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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
| dc.contributor.author | Tsung-Hsun Chang | en |
| dc.contributor.author | 張琮勛 | zh_TW |
| dc.date.accessioned | 2021-06-08T05:06:50Z | - |
| dc.date.copyright | 2011-07-07 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-06-29 | |
| dc.identifier.citation | 英文部分：
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(2005), “A Quadratic Interval Logit Model for Forecasting Bankruptcy.” Omega, 33(1), 85–91. [29] Witten, I. H. and Frank, E. (2000), Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco: Morgan Kaufmann. [30] Wu, C. H. and Tzeng G. H. (2007), “A Real-Valued Genetic Algorithm To Optimize the Parameters of Support Vector Machine for Predicting Bankruptcy.” Expert Systems with Applications, 32(2), 397–408. [31] Zhang, G. and Hu, M. Y. (1999), “Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis.” European Journal of Operational Research, 116(1), 16–32. [32] Zmijewski, M. E. (1984), “Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research, 22, 59–82. 中文部分： [1] 陳肇榮，「運用財務比率預測企業財務危機之實證研究」，國立政治大學財政研究所，博士論文，民國72 年6 月。 [2] 陳明賢，「財務危機預測之計量分析研究」，國立台灣大學商學研究所，碩士論文，民國75 年6 月。 [3] 潘玉葉，「台灣股票上市公司財務危機預警分析」，淡江大學管理科學研究所，博士論文，民國79 年5 月。 [4] 黃漢堂，整合支撐向量機模型(SVM)與市場基礎模型應用於台灣營建公司財務危機預測之研究，國立台灣大學土木工程學研究所，碩士論文，民國100年。 [5] 詹益忠，財務預警模型之比較，國立交通大學財務金融研究所，碩士論文，民國95年6月。 [6] 林左裕，劉長寬，「應用Logit 模型於銀行授信違約行為之研究」，2003 年中華民國住宅學會第十二屆年會論文集。 [7] 溫育芳，吳鴻毅，「我國上市公司治理評等系統之建立」，金融風險管理季刊，第二卷第三期，民國95年，1–28。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23658 | - |
| dc.description.abstract | 公司的倒閉風險，在學術界及銀行界都是相當受到重視的議題，一個判斷上的失誤，可能會產生重大的影響。本論文希望可以利用各種不同的資料分類方法，從傳統的Z-score、Logit迴歸到近期相當熱門的類神經網路及支援向量機等方法，尋找出較佳的破產預測模型，使企業或投資人能夠在公司營運出現問題前及早做出因應，以避免產生巨大的損失。 | zh_TW |
| dc.description.abstract | The risk of corporate bankruptcy has been an important topic for the academia and the banking sector, as any misjudgment may lead to disasters. This thesis compares the performance of various data classification methods, such as Z-score, Logit regression, neutral networks and support vector machines. With a better model, corporations and investors can be better prepared before companies show signs of bankruptcy so that huge losses may be avoided. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T05:06:50Z (GMT). No. of bitstreams: 1 ntu-100-R98944052-1.pdf: 426267 bytes, checksum: 17a150e9abe5927f1ba0d58d97392210 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 誌謝 I
中文摘要 II Abstract III 目錄 IV 表目錄 V 圖目錄 VI 第一章 緒論 1 1.1 背景及研究動機 1 1.2 研究目的 2 1.3 研究架構 2 第二章 文獻探討 3 2.1多變量區別分析 3 2.2 Logit分析 4 2.3 類神經網路 4 2.4 支援向量機 6 第三章 研究方法 8 3.1 多變量區別分析 8 3.2 Logistic迴歸分析 8 3.3 類神經網路 9 3.4 支援向量機 10 第四章 研究設計 13 4.1 財務危機之定義 13 4.2 財務變數的選取 15 4.3 資料收集 15 4.4 MDA模型 17 4.5 Logit模型 17 4.6 類神經網路 18 4.7 支援向量機 19 第五章 實驗結果及分析 21 第六章 結論 23 參考資料 24 | |
| dc.language.iso | zh-TW | |
| dc.title | 破產預測模型之比較 | zh_TW |
| dc.title | A Comparison of Bankruptcy Prediction Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 戴天時(Tian-Shyr Dai),金國興(Gow-Hsing King),張經略(Ching-Luei Chang) | |
| dc.subject.keyword | 破產預測,支援向量機,類神經網路,Z-score, | zh_TW |
| dc.subject.keyword | bankrupt prediction,SVM,ANN,Z-score, | en |
| dc.relation.page | 26 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2011-06-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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