Deep learning-based multi-factor models have been employed to predict cross-sectional stock returns by incorporating numerous stock-level characteristics and capturing their nonlinear relationships. However, the inherent complexity of deep learning models often makes them difficult to interpret, posing challenges in practical applications where explainability is critical. To address these challenges, we focus on Neural Additive Models (NAM) – a recently proposed deep learning model designed with high interpretability – and investigate its applicability to cross-sectional stock return prediction. Although NAM’s characteristic subnetwork architecture is useful to obtain highly interpretable outputs analogous to factor returns and exposures in linear factor models, we argue that naive training procedure may lead to unstable predictions. To overcome this problem, we propose a modified NAM architecture that incorporates a novel regularization term, resulting in a framework well-suited for cross-sectional stock return prediction. Through numerical simulations, we demonstrate that the proposed method improves interpretability without sacrificing predictive accuracy compared to the conventional NAM.