Three Essays on Mergers and Acquisitions: Negative Premium, Failed Mergers, and Deal Quality Implications of Medium of Exchange
Bibliographic record
Abstract
Mergers and acquisitions (M&A) are important economic events.They not only transform firms and industries but also have significant wealth implications for shareholders.In the past decade, investment in merger and acquisition activity has reached unprecedented levels, and the increase in M&A activity and the economic significance of this form of corporate restructuring provide the fundamental motivation for the study of the antecedents and consequences of mergers and acquisitions.In particular, this thesis is motivated by three areas within the M&A literature that demand further research, and it intends to address these gaps in the literature in three separate but interrelated essays.The results from the first essay provides evidence that while target firm's financial distress and overvaluation permit the bidder to offer a negative acquisition premium, it is the combination of the negative market reaction to the negative premium bid (resulting in a convergence of the target's stock price to bid price) and high block ownership (resulting in lower share liquidity) that lead target shareholders to prefer the negative premium bid over selling at market prices.The second essay investigates the issue of long-run post-acquisition underperformance, and contributes evidence to the literature that target firms in terminated deals not only outperform their own pre-acquisition performance but also outperform financial and operating performance of targets in completed deals.The third essay investigates the issue medium of exchange choice (i.e., cash or stock) under conditions of asymmetric information.This third essay reports evidence that the information content of announcement returns of bidding firms in stock financed deals is associated with the bidder valuation and the quality of the combination, but only the quality of the combination for cash bidders.The three essays, though investigating different aspects of the construction of the acquisition bid, each identify that the deal construction (e.g., deal consideration, medium of exchange) and deal outcomes (e.g., failure or success) is a key determinant of post-combination outcomes, both in terms of financial and operating performance. CHAPTER 2 Essay 1: Leaving money on the table? Negative premia on public acquisition targets
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".