Buy‐Side Analysts and Earnings Conference Calls
Bibliographic record
Abstract
ABSTRACT Companies’ earnings conference calls are perceived to be venues for sell‐side equity analysts to ask management questions. In this study, we examine another important conference call participant—the buy‐side analyst—that has been underexplored in the literature due to data limitations. Using a large sample of transcripts, we identify 3,834 buy‐side analysts from 701 institutional investment firms who participated (i.e., asked a question) in 13,332 conference calls to examine the determinants and implications of their participation. Buy‐side analysts are more likely to participate when sell‐side analyst coverage is low and dispersion in sell‐side earnings forecasts is high, consistent with buy‐side analysts participating when a company's information environment is poor. Institutional investors trade more of a company's stock in the quarters in which their buy‐side analysts participate in the call. Finally, we find evidence that buy‐side analyst participation is associated with company‐level absolute changes in future stock price, trading volume, institutional ownership, and short interest.
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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 | 0.050 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".