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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王泰昌 | zh_TW |
| dc.contributor.advisor | Tay-Chang Wang | en |
| dc.contributor.author | 歐晉宏 | zh_TW |
| dc.contributor.author | Jin-Hong Ou | en |
| dc.date.accessioned | 2024-06-19T16:07:25Z | - |
| dc.date.available | 2024-06-20 | - |
| dc.date.copyright | 2024-06-19 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-06-14 | - |
| dc.identifier.citation | Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 international conference on engineering and technology (ICET),
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92750 | - |
| dc.description.abstract | 本研究針對財務危機預測，開發了一種使用機器學習技術及AI方法的財務危機預警模型。考慮到財務危機的複雜性，本研究結合了財務比率、公司治理指標、市場數據和總體經濟指標等多方面指標，並運用了羅吉斯迴歸、隨機森林、支援向量機、神經網路在內等多種機器學習演算法及AI方法來建立預測模型。
研究結果顯示，單純貝氏分類器在各演算法中表現最佳，特別是在召回率方面具有顯著優勢，能有效辨識財務危機公司，降低金融機構的風險。進一步指出，經濟成長率和公司治理指標是影響財務危機預測的關鍵因素。本研究的貢獻在於提供了一個綜合多方面思考之財務危機預測模型，為金融機構在風險管理和決策方面提供了有效之工具。 | zh_TW |
| dc.description.abstract | This study develops a financial distress early warning model that employs machine learning techniques and AI methods to predict financial distress. Given the complexity of financial distress, this study integrates a variety of indicators including financial ratios, corporate governance metrics, market data, and macroeconomic indicators. The prediction model was constructed using a variety of machine learning algorithms and AI methods, such as logistic regression, random forests, support vector machines, and neural networks.
The results indicate that the Naive Bayes classifier performed the best among all algorithms, particularly in terms of recall rate, effectively identifying companies at risk of financial distress and reducing risk for financial institutions. Furthermore, it was found that economic growth rates and corporate governance metrics are critical factors affecting the prediction of financial distress. The contribution of this study lies in providing a comprehensive financial distress prediction model that incorporates multi-dimensional thinking, offering effective tools for risk management and decision-making in financial institutions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-06-19T16:07:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-06-19T16:07:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 I
中文摘要 II ABSTRACT III 目次 IV 圖次 V 表次 VI 第一章 緒論 1 第一節 研究動機與背景 1 第二節 研究目的 2 第三節 研究流程 3 第二章 文獻回顧 4 第一節 預測財務危機 4 第二節 機器學習與深度學習方法 8 第三章 研究方法 18 第一節 資料選取 18 第二節 資料自變數及應變數 18 第三節 模型建立與評估方式 20 第四章 實證結果 23 第一節 敘述性統計 23 第二節 模型預測結果 25 第三節 模型預測結果比較 39 第四節 Z-SCORE與機器學習模型預測結果比較分析 40 第五章 結論與建議 43 第一節 研究結論 43 第二節 研究限制 45 第三節 研究建議 46 參考文獻 47 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 總體經濟指標 | zh_TW |
| dc.subject | 預警模型 | zh_TW |
| dc.subject | 財務危機預測 | zh_TW |
| dc.subject | 財務比率 | zh_TW |
| dc.subject | AI方法 | zh_TW |
| dc.subject | 公司治理 | zh_TW |
| dc.subject | Early Warning Model | en |
| dc.subject | Corporate Governance | en |
| dc.subject | Financial Ratios | en |
| dc.subject | AI Methods | en |
| dc.subject | Financial Distress Prediction | en |
| dc.subject | Machine Learning | en |
| dc.subject | Macroeconomic Indicators | en |
| dc.title | 財務危機預測方法與比較 | zh_TW |
| dc.title | Financial Distress Prediction Methods and Comparison | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉嘉雯;林瑞青 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Wen Liu;Ruey-Ching Lin | en |
| dc.subject.keyword | 財務危機預測,機器學習,AI方法,財務比率,公司治理,總體經濟指標,預警模型, | zh_TW |
| dc.subject.keyword | Financial Distress Prediction,Machine Learning,AI Methods,Financial Ratios,Corporate Governance,Macroeconomic Indicators,Early Warning Model, | en |
| dc.relation.page | 52 | - |
| dc.identifier.doi | 10.6342/NTU202401140 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-06-14 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 會計學系 | - |
| 顯示於系所單位： | 會計學系 | |
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