Using 10-K text to gauge COVID-related corporate disclosure
Bibliographic record
Abstract
During the pandemic era, COVID-related disclosure has become quite critical for shareholders and other market participants to understand the uncertainties and challenges associated with a firm's operation. However, there is no well-grounded and systematic measure to gauge the intensity of COVID-related disclosure and its plausible impact. Therefore, this study develops and validates various COVID-related disclosure measures. More specifically, using a sample of publicly listed U.S. firms and applying natural language processing (NLP) on 10-K reports, we have developed two types of COVID dictionaries (or COVID-related disclosure measurement tools): (a) overall COVID dictionary (count of all COVID-related words/phrases) and (b) contextual COVID-dictionary (count of COVID related words/phrases preceded or followed by positive, negative tones, or financial constraints words). Subsequently, we have validated both types of COVID dictionaries by investigating their association with corporate liquidity events (e.g., dividend payment, dividend change). We confirm that the overall COVID dictionary effectively predicts a firm's liquidity event. We find similar results for contextual COVID dictionaries with a negative spin (i.e., COVID disclosures with a negative tone or an indication of financial constraints). Our results further show that better-governed firms (e.g., greater board independence, and more female directors) tend to have more COVID-related disclosures, despite the fact that more COVID-related disclosures suppress a firm's market-based stock performance (e.g. Tobin's Q). Our results suggest that better-governed firms prefer greater transparency, even if it may hurt their market performance in the short run.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".