密度ベースクラスタリングと感情分析を統合したポートフォリオ最適化：IDX ESG Leadersに関する実証研究
Integrating Density-Based Clustering and Sentiment Analysis for Portfolio Optimization: An Empirical Study on IDX ESG Leaders (原題)
Fausania Hibatullah, Rico Dwi Firmansyah, D. Kusrini
🤖 gxceed AI 要約
日本語
本研究は、インドネシアのESG株価指数（IDX ESG Leaders）を対象に、FinBERTによる感情分析とDBSCANクラスタリングを組み合わせたポートフォリオ最適化手法を提案する。感情スコアを組み込んだクラスタリングにより、従来の平均分散最適化よりもリスク調整後リターンが向上することを実証した。
English
This study proposes a portfolio optimization framework integrating FinBERT-based sentiment analysis and DBSCAN clustering for IDX ESG Leaders stocks. Empirical results show that sentiment-informed clustering improves risk-adjusted performance compared to conventional mean-variance optimization.
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投資の高度化が進む中、テキスト情報を活用したESG評価手法は投資家対応や統合報告書の分析に応用可能。ただし、インドネシア市場特有の結果であり、日本市場への適用には追加検証が必要。
In the global GX context
This study contributes to global ESG investing literature by demonstrating the value of integrating NLP and unsupervised learning in portfolio construction. It offers a replicable framework for markets with emerging ESG disclosure, relevant to ISSB-aligned reporting and sustainable finance.
👥 読者別の含意
🔬研究者:ESG投資におけるテキスト情報と機械学習の統合手法の実証的知見を提供。
🏢実務担当者:ESGポートフォリオ構築における感情分析とクラスタリングの活用可能性を示唆。
📄 Abstract（原文）
The heightened uncertainty in Indonesia’s capital market in early 2025, driven by capital outflows and macroeconomic instability, underscores the need for more robust data-driven portfolio construction frameworks beyond conventional fundamental analysis. This study develops an integrated statistical learning framework that combines transformer-based sentiment analysis and density-based clustering to improve portfolio optimization for stocks listed in the IDX ESG Leaders index. Market sentiment is quantified from financial news using the FinBERT model, producing structured sentiment scores that are incorporated as exogenous inputs. Subsequently, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied to identify latent structures within the asset space based on sentiment similarity, enabling non-parametric cluster formation without imposing distributional assumptions. The empirical findings indicate that IDX ESG Leaders stocks are characterized by predominantly positive sentiment distributions, with clustering results revealing two principal clusters and one noise component. To assess portfolio performance, the resulting clusters are integrated into a mean–variance optimization framework. The results demonstrate that portfolios constructed using sentiment-informed clustering achieve superior risk-adjusted performance, reflecting enhanced diversification and reduced estimation bias relative to conventional approaches. In contrast, portfolios exhibiting high concentration or sentiment homogeneity tend to yield lower efficiency, despite strong fundamental characteristics. Overall, this study demonstrates the statistical value of incorporating unstructured textual information and unsupervised learning techniques into portfolio optimization, providing empirical evidence that supports the integration of sentiment-driven features in data-driven asset allocation strategies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://adac.ee/index.php/stat/article/download/559/267first seen 2026-08-30 05:34:17 · last seen 2026-09-11 05:52:02
🔔 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。