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PreprintCC0arXiv

Specification LASSO and a Flexible Characteristics-Based Asset Pricing Model

  • Chaohua Dong
  • Shuyi Ge
  • Shaoran Li
  • Wen Su
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Abstract

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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Cite

BibTeXRIS
BibTeX
@misc{dong2026specification,
  title = {{Specification LASSO and a Flexible Characteristics-Based Asset Pricing Model}},
  author = {Chaohua Dong and Shuyi Ge and Shaoran Li and Wen Su},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.03391},
  eprint = {2611.03391},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.03391},
  note = {CC0},
}
RIS
TY  - UNPB
TI  - Specification LASSO and a Flexible Characteristics-Based Asset Pricing Model
AU  - Chaohua Dong
AU  - Shuyi Ge
AU  - Shaoran Li
AU  - Wen Su
PY  - 2026
DA  - 2026/11/02
PB  - arXiv
DO  - 10.48550/arxiv.2611.03391
UR  - https://arxiv.org/abs/2611.03391
AB  - 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.
ER  -