The implications of firms' derivative usage on the frequency and usefulness of management earnings forecasts
Bibliographic record
Abstract
Abstract We investigate how firms' use of derivatives impacts voluntary disclosure and offer four main findings. First, we find that when firms begin using derivative instruments, they increase the frequency of management earnings forecasts. Second, using path analysis, we find a direct link between derivative usage and forecast frequency, as well as an indirect link through reduced earnings volatility. Third, we find that CEOs with more pronounced career concerns increase forecast frequency only when derivatives make earnings easier to forecast and find no evidence that investor demand drives the decision to provide a forecast. These results suggest that the primary mechanism for the association between derivative usage and forecast frequency is a reduction in the manager's costs of providing the forecasts. Finally, we find that the majority of derivative‐induced forecasts are uninformative to capital market participants, especially after FAS 161 provided the necessary underlying data to understand how firms use derivatives. Overall, we provide the first empirical evidence that firms that use derivatives issue more management forecasts, but we also find that these incremental forecasts are largely uninformative and appear driven by managerial career concerns.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".