Evidence on the decision usefulness of fair values in business combinations
Bibliographic record
Abstract
Abstract Statement of Financial Accounting Standards (SFAS) 141 (Accounting Standards Codification [ASC] 805) requires that firms record identifiable assets and liabilities acquired in business combinations at fair value. While the FASB argued that these fair values should provide users with incremental decision‐useful information, opponents have continuously argued that they are too difficult to reliably estimate and could be subject to managerial discretion. Using hand‐collected data from US mergers and acquisitions, we find that, on average, fair value adjustments predict future cash flows incrementally beyond pre‐deal book values and cash flows, goodwill, and other firm and deal characteristics. We also find that the relation between fair value adjustments and future cash flows varies predictably based on several factors that affect managers' ability and incentives to provide accurate estimates. Furthermore, despite prevailing concerns about their usefulness, we find that fair values for intangible assets predict future cash flows, on average. However, we find that this relation is driven primarily by the fair values of customer‐ and contract‐related intangible assets and that the fair values of other types of identifiable intangibles do not necessarily convey incremental decision‐useful information. Finally, we find that users appear to rely on the information conveyed by these disclosures, as evidenced by revisions to analysts' forecasts and changes in stock prices. Overall, our findings provide insight regarding the usefulness of current standards and users' reliance on fair values in 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 | 0.024 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".