A Topic Model for 10-K Management Disclosures
Type
working paper
Date Issued
2023-08-15
Author(s)
Abstract
We investigate the topics discussed in the Management's Discussion and Analysis (MD&A) section of 10-K filings from January 1994 to December 2018. In our modeling approach, we
elicit the MD&A topics by clustering words around a set of anchor words that broadly define a potential topic. From the topics, we extract two hidden loading series from the MD&As - a
measure of topic prevalence and a measure of topic sentiment. The results are three-fold. First, the topics we find are intelligible and distinctive but are potentially multi-modal, which
may explain why classical topic models applied to 10-K filings often lack interpretability. Second, topic prevalence and sentiment tend to follow trends which, by and large, can be
rationalized historically. Third, sentiment affects topics heterogeneously, i.e., in topic-specific ways. Adding to the extant document-level techniques, our study demonstrates the potential
benefits of using a nuanced topic-level approach to analyze the MD&A.
elicit the MD&A topics by clustering words around a set of anchor words that broadly define a potential topic. From the topics, we extract two hidden loading series from the MD&As - a
measure of topic prevalence and a measure of topic sentiment. The results are three-fold. First, the topics we find are intelligible and distinctive but are potentially multi-modal, which
may explain why classical topic models applied to 10-K filings often lack interpretability. Second, topic prevalence and sentiment tend to follow trends which, by and large, can be
rationalized historically. Third, sentiment affects topics heterogeneously, i.e., in topic-specific ways. Adding to the extant document-level techniques, our study demonstrates the potential
benefits of using a nuanced topic-level approach to analyze the MD&A.
Language
English
Keywords
10-K files
MD&A
natural language processing
topic modeling
Event Title
CFE-CMStatistics 2023
Event Location
Zürich
Event Date
17.12.2023
Contact Email Address
matthias.fengler@unisg.ch
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EWP-2307.pdf
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4.24 MB
Format
Adobe PDF
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