An Adjustable Robust Approach for ESG-Aware Portfolio Optimization Under Decision-Dependent Return Uncertainty
意思決定依存のリターン不確実性下でのESG配慮ポートフォリオ最適化のための調整可能なロバストアプローチ (AI 翻訳)
Futi Liu, Zian Zhao
🤖 gxceed AI 要約
日本語
ESG基準を組み込んだポートフォリオ最適化において、リターン不確実性がポートフォリオ決定に依存する問題を扱う。共同多面体不確実性集合を構築し、CVaR制約と列生成法を用いた二段階ロバスト最適化を提案。実データで検証し、ESGパフォーマンスとリスク管理の両立を示す。
English
This paper addresses ESG-aware portfolio optimization under decision-dependent return uncertainty. It constructs a joint polyhedral uncertainty set and formulates a two-stage robust optimization with CVaR constraints, solved via column-and-constraint generation. Numerical experiments on real stock data demonstrate effective downside-risk control and improved ESG performance compared to benchmarks.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJ開示やESG投資の拡大に伴い、ESGスコアの不確実性を考慮した投資戦略の需要が高まっている。本手法は、日本の機関投資家やアセットマネージャーがESG情報の不完全性に対処するための実践的ツールを提供する。
In the global GX context
Globally, with ISSB and CSRD driving ESG disclosure, investors face challenges of data ambiguity and rating disagreements. This robust optimization approach offers a framework to integrate ESG criteria while managing uncertainty, relevant for asset managers and financial institutions navigating transition finance and sustainable investment.
👥 読者別の含意
🔬研究者:Provides a novel adjustable robust optimization method for ESG-aware portfolios with decision-dependent uncertainty, advancing the intersection of finance and OR.
🏢実務担当者:Offers a practical tool for portfolio managers to balance ESG performance and risk under uncertainty, useful for ESG integration and client reporting.
🏛政策担当者:Highlights the need for standardized ESG data and disclosure to reduce uncertainty, informing policy on sustainable finance frameworks.
📄 Abstract（原文）
Portfolio optimization is a fundamental problem in financial decision-making, and it is concerned with balancing expected return and investment risk. With the growing emphasis on sustainable investing, environmental, social, and governance (ESG) criteria have been incorporated into portfolio optimization. In practice, ESG-aware portfolio optimization faces parameter ambiguity from market fluctuations, delayed ESG disclosure, and rating disagreement, and the exposure to return uncertainty may depend on portfolio decisions rather than being fully exogenous. Existing studies, however, generally specify uncertainty sets independently of portfolio decisions. To address this limitation, an adjustable robust approach is proposed for ESG-aware portfolio optimization under decision-dependent return uncertainty. A joint polyhedral uncertainty set is constructed to capture the ambiguity in asset returns and ESG scores, where the return bounds depend on first-stage portfolio weights through ESG-related holdings, whereas ESG score uncertainty remains decision-independent. A two-stage robust framework with recourse rebalancing and proportional transaction costs is formulated, with financial loss and ESG performance balanced in the objective and tail risk controlled by a CVaR constraint embedded in a column-and-constraint generation scheme. The resulting minimax problem is solved by a column-and-constraint generation algorithm with a Rockafellar–Uryasev linearization of CVaR over iteratively generated scenarios. Numerical experiments using real stock data are designed to evaluate downside-risk control and portfolio ESG performance relative to deterministic and classical robust benchmarks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/math14152793first seen 2026-08-09 06:00:58
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。