Flash of green: are environmentally driven stock returns sustainable?
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the source of apparent abnormal returns accrued by “green” company stocks. Though one cannot completely rule out that market-to-book and size factors may already capture the information of Trucosts’ total damage measure, the authors attempt to attribute the effect to risk, a persistent desirable characteristic or a short-run attention effect. Design/methodology/approach The authors construct portfolios of stocks using the Trucost data for identifying more environmentally friendly companies. The authors then compare the risk-adjusted returns of the green portfolios to the non-green portfolios. A secondary analysis of the price impact of being listed on the Newsweek green company listed is used to determine attention effects. Findings The authors find that green stock returns outperform the most polluting stocks by 3.7 percent per year on a risk-adjusted basis. The evidence is most consistent with a significant but economically small attention effect coupled with a longer lasting and greater magnitude desirable characteristic driving green returns. The authors do not find evidence of a risk-contribution to the performance after controlling for well-known factors. Practical implications Fund managers may benefit from this research in selecting green stocks, and thereby enhancing investment performance, with desirable characteristics without fear of increasing risk. Social implications One social implication is that investing in sustainable and green firms may not only be beneficial for the common good but also for the investor. Increased capital flows, and hence lower borrowing costs, for green firms may assist in creating a more ecologically sustainable economy. Originality/value To the authors’ knowledge this paper unique in attempting to determine if the green premium is a short-run inefficiency resolved by attention or a result of a desirable characteristic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".