Right on target: Is public disclosure of non‐<scp>GAAP</scp> earnings associated with M&A efficiency?
Bibliographic record
Abstract
Abstract We examine the association between target firms' public non‐GAAP earnings disclosures and merger and acquisition (M&A) efficiency. This research question is important, given the widespread use of non‐GAAP metrics in M&A valuation and lack of evidence regarding the real effects of non‐GAAP disclosure. Public non‐GAAP disclosure can enhance bidders' ability to assess a target's core earnings and potential synergy, especially in the earlier stages of due diligence, and enable bidders to make better M&A decisions. We find that target firms' non‐GAAP disclosures are associated with greater M&A efficiency, greater synergies, and lower likelihood of post‐acquisition goodwill impairment. We also find some evidence that target firms' non‐GAAP disclosures are positively related to post‐acquisition operating performance. Further, we find modest evidence that the positive relation between non‐GAAP disclosures and M&A efficiency is stronger (1) for targets that are more difficult to value, (2) for targets with weaker information environments, and (3) when targets' non‐GAAP numbers are of higher quality. Overall, our evidence suggests that non‐GAAP disclosures help facilitate efficient resource allocation in M&As and are associated with real effects on corporate investment. Our evidence is potentially relevant to regulators' concerns about the usefulness of non‐GAAP metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.050 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".