ESG investment: What do we learn from its interaction with stock, currency and commodity markets?
Bibliographic record
Abstract
Abstract This paper examines ESG portfolio's causal relationship with conventional and ethical equity prices, exchange rates and commodity prices. Using multi‐scale wavelet decomposition, asset returns are decomposed into three timescales (short‐, medium‐ and long‐term), and a three‐step filtered framework is used to explore dynamic non‐linear linkages. We document significant bidirectional causal relationship between ESG, conventional and ethical equity portfolio returns. While the causality persists from the short‐ to medium‐term, it is relatively weaker in the long‐term. We further observe statistically significant causality running from ESG portfolio returns to currency and commodity returns. This causality is strongest in the short‐term, turns weaker in the medium‐term and, in some instances, disappears in the long‐term. These results are generally robust for the use of original returns and VAR‐filtered returns. However, as we control for conditional heteroskedasticity in the return series, the causality appears weaker particularly between ESG portfolio and commodity returns. Our results have important implications for planning portfolio allocation and devising hedging and diversification strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".