Impacts of Corporate Social Responsibility on the Corporate Optimum Risk: Evidence of Mergers and Acquisitions
Bibliographic record
Abstract
This paper investigates whether corporate social responsibility reduces deviations from the firm’s optimal risk-taking within the mergers and acquisitions (M&A) framework. Using two risk-taking measures and a sample of 10,647 transactions between 1991 and 2020 in public firms worldwide, we find that pre-M&A Environment Social and Governance (ESG) acts as a control mechanism to reduce deviations from optimal risk-taking. ESG reduces post-M&A excessive risk-taking but also limits excessive risk avoidance. A more robust ESG performance is associated with smaller deviations from optimal risk-taking levels, although integration complexity and managerial opportunism nurture deviations from the optimum risk-taking. These findings are consistent with the “stakeholder” view of ESG activities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".