Human capital in asset pricing: A machine learning perspective on the six-factor model for Pakistan's equity market

The study aims to extend the Fama-French five-factor model by adding human capital as the sixth factor, with a specific focus on Pakistan's frontier market. Additionally, we also test the efficacy of three estimation approaches, OLS, ARIMAX, and LSTM-RNN, by comparing their predictive power. Employing a machine learning approach to assess the predictive power of estimation techniques offers fresh insights into the importance of contextual and market-specific factors. The study provides empirical evidence that the complexity of deep learning models is not always an advantage, especially in ‘underdeveloped’ markets that lack high-frequency market data and large datasets. Additionally, our results also support the inclusion of the sixth factor (human capital) in the asset pricing model. The findings show that firms with high investment in human capital exhibit a positive premium, whereas firms with low investment in human capital exhibit a negative premium, supporting Human Resource Theory in the Pakistani market.