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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41194完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林修葳 | |
| dc.contributor.author | Sung-Chieh Wang | en |
| dc.contributor.author | 王頌傑 | zh_TW |
| dc.date.accessioned | 2021-06-14T17:23:04Z | - |
| dc.date.available | 2009-08-08 | |
| dc.date.copyright | 2008-08-08 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-24 | |
| dc.identifier.citation | Altman, E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance 23 (September): 589-609.
Chaveesuk, R., C. Srivaree-ratana and A. E. Smith (1999), “ Alternative Neural Network approaches to Corporate Bond Rating,”Journal of Engineering Valuation and Cost Analysis 2(2), 117-131. Coats, P. and Fant, L. (1993), “Recognizing Financial Distress Patterns Using a Neural Network Tool,”Financial Management 22, 142-155. Crouhy, M., D. Galai, and R. Mark. 2001. “Prototype risk rating system,” Journal of Banking and Finance 25: 47-95. Hillegeist, S. A., E. B. Keating, D. P. Cram, and K. G. Lundstedt. 2004. “Assessing the Probability of Bankruptcy” Review of Accounting Studies, 9, 5–34 http://www.display.org.tw/ http://www.ledinside.com/tw/node/3202 http://www.tej.com.tw/ Kim, Neung and Kyungho Kim. 1992. “Predicting Financial Condition of an Industry,” The Journal of Business Forecasting Methods & Systems 3 (Fall): 14-16. Lang, L. and R. Stultz. 1992. “Contagion and competitive intra-industry effects of bankruptcy announcements: An empirical analysis,” Journal of Financial Economics 32: 45–60. Leshno, M., and Y. Spector (1996), “Neural Network Prediction Analysis: The Bankruptcy Case,” Neruocomputing, 10, 125-147. Maksimovic, V. and G. M. Phillips. 1996. “Efficiency of Bankrupt Firms and Industry Conditions: Theory and Evidence,” Center for Economic Studies, U.S. Census Bureau. Working papers. Ohlson, J. A. 1980. “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research 18 (Spring): 109-130. Rose, P. S., W. T. Andrews, and G. A. Giroux. 1982. “Predicting Business Failure: A Macroeconomic Perspective,” Journal of Accounting, Auditing and Finance, 6(1): 20-31. Ross, S. A., R.W. Westerfield, and J. Jaffe (2005), Corporate Finance, 7th edition, McGraw Hill. Sharma S. and V. Mahajan (1980), “Early Warning Indicators of Business Failure.”Journal of Marketing, 44, 80-89. Sudheer, C. and R. A. Jarrow. 2004. “Bankruptcy Prediction with Industry Effects,” Review of Finance 8: 537-569 中華民國主計處（2004），《生產者價格投入係數表》。 台灣經濟研究院（2006），《2006年我國發光二極體產業分析》 財團法人國家實驗研究院科技政策中心（2004），《面板原料供應恐吃緊至2008年 台灣廠商佈局》。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41194 | - |
| dc.description.abstract | 本研究從總體面及產業面角度分析何種自變數會影響台灣面板產業整體的違約狀況，挑選與面板業相關的自變數與產業違約率進行迴歸分析，將其中顯著性佳的變數建構違約預測模型，再用以作樣本外測試，檢視預測結果。以期能找出更精準描述面板產業內企業違約的風險因子。
在研究方法上採用線性函數及分段線性函數兩種函數模型去分析解釋變數與應變數之間的關係。結果顯示油價、台幣對日圓貶值率、17吋面板價格、電腦電子光學製品WPI以及銀行對電子零組件製造業放款餘額年增幅變數，均與產業內企業違約率有顯著的相關性。 樣本外測試結果發現建立的預測模型其標準差誤差均相當微小，顯示模型有優良的預測能力，可用以當作修正傳統會計基礎預測模型的評分項目。 | zh_TW |
| dc.description.abstract | To predict the default probability of companies more precisely, this thesis considers several macroeconomic and industry variables to predict the default rate of Taiwan FPD industry. Examining the correlation between dependent and independent variables by using linear function regression and piecewise linear function regression.
The result reveals that oil price, new Taiwan dollar depreciation against Yen, 17’’ inches panel price, WPI of computer, electronic and optical products and the growth rate of loans & discounts at all banks to electronic parts & components have significant relevance with industry default rate of Taiwan FPD industry. Out-sample testing shows the prediction error is tiny, which means prediction of the model is excellent. Therefore, in spite of considering accounted-based variables, we can use these external environment variables to amend the score of company’s credit risk. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T17:23:04Z (GMT). No. of bitstreams: 1 ntu-97-R95724035-1.pdf: 2364914 bytes, checksum: aebe3d9e51a10056aaa5528a446a0ad0 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 目 錄
誌謝……………………………………………………………………………….. i 中文摘要………………………………………………………………………….. ii 英文摘要………………………………………………………………………….. ii 目錄……………………………………………………………………………….. iii 表目錄……………………………………………………………………………. iv 圖目錄………………………………………………………………………….… iv 第一章 緒論……………………………………………………………………… 1 第二章 文獻回顧………………………………………………………………… 5 第一節 使用外部環境變數修正違約率模型的邊際效益…………………… 5 第二節 依據不同產業做個別的違約預測的考量…………………………… 7 第三章 實證方法………………………………………………………………… 8 第一節 樣本選擇……………………………………………………………… 8 第二節 變數選擇……………………………………………………………… 10 第三節 實證方法……………………………………………………………… 20 第四章 實證分析………………………………………………………………… 23 第一節 敘述統計量…………………………………………………………… 23 第二節 實證結果……………………………………………………………… 24 第三節 預測誤差……………………………………………………………… 31 第五章 結論與建議……………………………………………………………… 32 第一節 結論…………………………………………………………………… 32 第二節 建議…………………………………………………………………… 33 參考文獻………………………………………………………………………….. 34 | |
| dc.language.iso | zh-TW | |
| dc.subject | 違約 | zh_TW |
| dc.subject | 信用評等 | zh_TW |
| dc.subject | 產業信用風險 | zh_TW |
| dc.subject | default | en |
| dc.subject | credit rating | en |
| dc.subject | industry credit risk | en |
| dc.title | 考量總體經濟及產業變數的違約預測模型--以台灣面板產業為例 | zh_TW |
| dc.title | Default prediction with macroeconomics and industrial variables：A case study of Taiwan FPD industry | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王佳真,陳慧玲 | |
| dc.subject.keyword | 產業信用風險,違約,信用評等, | zh_TW |
| dc.subject.keyword | industry credit risk,default,credit rating, | en |
| dc.relation.page | 36 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-26 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
| 顯示於系所單位： | 國際企業學系 | |
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