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    <abstract>Cross-market factor research studies whether firm-level signals from one or more markets can predict returns in a target market, but existing public benchmarks do not support cross-market disclosure-to-return evaluation. Building such a benchmark is challenging because filings differ across languages and regulatory systems, disclosure-derived similarity can be biased by common reporting components, and cross-market signals must be evaluated under feasible trading-time alignment. We introduce CrossAlpha, a public annual-report benchmark for cross-market factor research. CrossAlpha addresses these challenges through three corresponding components: Disclosure Distillation, which standardises heterogeneous filings into ten-category English business descriptions; Residual Schema Graph Construction, which builds PCA-whitened cross-market firm-pair scores from schema-level disclosures; and Timing-Aligned Evaluation, which pairs the graph with 11 years of daily OHLCV data to construct forward-return labels under feasible cross-market execution protocols. CrossAlpha covers 3,587 firms and about 10,700 firm-year reports from the United States, Japan, Taiwan, South Korea, and Hong Kong, and releases about 19M directed firm-pair scores. In experiments, disclosure-derived cross-market peers outperform domestic text, industry-code, and return-correlation peers in the US-to-Japan setting (ICIR 0.39 versus 0.07–0.18), and cross-market sources beat the domestic text baseline in most target markets. CrossAlpha offers a publicly released, reusable, return-grounded benchmark for cross-market financial NLP.</abstract>
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%0 Conference Proceedings
%T CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research with LLM Agents
%A Wang, Qian
%A Zhongyi, Tong
%A Chen, Nuo
%A Wu, Zhaomin
%A He, Bingsheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-crossalpha
%X Cross-market factor research studies whether firm-level signals from one or more markets can predict returns in a target market, but existing public benchmarks do not support cross-market disclosure-to-return evaluation. Building such a benchmark is challenging because filings differ across languages and regulatory systems, disclosure-derived similarity can be biased by common reporting components, and cross-market signals must be evaluated under feasible trading-time alignment. We introduce CrossAlpha, a public annual-report benchmark for cross-market factor research. CrossAlpha addresses these challenges through three corresponding components: Disclosure Distillation, which standardises heterogeneous filings into ten-category English business descriptions; Residual Schema Graph Construction, which builds PCA-whitened cross-market firm-pair scores from schema-level disclosures; and Timing-Aligned Evaluation, which pairs the graph with 11 years of daily OHLCV data to construct forward-return labels under feasible cross-market execution protocols. CrossAlpha covers 3,587 firms and about 10,700 firm-year reports from the United States, Japan, Taiwan, South Korea, and Hong Kong, and releases about 19M directed firm-pair scores. In experiments, disclosure-derived cross-market peers outperform domestic text, industry-code, and return-correlation peers in the US-to-Japan setting (ICIR 0.39 versus 0.07–0.18), and cross-market sources beat the domestic text baseline in most target markets. CrossAlpha offers a publicly released, reusable, return-grounded benchmark for cross-market financial NLP.
%R 10.18653/2026.findings-emnlp.914
%U https://aclanthology.org/2026.findings-emnlp.914/
%U https://doi.org/10.18653/2026.findings-emnlp.914
%P 19312-19341

Markdown (Informal)

[CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research with LLM Agents](https://aclanthology.org/2026.findings-emnlp.914/) (Wang et al., Findings 2026)

• CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research with LLM Agents (Wang et al., Findings 2026)

ACL

• Qian Wang, Tong Zhongyi, Nuo Chen, Zhaomin Wu, and Bingsheng He. 2026. CrossAlpha: An Annual-Report Benchmark for Cross-Market Factor Research with LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19312–19341, Budapest, Hungary. Association for Computational Linguistics.