Qualitative Disclosure and Changes in Sell‐Side Financial Analysts' Information Environment
Bibliographic record
Abstract
Abstract We examine a routine and timely disclosure, earnings press releases, to determine the extent to which several novel qualitative elements of such disclosures are associated with changes in sell‐side financial analysts' information environment. Using a comprehensive set of GARCH‐based (generalized autoregressive conditional heteroscedasticity) proxies, we examine how disclosure readability's components, across‐document textual similarity, and within‐document lexical diversity alter analysts' information environment. We find that readability in the form of shorter sentences, textual similarity, and lexical diversity are strongly related to decreases in analysts' uncertainty. Further, shorter sentences and lexical diversity improve both public and private information precision, whereas similarity affects solely analysts' private information precision. While the GARCH ‐based proxies allow us to alleviate concerns regarding potentially spurious inferences (Sheng and Thevenot 2012), we note as a caveat that such an estimation restricts our inferences to large, stable, and heavily followed firms. These findings should be of interest to analysts who may wish to explore the latent information contained within the qualitative elements of disclosure, regulators who direct the form and content of disclosure, and academics who study the use (and possible misuse) of various forms of information and its presentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".