Mathematical Models and Data-Driven Algorithms for Solving Economic Problems

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 717

Special Issue Editors


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Department of Economics, Faculty of Operation and Economics of Trasport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: financial management; investment management; financial analysis; corporate finance
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Žilina, 010 26 Žilina, Slovakia
Interests: data analysis; statistical analysis; econometrics; counterfactual evaluations; financial derivatives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematical modelling and sophisticated data-driven algorithms are increasingly essential for the resolution of intricate economic and financial issues. Conventional approaches, rooted in classical econometrics and optimization, are increasingly incorporating methods from applied mathematics, operations research, statistics, and computational intelligence. Stochastic modelling, dynamic optimization, game theory, network analysis, time series forecasting, and machine learning are all effective methods for capturing the interdependence, nonlinearity, and uncertainty that are present in contemporary economies. Data-driven methodologies, such as clustering, classification, anomaly detection, and simulation-based approaches, are simultaneously transforming the analysis, prediction, and interpretation of economic phenomena.

The objective of this Special Issue is to compile exceptional contributions that address economic and financial challenges by introducing and implementing mathematical models and algorithmic approaches. The subjects include policy design, risk management, algorithmic trading, sustainable business models, decision-making under uncertainty, and predictive analytics for markets, among others. Theoretical advancements and empirical applications are both encouraged, with a particular emphasis on those that illustrate the potential of innovative algorithms and rigorous mathematical tools to address real-world economic challenges.

We extend an invitation to researchers from academia and industry to submit their original and unpublished work, thereby promoting an interdisciplinary dialogue at the intersection of data science, economics, and mathematics.

Dr. Roman Blazek
Dr. Lucia Ďuricová
Guest Editors

Manuscript Submission Information

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Keywords

  • algorithmic economics
  • hybrid modelling approaches
  • data-centric modelling
  • explainable AI in economics
  • adaptive market dynamics
  • sustainable decision algorithms
  • prescriptive analytics
  • real-time economic analytics

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Published Papers (1 paper)

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Research

26 pages, 616 KB  
Article
Predictive Modelling of Corporate Financial Performance Under AI Integration: A Data-Driven Analysis of Demographic Variance
by Aneta Cugová, Juraj Cúg and Tibor Salát
Mathematics 2026, 14(6), 943; https://doi.org/10.3390/math14060943 - 11 Mar 2026
Viewed by 418
Abstract
This paper examines how companies in Slovakia and Poland perceive AI tool utilization and report changes in selected performance indicators after AI adoption (annual turnover, BIT, and employee error rates), and whether these assessments differ across firm demographics (country, company size, and length [...] Read more.
This paper examines how companies in Slovakia and Poland perceive AI tool utilization and report changes in selected performance indicators after AI adoption (annual turnover, BIT, and employee error rates), and whether these assessments differ across firm demographics (country, company size, and length of operation). Using a CAWI survey of 865 firms and a contingency-table framework with Pearson’s chi-square tests and Cramer’s V effect sizes, we observe statistically significant—yet predominantly weak—associations between firm demographics and both AI utilization and self-reported performance changes. The findings provide actionable implications for managers and policy-support institutions seeking to accelerate AI adoption and value realization in central Europe, while acknowledging the limitations of cross-sectional self-reported data. Full article
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