Environmental, Social, and Governance Considerations in WTI Financialization through Energy Funds
Bibliographic record
Abstract
This study investigates interactions between energy funds and the oil market and examines the influence of environmental, social, and governance (ESG) criteria in dynamic responses by fund managers and investors. We test for price and volatility transmission (also referred to as “spillover”) between energy funds and the oil market using recently developed econometric techniques. After identifying specific information flows, we investigate whether certain fund characteristics, including several ESG dimensions, are associated with the existence of information transmissions. Then, in logit regressions, we seek to identify if energy fund managers and their investors make decisions using information regarding ESG metrics, including fossil fuel involvement. The results confirm bidirectional price and volatility transmission between energy funds and the oil market, consistent with evidence of the financialization of energy markets that has been identified in recent studies. Several ESG dimensions are shown to influence investor sentiment and affect price and volatility interactions. Dynamic investor decisions in funds in reaction to oil prices do not appear to be strongly influenced by the fossil fuel involvement of the funds. Fund flows do appear to influence the oil market, with fund fossil fuel involvement being an important factor. This paper evaluates the impact of granular ESG characteristics on energy mutual fund flows, price, and volatility interactions with the oil market. While our results support the findings from previous studies, they also provide several new insights into the impacts of ESG criteria and investor behavior, particularly the dynamic response by fund managers and energy market investors related to the fossil fuel involvement of the funds.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".