Posted: Jun 10, 2023
The financing of economic development and infrastructure projects represents one of the most critical challenges facing both developed and emerging economies in the 21st century. Traditional approaches to infrastructure financing have relied heavily on government budgets, multilateral development banks, and commercial banking systems, yet these mechanisms often prove insufficient to meet the enormous capital requirements of modern infrastructure development. Corporate bond markets have emerged as a potentially transformative financing mechanism, yet their specific role in funding economic development projects remains inadequately understood through conventional economic analysis frameworks. This research addresses this gap by introducing a novel computational methodology that leverages machine learning and network analysis techniques to provide unprecedented insights into how corporate bond markets function as conduits for infrastructure financing. Our research is motivated by the observation that existing literature predominantly examines bond markets through macroeconomic lenses or institutional perspectives, failing to capture the complex, multi-dimensional relationships between bond issuance characteristics, infrastructure project types, and economic development outcomes. The computational approach developed in this paper represents a significant departure from traditional economic methodology, enabling the identification of patterns and relationships that conventional statistical methods might overlook. By treating the bond market as a complex adaptive system rather than a simple financial intermediary, we can model the dynamic interactions between various stakeholders, regulatory environments,
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