金融ビッグデータ分析における深層信念ネットワークに基づく企業財務健全性評価システム
An Enterprise Financial Health Rating System Based on Deep Belief Networks in Financial Big Data Analytics (原題)
J. Z. Lin
🤖 gxceed AI 要約
日本語
本研究は、深層信念ネットワーク（DBN）を用いて企業の財務健全性を7段階に分類する評価システムを構築。上海・深センA株市場と香港取引所の繊維・アパレル・皮革企業247社（2013-2023年、2,486社年）を対象に、財務指標とESG開示指標を統合。AUC-ROC 0.9218、F1スコア0.8312を達成し、ESG指標の追加で高リスク識別精度が3.24ポイント向上。SHAPによる解釈可能性も提供。
English
This study constructs a financial health rating system using Deep Belief Networks (DBN) to classify enterprises into seven tiers. Using 2,486 firm-year observations from 247 textile, apparel, and leather companies listed on Shanghai-Shenzhen A-shares and Hong Kong Stock Exchange (2013-2023), the model integrates financial and ESG disclosure indicators. It achieves AUC-ROC of 0.9218 and macro F1 of 0.8312, with ESG indicators improving high-risk identification by 3.24 percentage points. SHAP provides interpretability.
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開示基準や有報でのサステナビリティ情報開示が進む中、ESG開示指標を財務健全性評価に組み込む手法は、金融機関やサプライチェーン金融プラットフォームでの与信判断に応用可能。日本企業の開示データを用いた検証が今後の課題。
In the global GX context
As ISSB and CSRD frameworks push ESG disclosure integration, this study demonstrates how ESG indicators can enhance financial health assessment, offering a deployable tool for credit institutions and supply-chain finance. The methodology aligns with global trends in AI-driven ESG analytics and could be adapted to other markets.
👥 読者別の含意
🔬研究者:AI×ESG評価の実証例として、DBNとSHAPを組み合わせた手法が参考になる。
🏢実務担当者:サプライチェーン金融や与信判断に活用できるAIベースの評価ツールの実装例。
🏛政策担当者:ESG開示情報の金融安定性への寄与を示すエビデンスとして注目に値する。
📄 Abstract（原文）
The global textile and leather industry faces mounting pressure from market volatility, supply-chain uncertainty, financial risk, and ESG-related disclosure requirements. This study constructs an enterprise financial health rating system based on a Deep Belief Network (DBN). The sample includes 247 listed companies in the textile, apparel, and leather products sectors from the Shanghai-Shenzhen A-share markets and the Hong Kong Stock Exchange during 2013–2023, yielding 2,486 firm-year observations. Eighteen core features are extracted across five analytical dimensions: solvency, operational efficiency, profitability, cash flow quality, and ESG financial disclosure. A three-layer stacked Restricted Boltzmann Machine architecture combined with Bayesian hyperparameter optimization is used to perform seven-tier classification. The DBN model outperforms Logistic Regression, SVM, XGBoost, and LSTM across major metrics, achieving an AUC-ROC of 0.9218, a macro-averaged F1 score of 0.8312, and a KS statistic of 0.7134. Ablation experiments further show that incorporating ESG financial indicators improves high-risk rating identification accuracy by 3.24 percentage points. SHAP interpretability analysis provides traceable evidence for rating decisions. The system can therefore serve as a deployable intelligent rating tool for credit institutions and supply-chain finance platforms.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.7716/aem.v15i3.3918first seen 2026-08-19 05:35:42 · last seen 2026-09-21 05:20:54
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