Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference
Bibliographic record
Abstract
This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions. It considers a risk- and ambiguity-averse investor allocating resources to a risk-free asset, a market index, a green stock, and a brown stock. The study employs a robust control approach rooted in relative entropy to account for model misspecification and derive closed-form optimal investment strategies. The key contribution of this study includes demonstrating, using two sets of empirical data on asset returns and ESG ratings, the substantial influence of ambiguity on optimal trading strategies, particularly highlighting the differential effects of market, green, and brown ambiguities. As a by-product of our analytical solutions, the study contrasts ambiguity-averse investors with their non-ambiguity counterparts, revealing more cautious risk exposures with a reduction in short-selling positions for the former. Furthermore, three types of investors who employ popular suboptimal strategies are identified, together with two loss measures used to quantify their performance. The findings reveal that popular strategies, not accounting for ESG and misspecification in the model, could lead to significant financial costs, with the extent of loss varying depending on those two factors: investors’ ambiguity aversion profiles and ESG preferences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".