Projects

Nonlinear machine learning (ML) models are increasingly used to predict the cross-section of stock returns, yet the economic justification for their nonlinear gains remains underexplored. Building on an equilibrium-based framework, this study shows that nonlinear MLs are most valuable when stock payoffs deviate from elliptical distributions (e.

Since the global financial crisis, bank complexity has faced increasing scrutiny for its impact on financial stability, yet it remains difficult to measure. We introduce a novel explainable AI method to quantify complexity and find that it exhibits a pro-cyclical pattern, rising before crises and declining during periods of distress.