M&A Due Diligence, Post‐Acquisition Performance, and Financial Reporting for Business Combinations
Bibliographic record
Abstract
ABSTRACT Before completing merger and acquisition (M&A) transactions, acquiring firms conduct due diligence. This process provides acquiring firms with a more informed assessment of the expected costs, benefits, and risks of an acquisition and offers one last opportunity to renegotiate or terminate an M&A transaction. However, acquiring firms must trade off the costs and benefits of performing additional due diligence versus completing the acquisition. Based on an analysis of the time to negotiate the acquisition agreement and complete the transaction, I predict and find that competitive pressures, short‐term financial reporting incentives, and agency problems are associated with less due diligence. I also find that less due diligence is associated with lower post‐acquisition profitability, a higher probability of acquisition‐related goodwill impairments, and lower quality fair value estimates for the acquired assets and liabilities. These findings highlight due diligence as an important factor explaining cross‐sectional variation in post‐acquisition performance and financial reporting for business combinations.
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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 | 0.006 | 0.043 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".