The Integration of Value-at-Risk in Assessing ESG-Based Collaborative Synergies in Cross-Border Acquisitions: Real Options Approach
Bibliographic record
Abstract
This paper presents a novel framework for valuing ESG-based collaborative synergies in cross-border mergers and acquisitions (M&A) using a real options approach, with a specific application to L’Oréal’s acquisition of Aesop. The methodology integrates a Value-at-Risk (VaR) model to quantify and adjust for ESG-related risks, providing a more robust valuation framework. We demonstrate how linking sustainability practices with real option valuation in multinational corporations (MNCs) can enhance long-term value creation and reduce risk, thereby aligning synergy goals with ESG objectives. By applying our VaR-adjusted model to the L’Oréal–Aesop case, this study contributes to corporate finance by integrating advanced risk management and sustainability into synergy valuation, and to international business by providing an empirical example of this integrated valuation approach for cross-border acquisitions.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".