Surpassing stock market's noise-nonstationarity tradeoff by causal-based domain discovery

Songci Xu, Chenchen Zhang, Qiangqiang Cheng and Chi-Guhn Lee

Quantitative Finance, 2026, vol. 26, issue 4, 615-638

Abstract: While neural networks have been extensively applied in stock prediction, forecasting stock returns remains a significant challenge due to the nonstationary nature of financial markets. Existing models struggle to balance two competing demands: learning from large historical datasets to reduce noise, and maintaining temporal relevance in ever-changing market regimes. To address these challenges, we propose a novel Domain Discovery framework that leverages causal relationships to identify training samples from historical market environments that closely resemble the target prediction period. This selection mechanism enables models to learn more effectively from nonstationary data by avoiding outdated or mismatched information, while significantly increasing the number of useful labeled samples. Further, to avoid non-implementable alphas (i.e. profits unachievable under real-world market constraints), embedded within the Domain Discovery framework is a quantile-focused regularization technique that prioritizes the top-performing predictions, aligning training with practical investment goals. The entire approach is model-agnostic and integrates with a wide range of neural network architectures. Extensive experiments across six state-of-the-art architectures demonstrate consistent improvements in out-of-sample performance, underscoring the robustness and versatility of the proposed framework.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2025.2591906 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:26:y:2026:i:4:p:615-638

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2025.2591906

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().