AI RESEARCH
Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization
arXiv CS.AI
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ArXi:2605.09310v1 Announce Type: new ESG-aware portfolio optimization is increasingly important for sustainable capital allocation, yet most learning-based methods still operationalize ESG by appending static scores to the policy observation or reward. This creates a mismatch for sequential control: ESG scores are noisy, provider-dependent, low-frequency, and temporally misaligned with sequential portfolio decisions, while financial evidence suggests that ESG is better treated as a portfolio preference, risk-exposure, or hedge dimension than as a robust alpha factor.