Evaluating the Forecasting Accuracy of Valuation Models in Healthcare: A Comparative Analysis of CAPM and DDM

Abstract

Valuing firms in the healthcare sector presents unique challenges due to market volatility and inconsistent dividend policies. This study evaluates the forecasting accuracy of two widely used valuation models the Capital Asset Pricing Model (CAPM) and the Dividend Discount Model (DDM) within this context. A simulated dataset comprising 100 healthcare firms over the period 2019–2021 is utilized to ensure a controlled environment, minimizing the influence of external real-world variables. Model performance is assessed using Mean Absolute Error (MAE) and Percentage Absolute Error (PAE). The findings reveal that CAPM demonstrates superior predictive accuracy compared to DDM, with a notably lower average PAE (3.47% versus 8.48%). The weaker performance of DDM is largely attributed to the irregular and often unpredictable dividend patterns commonly observed in healthcare firms. This study provides empirical insights into the suitability of valuation models under controlled conditions, offering practical guidance for financial analysts and investors. It also contributes to the broader literature by highlighting the importance of selecting appropriate valuation techniques tailored to sector-specific characteristics.