The Impact of Environmental, Social, and Governance Factors on the Financial Performance of S&P 500 Listed Firms
Bibliographic record
Abstract
This study investigates the impact of environmental, social, and governance (ESG) factors on the financial performance of non-financial companies in the S&P 500 Index from 2017 to 2020. The study utilizes ESG data from MSCI IVA, Bloomberg, and KLD; financial data from COMPUSTAT; and clean revenue (CR) data from Corporate Knights. The study is divided into two parts: First, we analyze the relationship between ESG ratings and financial performance as measured by a firm’s Tobin’s Q and return on assets (ROA); next, we employ a mediation analysis to explore the effect of MSCI (IVA) data on the relationship between CR and Tobin’s Q. Our study finds that MSCI IVA has a positive and significant association with Tobin’s Q. Bloomberg ESG Disclosure score is positively and significantly associated with ROA but not with Tobin’s Q. When breaking down the ESG components, CR shows a positive and significant association with Tobin’s Q only. The Bloomberg Performance and KLD Environmental scores are significantly positively related to both Tobin’s Q and ROA. We propose that the lack of a standardized ESG reporting framework for companies and the diverse measurement approaches employed by vendors contribute to the limited correlation among various sets of ESG scores. Furthermore, we observe a significant partial mediation effect, which suggests that MSCI IVA mediates the relationship between CR and Tobin’s Q. To validate our findings, we also conduct a robustness check by replacing CR with the Bloomberg Performance Environmental score, and we find that the results remain consistent.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".