What's in a Name? Investors' Reactions to <scp>Non‐GAAP</scp> Labels*†
Bibliographic record
Abstract
ABSTRACT Using a mixed‐methods approach, I investigate how the terms that firms use to label non‐GAAP earnings interact with investors' scrutiny of non‐GAAP reporting to affect investors' information search behavior and investment decisions. This study informs regulators, who have expressed concern over the mislabeling of non‐GAAP measures, and managers, who are often criticized for their misuse of discretion in non‐GAAP reporting. I first provide descriptive evidence on the non‐GAAP labels used in practice, followed by a survey to understand what investors believe these labels convey about earnings quality. Finally, drawing on theory from psychology, I predict and then find in an experiment that investors who are more likely to scrutinize non‐GAAP reporting are not affected by non‐GAAP labels when deciding to seek out the non‐GAAP reconciliation, and react positively to appropriately used labels. However, investors who are less likely to scrutinize non‐GAAP reporting rely on the cue provided by the label when deciding whether to seek out the non‐GAAP reconciliation, and are more likely to be misled by inappropriately used labels. For regulators, these findings validate concerns related to the mislabeling of non‐GAAP measures and suggest increased non‐GAAP scrutiny helps counteract mislabeling. For managers, these findings suggest investors react favorably to the appropriate use of discretion in non‐GAAP reporting.
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".