Risk Management

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.

While machine learning (ML) and deep learning (DL) models are increasingly applied to predict both cross-section and time series of stock returns, the underlying sources of the nonlinearities they capture remain largely unexplored.