FinTech-Enabled Startup Portfolio Optimization Under Uncertainty: A Multi-Objective CVaR–ESG Framework
不確実性下におけるFinTech対応スタートアップポートフォリオ最適化：多目的CVaR–ESGフレームワーク (AI 翻訳)
Zornitza Yordanova, Hamed Nozari
🤖 gxceed AI 要約
日本語
本研究は、金融テクノロジーを活用し、期待収益、CVaRによる下方リスク、ESG指標、流動性を同時に最適化する多目的ポートフォリオ選択フレームワークを提案する。予算制約や集中度上限などの現実的な制約の下、リスク・持続可能性パラメータの調整により革新的で高ESGのスタートアップへの資金配分が可能となる。投資家やデジタル投資プラットフォームの実践的な意思決定ツールとして有用。
English
This study proposes a multi-objective portfolio optimization framework for startup investments, integrating expected returns, CVaR downside risk, ESG scores, and liquidity constraints. Under realistic constraints (budget, concentration, etc.), adjusting risk and sustainability parameters directs capital toward innovative startups with high ESG performance. It serves as a practical tool for investors, digital platforms, and policymakers in data-driven capital allocation.
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
日本ではスタートアップ投資におけるESG統合が注目されており、本フレームワークはGPIFやVCの投資判断における非財務情報活用の実装例として参考になる。SSBJ基準や有報でのESG開示が進む中、ポートフォリオレベルでの持続可能性評価手法として有用。
In the global GX context
Globally, the framework addresses the growing demand for ESG integration in venture capital and FinTech platforms. It aligns with principles of sustainable finance and responsible investment, offering a transparent balance between financial returns and sustainability goals, relevant for ISSB and TCFD-aligned reporting.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective model integrating CVaR risk and ESG for startup portfolios, extending portfolio theory with sustainability constraints.
🏢実務担当者:Investment platforms can implement this framework for automated, responsible capital allocation, balancing returns, risk, and ESG criteria.
🏛政策担当者:Offers a data-driven approach to guide capital toward sustainable startups, supporting green finance policies.
📄 Abstract（原文）
Startup investment decisions are always accompanied by high uncertainty, limited historical data, and the need to simultaneously consider financial performance, sustainability, and innovation. With the rapid expansion of financial technologies, the use of digital decision-support tools to manage this complex environment has become increasingly important. This study presents a multi-objective optimization framework for startup portfolio selection that simultaneously maximizes expected returns, minimizes downside risk using the Conditional Value-at-Risk (CVaR) measure, improves sustainability performance based on ESG indicators, and considers liquidity constraints. The main innovation of this study is the simultaneous integration of financial and non-financial criteria alongside a set of realistic structural constraints, including budget constraints, the number of options available, the concentration ceiling, and the minimum required levels for ESG, innovation, and liquidity. The results show that the proposed model is able to create a transparent balance between return, risk, sustainability, and investment horizon, and by changing the parameters related to risk and sustainability, it can target capital flows towards more innovative startups with higher ESG scores. This framework can be used as a practical tool for investors, digital investment platforms, and policymakers in responsible and data-driven capital allocation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/fintech5020044first seen 2026-07-18 08:09:40
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