Analyst workload and information production: Evidence from <scp>IPO</scp> assignments
Bibliographic record
Abstract
Abstract I examine how changes in analysts' workloads affect their information production. Examining determinants of analysts' information production is important because analyst research affects stock prices and capital allocation decisions. Using periods when analysts work on IPOs to proxy for shocks to their workloads, I predict and find that the accuracy, quantity, and timeliness of analysts' forecasts for non‐IPO firms decline when they are working on IPOs, and they herd closer to the consensus. The reductions in research quality are larger for less experienced analysts, larger IPOs, and less important portfolio firms. Last, I predict and find that information asymmetry increases for non‐IPO firms covered by analysts who are working on IPOs, consistent with analysts' reduction in research quality during IPO assignments negatively affecting the information environment of non‐IPO firms. In sum, I provide the first evidence that analysts' work on IPO deals imposes negative externalities on both the quality of research they produce for the non‐IPO firms they cover and the information environments of these firms, highlighting at least one reason why analyst workload is important to firms and investors.
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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 | 0.002 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".