Do green deals attract higher premiums? A study of the potential effect of ESG score on acquisition premium during times of uncertainty
Bibliographic record
Abstract
Purpose: The study aims to investigate whether the ESG score of the target influences the premium paid in M&A transactions. It seeks to contribute to the existing literature by shedding light on the intricate relationship between ESG scores and M&A premiums, especially amid economic uncertainties caused by the COVID 19 pandemic. Furthermore, it aims to examine the potential contributions of separate ESG components to acquisition premiums. Methodology: The hypotheses were tested using a multivariate analysis that initially employed the ordinary least squares (OLS) regression model. This approach was further expanded to include random effects models and robustness tests to further validate the findings. The acquisition premium is calculated by observing the percentage change in the target company's stock price between four weeks prior to announcement and the stock price paid by the acquirer. The target ESG score together with COVID-19 variables constitute the explanatory variables of this study. Theoretical framework: The analysis is based on the theoretical perspectives of information asymmetry and stakeholder theory. The paper then draws upon previous empirical studies done on the relationship between ESG and acquisition premium and factors of uncertainty and acquisition premium. Empirical foundation: The empirical foundation is based on 340 acquisitions deals where the target firms are based in the North American market, specifically the US and Canada. Conclusions: The findings reveal that ESG scores generally do not significantly impact acquisition premiums. Though, during the COVID-19 pandemic the individual scores positively influenced premiums, particularly through social and governance scores, indicating their increased value under uncertain times.
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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 | 0.016 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".