Oh what a beautiful morning! The time of day effect on the tone and market impact of conference calls
Bibliographic record
Abstract
Using textual analysis software, we examine whether and how the tone of the question and answer ("Q&A") portion of earnings-related conference calls varies with the time of day. We find that the tone of the conversations between analysts and managers becomes significantly more negative as the day wears off. This continuous, hour-by-hour change is likely the result of mental and physical fatigue gradually and imperceptibly setting in. The same pattern holds for textual uncertainty, increasing as the day wears off, the conversational tone is more wavering and less resolute. We document that conversational tone has economic consequences; more negatively toned conversations are associated with more negative abnormal stock returns during the call period and immediately thereafter. Notwithstanding the negativity associated with later day calls, firms exhibit significant "stickiness" in their choice of call time; having initiated the earnings conference call in the afternoon in the prior quarter is the most significant determinant of their doing so in the current quarter, dominating the sign of the earnings news and alternative measures of the firm’s need for equity capital. Analysis of post-call (50 days) returns indicates that there is an initial negative overreaction to bad news earnings information and, incrementally, to calls initiated in the afternoon, that eventually reverses. In contrast, the negative impact of tone deterioration on stock returns, documented here, does not reverse. To the best of our knowledge, this is the first study to document the effects of human physiological and mental factors on corporate communications with investors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".