Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization

• URL: http://arxiv.org/abs/2605.09310v1
• Date: Sun, 10 May 2026 04:06:48 GMT
• Title: Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization
• Abstract summary: ESG scores are noisy, provider-dependent, low-frequency, and temporally misaligned with sequential portfolio decisions.<n>We propose to impose ESG constraints without modifying the financial policy's observation or reward.<n>We introduce MACF-X, a family-specific interfaces that converts MACF costs and uncertainties into native constrained-optimization.
• Score: 34.69757894873943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We propose to impose ESG constraints without modifying the financial policy's observation or reward, using a Multimodal Action-Conditioned Constraint Field (MACF) that learns mechanism-specific ESG costs from point-in-time multimodal evidence and contemplated portfolio transitions. We then introduce MACF-X, a family of optimizer-specific adapters that converts MACF costs and uncertainties into native constrained-optimization interfaces through a shared slack- and uncertainty-aware pressure layer. Across multiple constraint-integration interfaces, MACF-X reduces tail ESG budget pressure while maintaining competitive financial performance. Ablations show that this improvement depends on dynamic evidence inputs and three-head decomposition, while static ESG-score proxies are nearly indistinguishable from score-shuffled noise baselines.

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