Unveiling themes in 10-K disclosures: A new topic modeling perspective

Matthias R. Fengler and Tri Minh Phan

International Review of Financial Analysis, 2025, vol. 103, issue C

Abstract: We analyze the topics in the Management’s Discussion and Analysis (MD&A) section of 10-K filings. Based on word embeddings, our approach identifies MD&A topics by clustering words around anchor words that broadly define potential themes. The resulting topics are interpretable, distinct, and minimally affected by noise. From the MD&As, we extract two loading series: topic prevalence and topic sentiment, both of which exhibit substantial temporal variation and heterogeneity across topics. Examining the link between MD&A topics and stock returns, we reveal that the market response to topic sentiment varies across topics and time horizons.

Keywords: 10-K files; MD&A; Natural language processing; Topic modeling (search for similar items in EconPapers)
JEL-codes: C55 G30 M41 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:103:y:2025:i:c:s105752192500208x

DOI: 10.1016/j.irfa.2025.104121

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