Methodological analysis demonstrates superior model selection in high-dimensional asset pricing, indicating that nonlinear interactions with firm size refine return predictions.
This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations confirm that S-LASSO outperforms existing alternatives across a variety of scenarios. Empirically, we apply S-LASSO to a characteristics-based asset pricing model, exploring the linear and nonlinear effects of asset-specific characteristics on cross-sectional returns, as well as their interactions with firm size.
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Dong et al. (2026) studied this question.
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