政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152046

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152046

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题名:	銀行倒閉風險評估與預測：可解釋機器學習模型在美國銀行業之應用 The Risk Assessment and Prediction of Bank Failures: The Application of Explainable Machine Learning Models in U.S. Banks
作者:	吳秉勳 Wu, Ping-Hsun
贡献者:	林士貴黃政仁 Lin, Shih-Kuei Huang, Cheng-Jen 吳秉勳 Wu, Ping-Hsun
关键词:	可解釋機器學習銀行倒閉預測懲罰型邏輯斯迴歸隨機森林 XGBoost CAMELS Explainable Machine Learning SHAP Bank Failure Prediction Penalized Logistic Regression Random Forest XGBoost CAMELS
日期:	2024
上传时间:	2024-07-01 12:33:22 (UTC+8)
摘要:	近年來，全球金融市場面臨諸多挑戰，大型銀行倒閉事件的重演引起重視。為了提前識別潛在的倒閉風險，本研究採用可解釋機器學習方法進行銀行倒閉預測並深入分析。本文選擇了懲罰型邏輯斯迴歸、隨機森林和極限梯度提升(eXtreme Gradient Boosting, 以下簡稱XGBoost)三種模型，對不同時間跨度(前一年T-1Y與前兩季T-2Q)的數據集進行分析，探討各財務變數對銀行倒閉預測的影響力。透過變數重要性和SHAP分析，本研究辨別了哪些變數對於銀行倒閉有顯著影響，並評估了這些變數與倒閉風險之間的正、負關聯性。研究結果表明，隨機森林模型在所有測試中表現最為出色，特別是在使用T-1Y資料集時，能夠展現出高度的預測準確性，而資本適足性及經營品質相關變數對於判別倒閉最為重要。此外，本研究的發現對於銀行風險管理與監理機構亦具有實務意義，有助於輔助其更早地識別潛在的倒閉風險並優化風險管理策略。 In recent years, the global financial market has faced numerous challenges, with the recurrence of major bank failures drawing significant attention. To proactively identify potential risks of failure, this study employs explainable machine learning methods to predict and thoroughly analyze bank failures. This paper selects three models: Penalized Logistic Regression, Random Forest, and XGBoost, to analyze datasets over different time spans (T-1Year and T-2Quarter), investigating the impact of various financial variables on bank failure prediction. Through feature importance and SHAP analysis, this research identifies variables that significantly influence the risk of bank failure and evaluates the positive or negative correlations between these variables and the risk of failure. The results indicate that the Random Forest model outperforms all others in every test, especially when using the T-1Y dataset, demonstrating high predictive accuracy. Variables related to Capital adequacy and Management quality are found to be the most critical in distinguishing failures. Moreover, the findings of this study are of practical significance to bank risk management and regulatory bodies, assisting them in identifying potential risks of failure earlier and refining risk management strategies.
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描述:	碩士國立政治大學金融學系 111352010
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111352010
数据类型:	thesis
显示于类别:	[金融學系] 學位論文

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