Does Corporate Social Responsibility Create Value in Acquisitions? Evidence from the German Market
Bibliographic record
Abstract
This paper examines the impact of a firm’s Corporate Social Responsibility (CSR) level on abnormal stock returns around merger and acquisitions (M&A) announcements. Using a sample of transactions announced by German DAX-listed acquirers from 2017 and 2022, the analysis assesses whether CSR creates value for acquiring firms’ shareholders and offers a comprehensive discussion of potential factors supporting or opposing this notion. Our study seeks to fill a notable gap in the German literature on the relationship between CSR performance and abnormal stock returns surrounding M&A announcements. Building upon prior research findings in the US and in an international sample, our investigation focuses on the German market. Employing event study methodology, our results indicate that M&A transactions of German-listed acquirers did not yield significant negative or positive cumulative abnormal returns for event windows of 3 and 11 days. Furthermore, based on multiple linear regression, no evidence was found that CSR positively or negatively influenced abnormal stock returns following M&A announcements, suggesting that positive and negative effects potentially offset each other. The outcomes of our research have important implications for investors, as CSR initiatives do not serve as a positive trading signal, guaranteeing excess returns, which contrasts findings from previous studies in other developed countries. For managers, it is essential to concentrate on factors beyond CSR performance, such as synergies and fit. Finally, both managers and investors should not view CSR as a shareholder value-enhancing short-term investment but as an integral component of fostering sustainable business development.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".