AI-driven capital structure forecasting for automotive giants: Enhancing stability, liquidity, and solvency - Gyulasaryan, Mikayel Rafayel; Matevosyan, Ashot Varazdat; Grigoryan, Ani Zohrab; Matevosyan, Mane Henrikh (Journal of Asian Scientific Research, 2025)
From 2015 to 2025, leading automotive companies listed on global stock exchanges have operated in a highly capital-intensive environment that requires effective management of financial structures. This study applies Artificial Intelligence (AI)–based forecasting models to analyze the capital structures of major automotive manufacturers, focusing on Toyota as a representative Asian firm and comparing it with Ford and BMW. The empirical results demonstrate that AI models improve the accuracy of debt-to-equity ratio forecasts by 12–15% compared to traditional statistical methods. For Toyota, AI-based forecasts indicate a stable capital structure with liquidity buffers consistently above industry averages, supporting stronger solvency and financial resilience. In contrast, Ford and BMW exhibit higher leverage sensitivity, with solvency ratios projected to decline under rising interest rate scenarios. These results underscore the comparative advantage of Toyota’s financial management practices within the Asian automotive sector while contextualizing its performance in the broader global landscape. The study further highlights the strategic value of AI-based financial forecasting tools, suggesting that their integration can optimize capital structures and support informed decision-making in industries characterized by significant capital requirements. Overall, the findings advocate for the adoption of AI methodologies as a means to strengthen financial planning and enhance corporate sustainability in the automotive sector.