Bibliographic record
Abstract
Abstract Downside risks are ubiquitous and can profoundly impact firm operations and valuation. Failure to adequately assess and manage target firms' downside risks hinders acquirers' ability to integrate and manage these businesses. This article introduces a novel measure of firms' downside risk similarity (DRS) based on risk factor descriptions and examines its implications for mergers and acquisitions (M&A) outcomes. We first validate that the measure is distinct from existing similarity measures and that it captures similarity in firms' potential significant downside. Using the new measure, we find that the market reacts more positively to deals in which acquirers and targets share more downside risks. Additional analyses show that this beneficial effect of DRS is driven primarily by risks that are idiosyncratic or firm‐specific, consistent with these risks requiring acquirers' relevant expertise to manage. Last, we document that in deals with more similar downside risks, the acquirers experience fewer risk profile changes and are less likely to suffer from adverse outcomes, such as deal‐specific goodwill impairment, divestitures, and significant profitability declines. Overall, we conclude that DRS plays a significant role in the M&A process.
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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 | 0.001 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".