KPI information acquisition by analysts: Evidence from conference calls
Bibliographic record
Abstract
Abstract Investors are increasingly placing reliance on alternative performance measures (APMs). Key performance indicators (KPIs) are a subset of these APMs that illustrate industry‐specific firm financial and operational performance. In this study, we investigate analysts’ demand for KPI‐related information in earnings conference calls and whether managers adjust their decisions about voluntary KPI disclosure in subsequent earnings calls. Using 51 KPIs for six industries, we find that after analysts request KPI‐related information, managers increase both the likelihood and intensity of their KPI disclosure in subsequent earnings conference calls. This effect is more pronounced when the firm has less relevant earnings and lower proprietary costs, and when analysts are connected to management. Analyst KPI demand leads to a higher coverage of KPIs in subsequent news and generates benefits in analyst forecast dispersion, cost of capital and stock liquidity. Our study highlights the role that analysts play in voluntary KPI disclosure when there is an absence of mandatory integrated reporting.
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How this classification was reachedexpand
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.022 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".