新興市場における信用格下げ予測：人工ニューラルネットワークと伝統的二値分類モデルの比較分析
Predicting credit rating downgrades in emerging markets: A comparative analysis of artificial neural networks and traditional binary classification models (原題)
Jiroj Buranasiri, Prajya Ngamjan, Nuttawaree Ratchpiboon
🤖 gxceed AI 要約
日本語
タイ証券取引所上場の非金融企業を対象に、信用格下げ予測における機械学習（ANN、ランダムフォレスト、XGBoost）と伝統的統計手法（ロジスティック回帰等）の性能を比較。閾値フリー指標ではロジスティック回帰が優れ、閾値ベースではランダムフォレストが最良。目的に応じたモデル選択の重要性を示す。
English
This study compares machine learning (ANN, Random Forest, XGBoost) and traditional statistical models (logistic regression, etc.) for predicting credit rating downgrades of Thai non-financial firms. Logistic regression excels in risk ranking, while Random Forest is best for screening. Model choice depends on the intended use.
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
信用リスク評価はESG評価や気候リスク分析と関連するが、本論文は直接的なGX要素を含まない。日本ではSSBJ開示や気候リスク管理の文脈で、信用リスクモデルへのML活用の参考になる可能性がある。
In the global GX context
While not directly GX-focused, this paper offers insights into ML applications for credit risk, which can be extended to climate-related credit risk assessment. It contributes to the broader discussion on integrating ESG factors into credit analysis.
👥 読者別の含意
🔬研究者:MLと伝統的モデルの比較手法や評価指標の選択に関する示唆が得られる。
🏢実務担当者:信用リスク評価におけるML活用の可能性と限界を理解できる。
📄 Abstract（原文）
This research compares the effectiveness of machine-learning and traditional statistical techniques in predicting annual credit rating downgrades for Thai non-financial firms listed on the Stock Exchange of Thailand during 2018–2023, using a time-ordered train-validation-test framework for predictive model evaluation. The machine-learning models are the artificial neural network (ANN), Random Forest, and XGBoost. The traditional techniques are the linear probability model, logistic regression, and probit regression. Model performance is evaluated using threshold-free PR-AUC and threshold-based metrics because downgrade events occur infrequently. The findings show that logistic regression is more useful for risk ranking, while Random Forest is more useful for downgrade screening. Under threshold-free metrics, logistic regression has the highest PR-AUC of 0.214. Under threshold-based metrics, Random Forest performs best, with a recall of 0.455 and an F1 score of 0.233, while the ANN is second-ranked. Overall, the results show that the preferred model depends on the purpose of use. Logistic regression is more suitable when the objective is to rank firms by downgrade risk, while Random Forest is more suitable when the objective is to screen firms for possible downgrade. Given the limited number of downgrade events in the test sample, the model comparisons should be interpreted as indicative rather than definitive.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://archive.conscientiabeam.com/index.php/29/article/download/5122/9373first seen 2026-09-09 05:47:30
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