Corporate Sustainability: A Model Uncertainty Analysis of Materiality
Bibliographic record
Abstract
ABSTRACT For decades, scholars searched for a connection between a corporation's current performance with respect to sustainability and the future returns of its stock. In 2016, Khan, Serafeim, and Yoon published an apparent breakthrough in this quest: guidance on materiality from the Sustainability Accounting Standards Board allowed the construction of corporate sustainability scales that reliably predicted stock returns. Their finding had immediate and broad impact, but it remains, in its authors' own words, just “first evidence.” Here, we further explore the relationship between material-sustainability and stock returns by performing a “model uncertainty analysis.” We reproduce the original estimate but conclude that it is a statistical artifact. We then use machine learning to explore the practicality of employing historical associations to determine which aspects of sustainability are material to investors. We conclude that, for one popular source of data on corporate sustainability, accurate guidance on materiality may be difficult to achieve. JEL Classifications: Q51; D22; L25; C11; C18.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".