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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 游張松(Chang-Sung Yu) | |
dc.contributor.author | Yih-Bin Chiang | en |
dc.contributor.author | 姜義彬 | zh_TW |
dc.date.accessioned | 2021-06-16T13:43:07Z | - |
dc.date.available | 2016-07-31 | |
dc.date.copyright | 2013-07-31 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-11 | |
dc.identifier.citation | 一、中文部份
李羽涵(2007),「金融預警系統之建立-以台灣商業銀行為例」,朝陽科技大學財務金融系碩士論文。 吳蕙真(2007),「台灣集團企業財務預警模式─加入公司治理變數探討」,東吳大學企業管理研究所碩士論文。 林坤霖、李梓瑜、林姿汝、吳欣純、蔡立婕(2007),「台灣企業淘空之探討-以博達為例」,正修科技大學企業管理系專題報告。 康雅欣(2012),「預測航空業破產-Binary Logit與LDA分析之比較」,淡江大學經濟學系碩士班碩士論文。 陳明賢(1986),「財務危機預測之計量分析研究」,台灣大學商學研究所碩士論文。 張大成、林郁翎、黃繼寬(2006),「產業差異與企業財務危機模型」,台灣金融財務季刊,2006年12月第七輯第四期。 葉銀華(2005),「蒸發的股王-領先發現地雷危機」,商智文化出版,2005年1月出版。 楊浚泓(2001),「考慮財務操作與合併報表後之財務危機預警模式」,碩士論文,國立中央大學財務管理研究所。 鄭文英、吳千慧、李勝榮(2006),考量產業、景氣循環因素之台灣上市電子業及塑膠業公司財務預警模式實證研究,中原企管評論,第四卷第一期:69-94。 二、英文部份 Altman, E. (1968), “Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy,” Journal of Finance, 23, No.4, pp. 589-609. Altman, Edward I., G. G. Haldeman, & P. Narayanan (1977), “ZETA Analysis: A new model to identify the bankruptcy risk of corporations,” Journal of Banking and Finance, pp. 29-54. Ashford, J. R. (1959), “An Approach to the Analysis of Data for Semi- Quantal Responses in Biology Response,” Biometrics, 15, pp. 573-581. Atiya, A. F. (2001), “Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey andNew Results,” IEEE Transactions on Neural Networks, 12, No.4, pp. 929-935. Beaver, W. H. (1966), “Financial Ratios as Predictors of Failure,” Journal of Accounting Research, 4, No. 3, pp. 71-111. Berkson, J.(1944) , “Application of the Logistic Function to Bioassay, ” Journal of American Statistical Association, 39, pp. 357-365. Blum, M. (1974), “Failure Company Discriminant Analysis, ” Journal of Accounting Research, Vol. 12, pp. 1-25. Chava, S. & R.A. Jarrow (2004), “Bankruptcy Prediction with Industry Effects,” Review of Finance, 8, pp. 537-569. Collins,R.A. & Green,R.D. (1982), “Statistical Methods for Bankruptcy Forecasting,” Journal of Economics and Business, 34(4), pp. 349-354. Deakin E, B. (1972), “ A Discriminant Analysis of Predictors of Business Failure,” Journal of Accounting Research, pp. 166-179. Fitzpatrick, P. (1932), “ A Comparison of the Ratios of Successful Industrial Enterprises with those of Failed Companies,” The Accountants Publishing Company. Koh, H. C. & Tan, S. S. (1999), “A neural network approach to the prediction of going concern status,” Accounting and Business Research, 29, pp. 211-216. Mayer, P. A. & Pifer, H. W. (1970), “Prediction of Bank Failure,” Journal of Finance, 25, pp. 853-868. Merwin, C. (1942), “ Financing Small Corporations: In Five Manufacturing Industries, ” 1926-1936, National Bureau of Economic Research. Odom, M.D. & Sharda, R. (1990), “A Neural Network Model for Bankruptcy Prediction, ” IEEE INNS IJCNN, Vol.1.2, pp. 163-168. Ohlson, J. A. (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research, 18, pp. 109-131. Smith, R. F. & Winkor, A. H. (1935), “Change in Financial Structure of Unsuccessful Industrial Corporations,” University of Illinois, Bureau of Business Research, Bulletin, 51, pp. 20-31. Truett, J., Cornfield, J. & Kannel, W. (1967), “A Multivariate Analysis of the Risk of Coronary Heart Disease in Framingham,” Journal of Chronic Diseases, 20, pp. 511-524. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62354 | - |
dc.description.abstract | 本研究動機起自2001年安隆財報舞弊至近年全球中資股地雷事件頻傳,以個案分析2004年博達財報造假案,從會計制度、財務分析、銀行授信、信用評等和資本市場運作等審核角度,重新整理和歸納分析博達案,期望能在歷史的事件中汲取教訓。另外,本研究以2001年至2011年間上市櫃中小型電子業公司的財務報表,資本額介於10億至100億之間,並加入4家面臨破產或具有財務危機之公司,總計資料為3,332筆作為樣本數,採Logistic Regression Model(羅吉斯迴歸模型)為基礎,應變數分類為破產與否,未破產公司再利用TCRI信用評等作對照條件加以分析,並佐以個案分析相關資料和過去學者專家之研究,從財務變數和總體經濟變數找出企業發生危機的相關變數,來建構企業財務危機預警模型,期望能提早發現導致公司破產的相關因子。
實証結果顯示,在應變數以財務健全對照破產公司分類,自變數以槓桿比率和利率對於破產模型具有顯著效果,但槓桿比例與預期破產模型關係呈現相反,檢視破產公司樣本資料,印證台灣2004年所爆發的地雷股,其財務報表在自有資產上,大都維持良好的財務槓桿,並無異常負債比例,也因此該年地雷風暴都是猝死倒閉,此也造成自變數槓桿比率與財務管理學上相違之因。另以未破產的公司資料再加上信用評等對照公司財務狀況作分析,總資產報酬率、槓桿比率、市場價值和利率等變數都對此模型具有顯著的效果,且與破產模型關係預期符合。信用評等與總資產報酬率、槓桿比率、市場價值及利率等變數影響有顯著關連,此與台灣金融業實務上是一致的,其作業流程依賴信用評等指標作為授信依據。 經個案與實証分析,公司的財務狀況,若能正確藉由財務報表顯示,資產報酬率(ROA)和槓桿比率等財務比率變數都具有顯著效果,投資人可由財報正確評估公司經營績效和未來發展潛力。但當公司在財報上動手腳,以財務比例作為分析企業破產之相關變數就不顯著,顯示當財報舞弊時,財務報表的任何分析只是徒勞無功,甚至還可能會誤導投資人的決策分析。 | zh_TW |
dc.description.abstract | Enron scandal in 2001 is the head to lead my study, while in recent years subsequent rubbish stock events happened in Global Chinese base companies, which finds me starting the research by analyzing the case of fictional financial report of PROCOMP Informatics LTD. occurred in 2004. By examining accounting system, financial analysis, loan credit rating in bank, credit evaluation and capital market operation to manage, induct and analyze PROCOMP Informatics LTD. case again, which purpose is to learn a lesson from the history. Besides, the sampling of the study is based on the SME (small-medium-enterprise) technology companies, which were listed in Taiwan’s stock market from 2001 to 2011 and capital between 1 billion and 10 billion. While adding 4 companies which are facing bankruptcy or financial crisis, the total sample size of the study reaches 3,332. By using Logistic Regression Model as foundation, the dependent variance is fixed as whether went bankruptcy or not. In additional, by using TCRI (Taiwan Corporate Credit Risk Index) as control condition, to run the analysis of those companies which didn’t go bankruptcy. Furthermore, according to financial and macroeconomic variance of the case study mentioned above and the researches were done by savants in the past, to build a financial crisis warning model from those related variances lead enterprises to go financial crisis, which expect to discover earlier the factors which lead a company to go bankruptcy.
The evidence states, by using whether a company has health finance situation or not as dependent variances, leverage ratio and interest are 2 independent variances which show statistical significance level of the bankruptcy, which actually points the relation between leverage ratio and the model are on the contrary. To check the samples of bankruptcy companies, the financial statement of those companies which became rubbish stock in 2004, basically the asset remain in good leverage ratio, without any unusual debt ratio. It directs me to believe those companies were closed in sudden in the period when rubbish stock event happened. It’s also the reason that independent variance leverage ratio is big different from what mentioned in financial management. If adding credit evaluation data to analyze the financial circumstance of the companies which didn’t go bankruptcy, no matter the return on total assets, leverage ratio, market value, or the variance of interest are figured out its significance level, which conclusion meets my anticipate. Credit rating and the return on total assets, leverage ratio, market value, or the variance of interest shows significance level is actually unanimous in Taiwan financial sector, whose loan credit rating process mainly relies on credit rating system. After the analysis of case study, the evidence is if the financial statement can exactly show the financial ratio variances like ROA and leverage ratio are under significant level, then investors no doubt enable to estimate an enterprise’s operate performance and potential. However, if the enterprise buries fictions in the financial statement, to use financial ratio as tool to analyze enterprise’s bankruptcy wouldn’t show significant level. It’s obviously that not only financial report analysis will be worked to no avail, but also lead investors’ to make wrong decision if make financial statement fiction. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:43:07Z (GMT). No. of bitstreams: 1 ntu-102-P98745008-1.pdf: 5140829 bytes, checksum: eced757f9223f10358be1d610676ef9c (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員審定書 *
誌 謝 ⅰ 中文摘要 ⅱ ABSTRACT ⅲ 圖目錄 ⅶ 表目錄 ⅷ 第一章 緒論 1 第一節 研究動機與背景 1 第二節 研究目的 3 第三節 研究架構 5 第二章 文獻探討 6 第一節 單變量破產模型分析 6 第二節 多變量破產模型分析 7 一、Z-Score 模型 7 二、兩次式區別函數模型 8 三、以現金流量觀點建構之預警模型 8 四、Zeta模型 9 第三節 迴歸模型分析 10 第四節 類神經模型分析 13 第三章 個案分析 16 第一節 金融機構的審查程序 16 第二節 個案分析-博達案 20 一、博達的作帳手法 21 二、政府的亡羊補牢和法令修改 30 第四章 總體產業之財務分析及其研究框架 33 第一節 總體的經濟環境 33 第二節 二分類羅吉斯迴歸(LOGISTIC REGRESSION MODEL)模型 35 第三節 研究樣本選取 37 第四節 研究變數之選定 38 第五章 實證結果與分析 41 第一節 基本統計量 41 第二節 研究結果分析 43 第六章 研究結論與建議 44 第一節 研究結論 44 第二節 研究建議 45 參考文獻 48 一、中文部份 48 二、英文部份 49 | |
dc.language.iso | zh-TW | |
dc.title | 台灣上市櫃電子業公司之財報舞弊研究-以博達個案為例 | zh_TW |
dc.title | A Study on Fictional Financial Report of The Public Listed Technology Companies in Taiwan's Stock Market: A Case Study of PROCOMP Informatics LTD. | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 葉小蓁(Hsiaw-Chan Yeh) | |
dc.contributor.oralexamcommittee | 張舜德,姜國輝,朱惠中 | |
dc.subject.keyword | 財務危機預警系統,地雷股,TCRI信用評等, | zh_TW |
dc.subject.keyword | financial crisis warning model,rubbish stock,TCRI, | en |
dc.relation.page | 50 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-07-11 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
顯示於系所單位: | 財務金融學系 |
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