Impact of Geopolitical and International Trade Dynamics on Corporate Vulnerability and Insolvency Risk: A Graph-Based Approach

Abstract:

In the context of the globalization process, the interplay between geopolitical dynamics and international trade fluctuations has had significant effects on global economic and business stability. Recent crises, such as the US–China trade war, the invasion of Ukraine, and the COVID-19 pandemic, have highlighted how changes in the structure of international trade can amplify the risks of business failure and reshape global competitiveness. This study aims to analyze in depth the transmission of business failure risk within the global trade network by assessing the sensitivity of industrial sectors in different countries to disruptive/critical/significant events. Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. The mechanisms that relate geopolitical factors, such as trade sanctions and international conflicts, with the oscillations in the global market are analyzed. This study not only contributes to our understanding of how the macroeconomic environment influences business survival, but also provides analytical tools for strategic decision making. By providing an empirical and theoretical framework for early risk identification, it brings a novel perspective to academia and business, facilitating better adaptation to an increasingly volatile and uncertain business environment.

Aplicación:

Este artículo aporta un marco cuantitativo transferible para convertir la futurización en escenarios medibles mediante modelos de red e IA: muestra cómo representar un sistema socioeconómico como un grafo ponderado, evaluar su vulnerabilidad y la propagación de shocks a partir de métricas estructurales (centralidades, intermediación, influencia), identificar comunidades funcionales con algoritmos como Louvain y, además, entrenar modelos predictivos (p. ej., random forest y redes neuronales) usando esas métricas como variables explicativas para estimar riesgo, complementándolo con análisis de agrupamiento espacial (Moran) para distinguir efectos geográficos frente a dependencias de red. En el Prometeo, este enfoque se adapta directamente a la Huerta definiendo redes agroalimentarias (actores, flujos de producto/insumos, logística y dependencias), permitiendo localizar hubs y cuellos de botella, comparar resiliencia entre clústeres y simular impactos de perturbaciones climáticas, regulatorias o de mercado; además, se integra naturalmente con las proyecciones semánticas del proyecto, ya que estas pueden actuar como señales tempranas para parametrizar shocks (cambios en pesos, ruptura de enlaces, degradación de nodos), conectando de manera trazable la evidencia textual con la simulación estructural y la priorización de decisiones.

We would like to acknowledge funding from the Generalitat Valenciana (Spain) through the PROMETEO 2024 CIPROM/2023/32 grant.