Purpose: Historically, firms sought to maximize their value and reduce their costs, respectively, through an ideal capital structure. With the focus on firm-specific determinants, scholars have rarely considered macroeconomic factors in relation to corporate capital structure. The current work expanded the extant literature on macroeconomic influences over investment decision-making by filling existing knowledge gaps through an examination of the determinants influencing Indian manufacturing firms’ selection of capital structure.

Methodology: Empirical data were collected from 549 Indian manufacturing companies between 2010–2011 and 2020–2021. In particular, the Generalized Method of Moments (GMM) and machine learning models (LS Boosted Trees and Random Forest) were applied to examine the macroeconomic variables–capital structure association. GMM was used to capture endogeneity and temporal dynamics in the data, while the machine learning models revealed complex nonlinear relationships.

Findings: The GDP growth rate, interest rate, stock market conditions, and leverage were found to be significantly correlated. As previous studies have primarily focused on firm-level indicators, they have paid limited attention to macro-level variables that may influence organizational leverage decisions. Practical

Implications: The findings of this study will inform finance managers and policymakers about the impact of macroeconomic indicators, enabling them to better leverage key financial strategies in developing countries such as India.

Originality: This empirical work substantially broadens academic discourse by studying macroeconomic variables that affect capital structure in India. As a result, machine learning is viewed as valuable to highlight alternative perspectives on capital structure.

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