Marcos López de Prado

In this enlightening discussion, Marcos López de Prado, a professor and expert in machine learning for finance, joins Vincent Zoonekynd, a leader in quantitative research. They unveil the pitfalls of factor models, including the perils of p-hacking and collider bias. Listeners discover how causal graphs can enhance factor attribution and improve model design. The duo emphasizes that factor investing should focus on causality rather than mere statistical correlations, urging researchers to clarify their assumptions in model reporting.