Statistical Learning Generalization Guarantees for Multifactor Stock Selection under Adversarial Distribution Shift

Yixuan  Liu

doi:10.54254/2753-8818/2026.29323

Article Information

Received: 4 November 2025

Published: 11 November 2025

DOI: https://doi.org/10.54254/2753-8818/2026.29323

Article Type: Research Article

Cite this article

Liu,Y. (2025). Statistical Learning Generalization Guarantees for Multifactor Stock Selection under Adversarial Distribution Shift. Theoretical and Natural Science,150,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: TNS Vol.150

Part of Series: Theoretical and Natural Science

ISSN: 2753-8818 (Print) / 2753-8826 (Online)

Copyright & License

[1]Bhanot, Karan, et al. "Adversarial Auditing of Machine Learning Models under Compound Shift." European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2023.

[2]Liang, T. (2024). Blessings and curses of covariate shifts: adversarial learning dynamics, directional convergence, and equilibria. Journal of Machine Learning Research, 25(140), 1-27.

[3]Singh, H., Joshi, S., Doshi-Velez, F., & Lakkaraju, H. (2021). Learning under adversarial and interventional shifts. arXiv preprint arXiv: 2103.15933.

[4]Liu, Jiashuo, et al. "Stable adversarial learning under distributional shifts." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 10. 2021.

[5]Vidot, Guillaume, et al. "A pac-bayes analysis of adversarial robustness." (2021).

[6]Sabanayagam, Mahalakshmi, et al. "Generalization Certificates for Adversarially Robust Bayesian Linear Regression." arXiv preprint arXiv: 2502.14298 (2025).

[7]Zhou, Sijia, Yunwen Lei, and Ata Kabán. "Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis." arXiv preprint arXiv: 2504.02957 (2025).

[8]Zhang, Yabin, et al. "Adversarial style augmentation for domain generalization." arXiv preprint arXiv: 2301.12643 (2023).

[9]Liu, Jiashuo, et al. "Distributionally robust learning with stable adversarial training." IEEE Transactions on Knowledge and Data Engineering 35.11 (2022): 11288-11300.

[10]Jin, Gaojie, et al. "Enhancing adversarial training with second-order statistics of weights." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

[11]Ashtiani, Hassan, Vinayak Pathak, and Ruth Urner. "Simplifying Adversarially Robust PAC Learning with Tolerance." arXiv preprint arXiv: 2502.07232 (2025).