The capital asset pricing model (CAPM) after 60 years: Key insights, unresolved issues, and future directions

Abstract

This study offers a comprehensive re-evaluation of the Capital Asset Pricing Model (CAPM) and its multifactor extensions across five major African equity markets—Nigeria, South Africa, Kenya, Egypt, and the BRVM—over the period 2000–2024. Using OLS, Fama–MacBeth, and GMM estimation techniques, we assess the empirical validity and cross-market performance of the CAPM, Fama–French 3-, 5-, and 6-Factor models, Carhart 4-Factor model, Liquidity-Adjusted CAPM, and Consumption CAPM. While the CAPM beta remains statistically significant across all markets, its explanatory power is limited, particularly in less liquid and less integrated markets. Multi-factor models consistently outperform the CAPM, with the Fama–French 5- and 6-Factor models demonstrating superior adjusted R 2 and pricing accuracy. Liquidity and consumption factors yield mixed results, while behavioural and sentiment-augmented models offer marginal improvements. Machine learning approaches deliver the highest predictive accuracy but raise interpretability concerns. The novelty of this study lies in its unified empirical framework, which integrates traditional, behavioural, and machine learning models across a harmonized multi-country dataset. It is the first to systematically test the stability and contextual relevance of global asset pricing models in African markets using a 25-year panel. The findings underscore the partial portability of global models and the need for context-sensitive adaptations. This study contributes to the literature by providing robust cross-market evidence, advancing methodological pluralism, and offering actionable insights for policymakers, investors, and researchers seeking to enhance asset pricing in emerging and frontier markets. • CAPM remains relevant but shows limited explanatory power in African markets. • Multifactor models outperform CAPM across five major African stock exchanges. • Liquidity and consumption factors exhibit mixed pricing evidence across markets. • Machine learning improves predictive accuracy but raises interpretability concerns. • Behavioural factors marginally enhance model fit in emerging market contexts.