Are Earnings Forecasts Informed by Proxy Statement Compensation Disclosures?
Bibliographic record
Abstract
ABSTRACT We investigate the extent to which market participants use compensation payouts released in the DEF 14A proxy statement (DEF14A) to assess future firm performance by examining sell‐side analysts' earnings forecasts. Consistent with prior work, we confirm that CEO compensation unexplained by current observable economic factors is positively associated with future firm performance. We find that both the likelihood that analysts revise their forecasts following release of the DEF14A and the magnitude and direction of analysts' forecast revisions are positively associated with unexplained CEO compensation. These associations are stronger after the SEC required additional compensation‐related disclosures in late 2006 but lower if the firm has weak corporate governance or more precise other information. Analysts' reactions are not complete, however. Analysts' forecast errors measured months after the DEF14A release are associated with past unexplained compensation, especially in the pre‐2006 period and for analysts who do not revise at the DEF14A release. Taken together, our results suggest that compensation payouts released in the DEF14A contain useful forward‐looking information that is recognized by at least some sophisticated market participants and that the increased disclosure regulations assisted market participants in incorporating this information.
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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 | 0.007 | 0.071 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".