Value is in the Eye of the Beholder: The Relative Valuation Roles of Earnings and Book Value in Merger Pricing
Bibliographic record
Abstract
ABSTRACT Gupta and Gerchak (2002) argue that different acquirers can arrive at different equity valuations for the same target depending on their strategic intent. A reason for acquirers' equity valuations to vary, holding target fundamentals constant, may be that individual acquirers place different weights on underlying fundamentals. I examine this possibility using Burgstahler and Dichev's (1997) theoretical framework. They argue that the relative importance of earnings and book value depends on expected adaptation, which is the likelihood that the existing earnings generating process will be altered. Using restructuring costs to proxy for expected adaptation at the individual acquirer level, I find that the association between the target's earnings (book value) and acquirers' bid prices is decreasing (increasing) in expected adaptation, consistent with theoretical predictions. These findings are less pronounced during merger waves and intense bid competition for the target.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".