving as a comprehensive benchmark. The second, a Divide-and-Conquer approach, combines a heuristic greedy algorithm with a binary linear programming, decomposing the problem into sequential subproblems. Both approaches are implemented using the solver Hexaly. A comparative analysis is conducted under different scenarios and problem settings to highlight the advantages and drawbacks of each approach. The results show that the Divide-and-Conquer approach significantly improves computational efficiency, reducing time by up to 99% and vehicle usage by around 15–20% compared to the Holistic method. On the other hand, the Holistic method better ensures that mandatory jobs are completed, although at the cost of more resources. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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12 pages, 1541 KB  
Article
On the Autocorrelation and Stationarity of Multi-Scale Returns
by Carlos Manuel Rodríguez-Martínez, Héctor Francisco Coronel-Brizio, Horacio Tapia-McClung, Manuel Enríque Rodríguez-Achach and Alejandro Raúl Hernández-Montoya
Mathematics 2025, 13(17), 2877; https://doi.org/10.3390/math13172877 - 5 Sep 2025
Cited by 1 | Viewed by 984
Abstract
In this article, we conduct a statistical analysis of the autocorrelation functions (ACF) of multi-scale logarithmic returns computed over maximal monotonic uninterrupted trends (runs) in financial indices’ daily data. We analyze the Dow Jones Industrial Average (DJIA) and the Mexican IPC (Índice de [...] Read more.
In this article, we conduct a statistical analysis of the autocorrelation functions (ACF) of multi-scale logarithmic returns computed over maximal monotonic uninterrupted trends (runs) in financial indices’ daily data. We analyze the Dow Jones Industrial Average (DJIA) and the Mexican IPC (Índice de Precios y Cotizaciones) over a period from 30 October 1978 to 19 May 2025. We examine how deterministic alternation of signs shapes the ACF of multi-scale returns, and we evaluate covariance stationarity via formal tests (e.g., Augmented Dickey–Fuller and Phillips–Perron). We conclude that, despite the persistent long-memory oscillations in the ACF, multi-scale return series pass the stationarity tests, an outcome with interesting implications for econometric modeling of financial time series. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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19 pages, 1414 KB  
Article
Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty
by Lyubov Doroshenko, Loretta Mastroeni and Alessandro Mazzoccoli
Mathematics 2025, 13(8), 1346; https://doi.org/10.3390/math13081346 - 20 Apr 2025
Cited by 8 | Viewed by 2914
Abstract
The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate [...] Read more.
The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate the relationship between these indices and commodity prices across multiple time scales. The wavelet approach captures complex, time-varying dependencies, offering a more nuanced understanding of how uncertainty indices influence commodity price fluctuations. By integrating this analysis with predictability measures, we assess how uncertainty indices enhance forecasting accuracy. We further incorporate deep learning models capable of capturing sequential patterns in financial time series into our analysis to better evaluate their predictive potential. Our findings highlight the varying impact of financial and economic uncertainty on the predictability of commodity prices, showing that while some indices offer valuable forecasting information, others display strong correlations without significant predictive power. These results underscore the need for tailored predictive models, as different commodities react differently to the same financial conditions. By combining wavelet-based measures with machine learning techniques, this study presents a comprehensive framework for evaluating the role of uncertainty in commodity markets. The insights gained can support investors, policymakers, and market analysts in making more informed decisions. Full article
(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)
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