Drawbacks of a Delisting from a Sustainability Index: An Empirical Analysis
Bibliographic record
Abstract
All businesses must balance a focus on profits with a focus on ethics. In today’s business world, investors are paying an increasing amount of attention to companies’ commitments to corporate sustainability (CS). In order to enhance the credibility of their CS investments, large companies often seek listings in the major sustainability indexes. However, what are the drawbacks of being delisted from such indexes? This paper aims to determine the impact of such a delisting on firm performance. Our sample covers firms included in and deleted from the FTSE4Good sustainability index from 2008 to 2011. The results reveal a negative relationship between firm performance and a delisting. This relationship is explained in terms of how performance is negatively affected by firms’ scores on different sustainability criteria. In addition, a listing on one of the three main stock exchanges is found to positively moderate the negative relationship between being delisted from a sustainability index and stock-market reactions.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".