Determinants of Acquisition Premiums: Empirical Evidence from Mining Industry in Australia and Canada
Bibliographic record
Abstract
The objective of this study is to identify and analyse the determinants of the cross-sectional differences in mining industry takeover premiums. The study investigates how the method of payment, international corporate diversification, target takeover resistance, the distribution of target firm ownership and target firm performance affect the size of observed acquisition premiums offered to target mining firms. Using Australian and Canadian data spanning 1997-2007 the study answers five research questions constructed based on the above variables in attempting to fill the gaps left by previous merger and acquisition research. The results show that cash takeovers, hostile target management, the distribution of target ownership and poor target managerial performance prior to the takeover announcement all have statistically significant effects of acquisition premiums, where as the perceived international diversification benefits and the price-to-earnings ratio of the target firm fail to identify the hypothesised relationship. The results have beneficial implications for both individual investors and target firm management associated with an acquisition in the mining industry in regard to evaluating a takeover offer with the aim of maximising investment returns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".