Stock Return Volatility, Firm Real Option Value, and Mergers and Acquisitions Premiums
Bibliographic record
Abstract
Considerable effort has been devoted to indicate the critical determinants of acquisition premiums. However, the determinants of mergers and acquisitions (M&A) premiums are not yet fully understood. This research paper empirically examines the effect of stock return volatility on mergers and acquisitions premiums through real options value of bidder and target firms. With a sample of 2,559 completed M&A deals in the US during 1986-2016, we find that bidder firms tend to pay more premiums for the targets that have more future real option value and higher risk. To be more specific, when targets have more real options measured as high Research and Development (R&D) to market value, high sales growth rate, and low leverage ratio, the relationship between target return volatility and acquisition premiums is stronger. This study contributes not only to the literature regarding the determinants of mergers and acquisitions premiums but also to the literature of real options value. Keywords: Mergers and acquisition premiums, acquisition premiums, stock return volatility, real options, growth options
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".