While corporate narrative disclosures provide crucial information to capital markets, comprehensively evaluating their qualitative changes over time remains challenging. Narrative text is inherently multidimensional, meaning that an improvement in one textual dimension often occurs alongside changes in others.
arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.
arXiv:2610.09529v1 Announce Type: new Abstract: There are more than 2,000 listed companies on the UK's London Stock Exchange, divided into 11 sectors who are required to communicate their financial r...
arXiv:2606. 18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs).
arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.
arXiv:2604. 27374v2 Announce Type: replace Abstract: As LLMs become credible readers of earnings calls, investor-relations Q\&A, guidance, and disclosure language, supervised financial NLP benchmarks increasingly function as decision evidence for model selection and deployment.
The study examines whether annual reports can reveal how companies disclose AI-related risks and responses. Using a two-stage classification pipeline on 9,821 reports from 1,362 UK listed firms (2020‑2026), the authors find that mentions of AI risk rose from 2.8% to 41.2% and AI adoption disclosures from 13.8% to 45.2%, with most risk mentions clustering around major vendors like Microsoft. Disclosure varies by sector and market segment, with Critical National Infrastructure and AIM reports lagging, and substantive risk disclosures remain rare—only 4.3% in 2025. "Why It Matters": The findings show that while AI risk is increasingly referenced in corporate reports, substantive disclosures are scarce, highlighting a gap in transparency that could affect societal resilience.
The IGT system tackles PolyFiQA Task 2 of the FinMMEval Lab, a multilingual financial QA challenge involving English SEC filings and news in five languages. It distinguishes two question families: numeric‑structured queries are answered via keyword extraction from filings, while synthesis queries use rule‑based passage selection from news. The approach yields a development ROUGE‑1 of ~0.395, a 60% boost over a generic RAG baseline, and places third among twelve teams on the official test set.
arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.
The paper introduces a training‑free, alignment‑free method for corporate intelligence that uses deterministic sparse seed vectors to hash word strings into a fixed high‑dimensional basis. By accumulating these seed vectors across sentence contexts, the authors create corpus‑specific semantic signatures that enable rapid document comparison, issuer fingerprinting, vocabulary shift tracking, and thematic sentence extraction—all on standard CPU hardware. Applied to a multi‑year set of SEC filings, the approach reveals distinct semantic profiles for major corporate events such as Boeing’s 737 MAX crisis, Intel’s supply‑chain disruptions, and Bunge’s acquisition of Viterra, with each profile traceable to its source sentences without any domain‑specific training or LLM inference.
Researchers investigated how different large language models (LLMs) convert corporate text into empirical variables, focusing on thirteen measures such as sentiment, management clarity, uncertainty, answer specificity, and climate and political risk. Using seven LLMs to score earnings call transcripts of S&P 500 companies, they found low cross-model rank correlations (average 0.52) and that transcript-level differences across providers explained only 34% of total score variation. The study shows that model choice significantly alters downstream inference, with varying coefficient magnitudes, signs, and statistical significance, and that averaging across providers stabilizes rankings but not score levels.
arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NM...
arXiv:2502. 15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders.