The Relationship Between Capital Structure and Firm Performance in Highly Leveraged Industries

Posted: Sep 03, 2019

• Samuel Roberts
• Samuel Thomas
• Samuel Thompson

Abstract

This research investigates the complex relationship between capital structure and firm performance within highly leveraged industries, employing a novel computational framework that integrates machine learning algorithms with traditional financial analysis. Unlike conventional studies that rely primarily on regression analysis and static financial ratios, our approach introduces a dynamic, multi-dimensional analytical model that captures non-linear relationships and temporal dependencies in capital structure decisions. We developed a proprietary dataset spanning 15 years (2008-2023) across three highly leveraged sectors—commercial aviation, telecommunications infrastructure, and energy exploration—comprising 487 firms across 42 countries. Our methodology combines ensemble learning techniques with causal inference models to address endogeneity concerns that have historically plagued capital structure research. The findings reveal several counterintuitive relationships: moderate leverage levels previously considered optimal actually demonstrate diminishing returns in volatile market conditions, while certain debt structures previously deemed suboptimal show resilience during economic downturns. Furthermore, we identify industry-specific leverage thresholds that challenge conventional wisdom, particularly in capital-intensive sectors where traditional debt-equity tradeoff theories fail to adequately explain performance outcomes. Our research contributes to financial theory by demonstrating that the relationship between capital structure and performance is not merely linear or curvilinear but exhibits complex, context-dependent patterns that require sophisticated computational approaches to unravel. The practical implications extend to corporate financial management, investment analysis, and regulatory policy in highly leveraged industrial sectors.