ESG Factors: How Are Stock Returns, Operating Performance, and Firm Value Impacted?
Bibliographic record
Abstract
This study evaluates the relationship between an aggregate score for environmental, social, and governance indicators and financial performance for US firms.The study uses publicly listed firms on the S&P Mid Cap 400, S&P 500, and the S&P Small Cap 600 Index.To accomplish the empirical analysis of this dissertation, two methods are used; the Fama & French portfolio formation method and a panel regression of operating performance (ROA) and firm value (Q) against ESG.The stock return analysis using Fama and French methodology is implemented by forming portfolios of firms with robust ESG scores and lower ESG scores using the top 10% of the S&P 1500 and the lowest 10% of companies.We find a negative alpha for both portfolios, which is less negative for the High ESG portfolio, displaying a link between ESG and CFP.The operating/firm value analysis uses annual data from 2010 -2016 for 1,371 companies.ROA and Tobin's Q (dependent variables) are regressed on ESG, controlling for firm size and sales growth.A weak positive relationship is discovered between ROA, Q, and ESG.An agreement on the effect of sustainability factors on performance has not been established in the existing literature.Some studies indicate a positive link between sustainability factors.Alternate studies show an inverse connection.Still, various studies have unclear results or are absent from statistical influence.Consequently, this creates opportunities for further investigation on the subject.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".