Buy‐Side Analysts and Earnings Conference Calls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 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 it