[Submitted on 15 Jul 2026]
Title:How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment
View a PDF of the paper titled How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment, by Sanggyu Sean Choi
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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. We extend a supervised lexicon-learning approach to 10-K filings and their Item 1A risk-factor sections, training sentiment scores against both return and volatility labels at three levels of aggregation: sector, portfolio, and individual firm. Across 1,383 filings from 94 Nasdaq-100 technology constituents (2006--2023), we evaluate the resulting twelve sentiment metrics on classification accuracy, correlation with realised market outcomes, and qualitative lexical content. Full-filing text produces more accurate sentiment at the sector and portfolio level for both targets, but this reverses at the individual-firm level, where the narrower Item 1A section performs better -- an effect we attribute to the interaction between document volume and the amount of independent training signal available at each level of aggregation. A Loughran-McDonald dictionary baseline is consistently, strongly negatively correlated with price at every level tested, underscoring the value of a supervised approach for regulatory disclosure text. These findings, and the design choices they motivate, establish the sentiment-generation methodology underlying a subsequent, larger-scale, multi-source system.
Subjects:
Machine Learning (cs.LG); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF); Statistical Finance (q-fin.ST)
Cite as: arXiv:2607.14174 [cs.LG]
(or arXiv:2607.14174v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.14174
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sanggyu Choi [view email] [v1] Wed, 15 Jul 2026 11:16:38 UTC (874 KB)
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