Target Financial Reporting Quality and M&A Deals that Go Bust*
Bibliographic record
Abstract
This study investigates the role of financial reporting quality in merger and acquisition (M&A) deals that are ultimately terminated (i.e., go bust). If a target is a U.S. publicly traded company, an acquirer’s initial assessment of the potential benefits associated with the acquisition of the company is based on publicly available information. Generally, the acquirer obtains limited private information from the target prior to announcing the deal, but engages in transactional due diligence after signing the acquisition agreement to affirm that the financial reporting warranties made by the target are accurate. We construct a low‐quality financial reporting score based on measures prior research identifies as being associated with less reliable, less relevant, and less precise financial reporting. We find that acquirers offer higher premiums for targets with low‐quality financial reporting. However, we also find that low‐quality financial reporting increases the likelihood of deal renegotiation, and contributes to the probability of deals going bust. We document that failed targets are more likely to restate their financial statements after the announcement of the deal, supporting our conjecture that low‐quality financial reporting contributes to deals being terminated. Our research develops a new measure of low‐quality financial reporting, documents that the measure is related to M&A deal outcomes and financial restatements, and provides insights into the consequences of M&A transactional due diligence.
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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".