ESG factors in M&A in India: Performance and market insights from 2010 to 2023
Bibliographic record
Abstract
This study assesses the impact of mergers and acquisitions on Environmental, Social, and Governance (ESG) performance and market value of acquiring companies operating in India. Data were collected and analyzed from 69 M&A announcements from January 2010 to June 2023, sourced from the Bloomberg database. The analysis reveals a positive correlation between the post-merger market value of acquiring firms and their ESG performance, indicating that an improvement in ESG factors is associated with increased market value after mergers. Additionally, a positive correlation was identified between acquiring companies’ post-merger ESG performance and their target firms’ pre-merger ESG performance. This finding suggests that when acquiring a target firm with high ESG performance, the acquirer is likely to experience an improvement in its own post-merger ESG performance. Moreover, both the post-merger market value and ESG performance of the acquirer are likely to improve with the profitability and size of firms but will have a negative impact based on the leverage components of the acquiring firms. 
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".