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        <title>Bags of Words, Bags of Analysts: Two Event Spaces for Industry Classification</title>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>A company’s GICS sector can be inferred from what the firm writes about itself, and it can be inferred from who chooses to write about the firm. We show these are the same inference over two event spaces, and fit one multinomial naïve Bayes to both. In the first a company is a bag of the words of Item 1 of its 10-K; in the second it is a bag of the 814 analysts who covered it, with no use of what they wrote. Sector prototypes are counted log-probabilities in both cases, w_s = log p_s , so the two differ only in what is being counted.\par Over 426 S P 500 constituents the word space gives 83.7% top-1 recovery and the analyst space 73.7%, against a 16.2% majority-class baseline. Because the two spaces are conditionally independent given the sector their log-likelihoods simply add – but doing so captures only a quarter of the available gain (+0.75 points). A filing supplies some 2,100 wordevents against 18 analyst events at the median, and naïve Bayes counts each as independent, so the word log-likelihood varies across the eleven sectors with a median standard deviation of 766 nats against 8.3 for the analyst log-likelihood, a factor of 92: the sum is numerically dominated and analyst evidence rarely moves a decision. Standardising each log-likelihood within a company before adding removes the scale disparity and recovers +2.9 points [0.4, 5.4], reaching 86.5% top-1 and 94.8% top-2.\par A dense encoder is not better here, and not combinable on the same terms. Qwen3-Embedding-4B over the same filings reaches 76.9% under the nearest-centroid classifier that dense vectors admit, below naïve Bayes over the same words; and having no event counts it cannot form a third event space, so combining it means leaving the likelihood and choosing a scale.</abstract>
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%0 Conference Proceedings
%T Bags of Words, Bags of Analysts: Two Event Spaces for Industry Classification
%A Lensky, Artem
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F lensky-2026-bags
%X A company’s GICS sector can be inferred from what the firm writes about itself, and it can be inferred from who chooses to write about the firm. We show these are the same inference over two event spaces, and fit one multinomial naïve Bayes to both. In the first a company is a bag of the words of Item 1 of its 10-K; in the second it is a bag of the 814 analysts who covered it, with no use of what they wrote. Sector prototypes are counted log-probabilities in both cases, w_s = log p_s , so the two differ only in what is being counted.\par Over 426 S P 500 constituents the word space gives 83.7% top-1 recovery and the analyst space 73.7%, against a 16.2% majority-class baseline. Because the two spaces are conditionally independent given the sector their log-likelihoods simply add – but doing so captures only a quarter of the available gain (+0.75 points). A filing supplies some 2,100 wordevents against 18 analyst events at the median, and naïve Bayes counts each as independent, so the word log-likelihood varies across the eleven sectors with a median standard deviation of 766 nats against 8.3 for the analyst log-likelihood, a factor of 92: the sum is numerically dominated and analyst evidence rarely moves a decision. Standardising each log-likelihood within a company before adding removes the scale disparity and recovers +2.9 points [0.4, 5.4], reaching 86.5% top-1 and 94.8% top-2.\par A dense encoder is not better here, and not combinable on the same terms. Qwen3-Embedding-4B over the same filings reaches 76.9% under the nearest-centroid classifier that dense vectors admit, below naïve Bayes over the same words; and having no event counts it cannot form a third event space, so combining it means leaving the likelihood and choosing a scale.
%U https://aclanthology.org/2026.finnlp-1.57/
%P 681-686

Markdown (Informal)

[Bags of Words, Bags of Analysts: Two Event Spaces for Industry Classification](https://aclanthology.org/2026.finnlp-1.57/) (Lensky, FinNLP 2026)

• Bags of Words, Bags of Analysts: Two Event Spaces for Industry Classification (Lensky, FinNLP 2026)

ACL

• Artem Lensky. 2026. Bags of Words, Bags of Analysts: Two Event Spaces for Industry Classification. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 681–686, Budapest, Hungary. Association for Computational Linguistics.