Modeling Sustainable Market Volatility and Sectoral Decoupling Through FinBERT-Based Narrative Analysis
FinBERTに基づくナラティブ分析による持続可能な市場ボラティリティとセクター別デカップリングのモデル化 (AI 翻訳)
Cristian Valentin Hapenciuc, Daniela Mihaela NEAMTU, Teodora Cajvan, Camelia Băeșu
🤖 gxceed AI 要約
日本語
本研究は、金融ニュースのテキストデータにFinBERTを適用し、ESGナラティブが市場ボラティリティに与える影響を分析。東欧のテック・エネルギー・銀行セクターを対象に、ESGがリスク要因から価値要因へと変化した「レジームシフト」の兆候を発見。テクノロジー資産が伝統的な指標からデカップリングする「デジタルレジリエンス」効果も示唆。
English
This study applies FinBERT to financial news text to analyze the impact of ESG narratives on market volatility. Focusing on tech, energy, and banking sectors in Eastern Europe, it finds signs of a 'regime shift' where ESG transitions from a risk factor to a value driver. It also suggests a 'digital resilience' effect where tech assets decouple from traditional indices.
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開示が進む中、ESG情報が市場でどう評価されるかは重要。本稿のナラティブ分析手法は、日本の有報や統合報告書のテキスト分析に応用可能で、投資家対応や開示戦略の示唆を与える。
In the global GX context
Globally, this contributes to the growing literature on ESG sentiment and market dynamics, relevant for TCFD/ISSB-aligned disclosures. It demonstrates how NLP can extract leading indicators from unstructured data, complementing traditional financial metrics.
👥 読者別の含意
🔬研究者:NLPとESG評価を組み合わせた実証分析の事例として、手法と結果が参考になる。
🏢実務担当者:ESGナラティブが市場に与える影響を理解し、開示戦略やリスク管理に活用できる。
🏛政策担当者:市場のESG認識の変化を把握し、情報開示政策の効果を評価する手がかりとなる。
📄 Abstract（原文）
This research explores the structural transformation of contemporary financial mar-kets as they pivot from traditional fundamental indicators toward a "narrative eco-nomics" paradigm, where informational flows and collective sentiment strongly cor-relate with asset price discovery. The primary objective is to evaluate the efficacy of Computational Sentiment Analysis as a real-time early warning mechanism for mar-ket volatility, specifically addressing the critical latency gap inherent in official mac-roeconomic "hard data". Adopting a quantitative methodology rooted in Data Science, the study utilizes the FinBERT deep learning architecture to analyze a 12-month lon-gitudinal dataset (May 2025 – May 2026) of financial news and discourse. Framed strictly as an exploratory, multi-entity case study rather than a sector-wide analysis, the investigation focuses on strategic proxies of the 'Twin Transition' in Eastern Eu-rope: Technology (UiPath), Energy (OMV Petrom/Hidroelectrica), and Banking (Transilvania Bank). Consequently, the findings highlight localized, context-specific dynamics rather than establishing broad, sector-wide behavioral rules. The findings provide exploratory support for a potential "regime shift" in market behavior regard-ing sustainability; ESG narratives transitioned from being perceived as a systemic risk in late 2025 to a primary factor associated with market value by early 2026. The evi-dence indicates patterns consistent with a "digital resilience" effect, wherein technolo-gy assets successfully decoupled from the industrial stagnation of traditional proxies, such as the German Deutscher Aktienindex(DAX). Conversely, the energy sector dis-played diminishing marginal impact of economic narratives regarding geopolitical shocks, with investors increasingly prioritizing long-term transition risks and indus-trial demand over short-term alarmist headlines. The study concludes that unstruc-tured textual data serves as a vital leading indicator for market dynamics, underscor-ing the imperative for integrating advanced Natural Language Processing (NLP) into modern economic forecasting and resilient risk management strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.20944/preprints202607.0891.v1first seen 2026-08-08 04:51:07
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