Executive Summary
Using a variety of NLP techniques, we look at how companies use the narrative MD&A in the 10-K to compensate when their financial statements have relatively lower information contained in the quantitative numbers.
Innovations
NLP techniques used include:
- Training a deep learning model to distinguish between informative, concrete forward-looking statements1 and vague, uninformative statements2
- Implementing TopicTiling to segment the text
- Training a topic model to categorize disclosure sections
- Perform keyword and regular expression searches to identify non-GAAP disclosures
- Extracting targeted data from EDGAR filings
1 For example, “We expect sales growth this year to be in line with the growth we saw last year”
2 For example, “A weakening economy could cause disruption to our sales growth”
Abstract
Firms are required to provide financial information via the financial statements and the management discussion and analysis (MD&A), a narrative explanation of the financial statements. Our study examines how firms use the MD&A channel when their financial statement channel is inadequate. We focus on two textual attributes of the MD&A: non-GAAP disclosure and forward-looking statements. We find that firms with less adequate financial statements discuss non-GAAP measures more and provide a larger number of forward-looking statements. We then identify the topics, and therefore the context, in which non-GAAP and forward-looking disclosures are provided. Our study provides evidence on how managers use the MD&A, a relatively more flexible channel, to provide information when their financial statement channel is less adequate.
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- Paper (SSRN)
Citation
Brown, S. V., Hinson, L. A., and J. W. Tucker. 2024. Financial Statement Adequacy and Firms’ MD&A Disclosures. Contemporary Accounting Research. 41 (1): 126-162.