Evaluation of the Capital Asset Pricing Model (CAPM) in assessing the risk and return of a single asset in the era of modern market volatility - Ahmad Sodiq; Diana Ayuning Tyas; Meiriska Putri Fajri (PT Wim Solusi Prima, 2026)
This study aims to evaluate the relevance and accuracy of the Capital Asset Pricing Model (CAPM) in assessing the risk and return of individual assets in the era of modern post-pandemic market volatility. Rising global volatility due to the COVID-19 pandemic, geopolitical conflicts, high inflation, and monetary policy tightening has challenged the CAPM’s fundamental assumptions, which rely on beta stability and market efficiency. This study employs a descriptive approach through a review of recent literature (2021–2026) combined with quantitative analysis to demonstrate the deviation between CAPM-predicted returns and actual returns. The results indicate that the CAPM has significant limitations in explaining the returns of individual assets, particularly in volatile market conditions, as evidenced by a deviation of 6.69% between the expected return (15.09%) and the actual return (8.40%). These inaccuracies are caused by several key factors, namely time-varying beta, the failure of the assumption of a normal distribution of returns, and the influence of behavioral factors such as herding behavior, loss aversion, and market sentiment. Furthermore, the CAPM is deemed incapable of capturing the multidimensional complexity of risk that influences asset prices in modern markets. A comparison with alternative models shows that multifactor models such as the Fama-French model and the Arbitrage Pricing Theory (APT) offer better explanatory power in accounting for variations in asset returns. Nevertheless, the CAPM remains relevant as a basic conceptual framework and benchmark in financial analysis. Therefore, this study concludes that the CAPM is no longer adequate as a standalone model for single-asset valuation in an era of high volatility, necessitating a more comprehensive approach that integrates multifactor models and behavioral factors.