EVALUATING CORPORATE VALUATION FRAMEWORKS IN HIGH-INFLATION MACROECONOMIC ENVIRONMENTS: CONVENTIONAL MULTIPLES VS. ADVANCED PREDICTIVE ANALYTICS

Abstract

In the context of volatile macroeconomic environments, accurate corporate valuation is paramount for successful Mergers and Acquisitions (M&A). High-inflation regimes distort traditional financial metrics, challenging the reliability of classical valuation models. This thesis provides a comparative analysis of conventional corporate valuation frameworks—specifically Discounted Cash Flow (DCF) and standard market multiples—against modern predictive analytics and econometric stochastic modeling. While conventional methods offer simplicity and historical precedence, they are highly vulnerable to inflation-induced discount rate volatility and cash flow misestimations. Modern analytical techniques, incorporating Monte Carlo simulations and real-time data analytics, provide enhanced predictive accuracy and risk-adjusted target valuation. The findings suggest that relying solely on conventional multiples in high-inflation environments poses significant financial risks, and advocates for a hybrid valuation approach utilizing data-driven stress testing to secure long-term M&A success.