Can Investors Detect Managers' Lack of Spontaneity? Adherence to Predetermined Scripts during Earnings Conference Calls
Bibliographic record
Abstract
ABSTRACT This paper examines whether market participants infer negative information about future unexpected firm performance when managers adhere to predetermined scripts when responding to questions during earnings conference calls. I argue that managers respond to questions from prepared scripts to avoid the disclosure of bad news. Using a measure of the adherence to predetermined language, I provide evidence that a lack of spontaneity is negatively associated with the market reaction to the call and with the abnormal returns in the subsequent quarter. I further find that analysts downgrade their forecasts following these calls. I also provide evidence that adherence to predetermined language is negatively associated with future unexpected firm accounting performance, supporting investors' negative response to it. Finally, I find that bid-ask spreads increase and firms are less likely to guide future earnings when managers adhere to the predetermined language of a script, suggesting that firms provide less information, not more, during these calls. JEL Classifications: G14; M40; M41.
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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 | 0.006 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".