Identifying ESG Correlation with Corporate Financial Performance: Research on Exploration & Production Oil and Gas Companies
Bibliographic record
Abstract
2015 dissertation for MSc Finance and Risk. Selected by academic staff as a good example of a masters level dissertation. \nPurpose: The research paper purpose is to investigate the ESG correlation with financial performance from operational accounting and intrinsic firm value perspective. The study throroughly concentrates on E&P companies in UK, Canada and US because there is a deficiency of studies analysing single industry or sector. Moreover, the study is going to add a particular value to investors and stakeholders involved in the E&P companies. \nCritical Literature Review: The literature review examines individually E, S, and G factors in prior research papers in order to establish a foundation to construct the current study thesis with particular focus on E-score because of its pivotal impact on E&P sector. \nMethodology: An Ordinary Least Squares (OLS) panel data method is used in Eviews8, econometric software, to test the ESG factors and financial performance correlation. In addition, the companies' financial data is collected via the Bloomberg Professional Service Terminal while the ESG data via Thomson Reuters DataStream. \nData Analysis: The empirical framework is divided into two models, which consist of 73 and 34 E&P companies over the period from 2009 to 2014. The first model aims to identify prior studies suggested variables as irrelevant for the E&P sector. Whereas, the second model purpose is to enhance the first model equation and to supplement unique determinants for the E&P companies. \nFindings: The first model results prove a gap in the previous studies by identifying weak explanatory power in the variables. However the second model signifies an enhanced model with better-integrated variables. In result, the operating performance demonstrates a positive correlation with E-score while firm value indicates a negative correlation, which is inconsistent with the majority of research paper findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".