Asset Pricing: A Comparative Analysis of Fama-French Five-Factor with Human Capital-Based Six-Factor Model

[Purpose] Decisions on investments in financial securities rely on robust asset pricing models. Investors continually seek more effective ways to make their portfolios more efficient by using pricing models that are more accurate and obtain better yields. This study compares the performance of the Fama-French five-factor model with a new six-factor model that incorporates the human capital (HC) factor. [Methodology] The study employs the Fama and French time series regression and the Fama-MacBeth two-pass time series regression approach to analyze data of 170 firms (non-financial) listed on the Pakistan Stock Exchange (PSX) over the period between July 2010 and June 2020. [Findings] Findings indicate that HC is a significant factor that enhances the efficiency of asset pricing models. The six-factor model outperforms the Fama-French five-factor model, suggesting that the inclusion of HC yields a more effective asset pricing model. [Implications] The findings have important implications for investors, policymakers, and academics, indicating that the six-factor model is more effective in estimating expected returns and pricing financial securities. Hence, the findings of this study are strongly related to the practice of decision sciences, thereby providing insights to assess how the factors in factor models affect the variation in risk-adjusted returns, and it is useful for portfolio managers to make optimal asset-management decisions. [Originality/Value] This research contributes to the development of asset pricing models by introducing and validating the HC factor, offering a novel approach to improving the efficiency of valuation models used in financial markets.