The Classic (S,s) Model and its Applications in Corporate Finance Research

The classic (S,s) model, originally developed for inventory management, has gained traction in macroeconomic and monetary policy research but remains underutilized in corporate finance. This paper addresses this gap by exploring how the (S,s) model can be applied to corporate financial decisions, particularly in emerging markets with significant financing frictions. This study adopts a conceptual and theoretical approach, synthesizing existing literature and empirical findings to demonstrate the applicability of the (S,s) model in corporate finance. The paper draws on foundational works by Arrow et al. (1951), Caplin and Leahy (2010), and recent empirical studies such as Paseda and Ayadi (2023). It examines how firms adjust capital structure, dividend policy, investment, and cash management using threshold-based decision rules. The model's relevance is illustrated through case studies and empirical validations across different financial contexts, including dynamic panel regressions, contingent-claim frameworks, and cash flow sensitivity analyses. The study finds that the (S,s) model effectively explains infrequent and threshold-based adjustments in corporate financial policies. Firms tend to adjust their leverage, dividend payouts, and investment levels only when deviations from target thresholds become significant, due to adjustment costs such as transaction fees and market reactions. Empirical evidence supports the model's predictions across various domains: capital structure adjustments are pro-cyclical at the lower threshold and counter-cyclical at the upper threshold; dividend policies reflect cash reserve thresholds; and investment decisions align with capital stock boundaries. The model also aligns with the Miller-Orr cash management framework. These findings underscore the model's robustness in capturing real-world corporate behavior, especially in environments with imperfect capital markets. The (S,s) model offers a flexible and empirically grounded framework for corporate financial policy, particularly in emerging markets where traditional models like Modigliani-Miller may fall short. Policymakers and financial managers can use this model to design adaptive financial strategies that account for adjustment costs and economic cycles. It also provides a foundation for integrating behavioral and dynamic elements into corporate finance, with potential applications in AI-driven financial decision-making and regulatory policy design.