(3.235.139.152) 您好!臺灣時間:2021/05/11 12:40
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:呂佳容
研究生(外文):Chia-Jung Lu
論文名稱:中小企業授信模式之研究
論文名稱(外文):A Study of Credit Rating Models for Small and Medium Enterprises
指導教授:余銘忠余銘忠引用關係
指導教授(外文):Min-Chun Yu
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:企業管理系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:60
中文關鍵詞:信用評等支援向量機類神經網路分類技術
外文關鍵詞:Credit RatingArtificial Neural NetworksSupport Vector MachinesClassification Technology
相關次數:
  • 被引用被引用:0
  • 點閱點閱:680
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
銀行的獲利來源主要來自於放款業務,近年來,由於競爭激烈,為爭取更多客戶,致未審慎考量客戶未來還款能力,導致銀行成本上升、呆帳增加,使得利潤不足以填補呆帳。信用評等對於銀行業者借貸給客戶時,是重要的衡量指標及風險管理機制。台灣的經濟體系以中小企業為主,過去學者探討信用評等議題以大型企業為對象居多,大型上市櫃公司的財務透明度較高,易於取得,但相對於中小企業而言,財務及資訊面不易獲得,且正確性較低,故其信用之評等較為不易。
本研究係以國內某商業銀行之中小企業借款作為研究對象,採用類神經網路(Artificial Neural Networks, ANNs)、支援向量機(Support Vector Machines, SVM)兩種工具建立中小企業信用評等之模型,以企業財務比率及非財務條件為預測變數,希冀能客觀評估中小企業貸款之信用風險狀況,以作為評等之依據,並與傳統方法之區別分析進行比較其信用分類準確率。以盼藉由導入人工智慧分類技術以供銀行業者作為信用評等業務之參考。
For banking industry, business loan accounts for a large portion of business revenue. However, business profit has been decreasing due to severe price competition. In order to attract more customers, many banks have been either carelessly or knowingly unsuccessful to verify borrowers’ capability of paying back loan on time. As a result, bad debts are piling up and loss incurred. Therefore, the quality of credit rating techniques is vital to effectively grade potential borrowers. Past studies have primarily focused on credit rating methods for large listed companies where rather transparent and accurate financial information has been released regularly. However, for small and medium enterprises, financial information is always hard to collect and with little credibility.
This study aims at investigating credit rating models suitable for small and medium businesses. Non-financial information such as human capital for company owners is used to accompany several financial ratios for credit rating. Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) are hence utilized to establish credit rating models using the above information. The performances of the artificial intelligence based techniques are compared with traditional Discriminant analysis.
中文摘要 ………………………………………………………………. i

英文摘要 ………………………………………………………………. ii

致謝 ………………………………………………………………. iii

目錄 ………………………………………………………………. iv

表目錄 ………………………………………………………………. v

圖目錄 ………………………………………………………………. vi

第一章 緒論…………………………………………………………. 1
第一節 研究背景…………………………………………………. 1
第二節 研究動機…………………………………………………. 4
第三節 研究目的…………………………………………………. 6
第四節 研究流程…………………………………………………. 7
第二章 文獻探討……………………………………………………. 8
第一節 信用評等…………………………………………………. 8
第二節 信用評等之分類技術……………………………………. 13
第三章 研究方法……………………………………………………. 19
第一節 資料蒐集…………………………………………………. 19
第二節 分類技術…………………………………………………. 24
第四章 實證資料分析………………………………………………. 37
第一節 實證結果…………………………………………………. 38
第二節 綜合分析結果比較………………………………………. 45
第五章 結論與建議…………………………………………………. 49
第一節 結論與管理意涵…………………………………………. 49
第二節 研究限制與後續研究建議………………………………. 53
參考文獻 ………………………………………………………………. 55
一、中文部份
1.中華民國銀行公會網站(無日期)。金融概況之金融機構及業務之變動。民國97年9月12日取自:http://www.ba.org.tw/financial05.asp
2.朱志忠(2006)。淺論不良債權與資產管理公司市場概況。華南金控月刊,37(1),9-22。
3.企業評等與銀行授信(葉英俊、紀榮年譯)(1998)(初版)。財團法人台灣金融研訓院。
4.邱志洲、李天行、周宇超、呂奇傑(2002)。整合鑑別分析與類神經網路在資料探勘上之應用。工業工程學刊,15(2),11-31。
5.金融統計指標(98年2月版)(發佈日:民國98年4月15日)【資料檔】。台北市:行政院金融監督管理委員會。
6.周俊宏(2006)。運用支撐向量機與類神經網路於信用卡授信決策之研究。國立台灣科技大學碩士論文。
7.張斐章、張麗秋(2007)。類神經網路(三版)。台北:東華書局。
8.陳順宇(2005)。多變量分析(四版)。台北市:華泰書局。
9.連惟謙(2004)。應用資料分析技術進行顧客流失與顧客價值之研究。中原大學碩士論文。
10.黃怡華(2004)。應用類神經網路與關聯法則於銀行消費性貸款。國立成功大學碩士論文。
11.黃承龍、陳穆臻、王界人(2004)。支援向量機於信用評等之應用。計量管理期刊,1,155-172。
12.葉怡成(2000)。類神經網路模式應用與實作。儒林出版社。
13.葉國興、黃天麟、倪成彬(1999)。銀行對企業授信規範。財團法人金融人員研究中心。
14.經濟部中小企業處(2008)。2008中小企業白皮書。台北市:經濟處。
15.類神經網路設計(汪惠健譯)(2007)(初版)。臺北市:湯姆生。(原著出版年:1996年)。
二、英文部分
1.Abdou, H., Pointon, J., & El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications, 35, 1275-1292.
2.Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
3.Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis & neural networks. Journal of Banking and Finance, 18, 505-529.
4.Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929-935.
5.Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.
6.Chuang, C. L, & Lin, R. H. (2009). Constructing a reassigning credit scoring model. Expert Systems with Applications, 36(2), 1685-1694.
7.Coats, P.K., & Fant, L. F. (1993). Recognizing financial distress patterns using neural network tool. Financial Management, Autumn, 142-155.
8.Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematical Control Signal Systems, 2, 303-314.
9.Desai, V. S., Crook, J. N., Overstreet, G. A., & Jr. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95, 24-37.
10.Doumpos, M., Kosmidou, K., Baourakis, G., & Zopounidis, C. (2002). Credit risk assessment using a multicriteria hierarchical discrimination approach: A comparative analysis. European Journal of Operational Research, 138, 392-412.
11.Dutta, S., Shekhar, S., & Wong, W. Y. (1994). Decision support in non-conservative domains: Generalization with neural networks. Decision Support System, 11, 527-544.
12.Ederington, L. H. (1985). Classification models and bond ratings. The Financial Review, 20(4), 237-262.
13.Glorfeld, L. W. (1996). A methodology for simplification and interpretation of backpropagation-based neural network models. Expert Systems with Applications, 10(1), 37-54.
14.Hecht-Nielsen, R. (1990). Neurocomputing. Menlo Park, CA: Addison- Wesley.
15.Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximations. Neural Networks, 2, 336-359.
16.Huang, C. L., Chen, M. L., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33, 847-856.
17.Huang, Z., Chen, H. C., Hsu, C. J., Chen, W. H., & Wu, S. S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37, 543- 558.
18.Johnson, R. A., & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis. Prentice-Hall, Upper Saddle River, New Jersey.
19.Joos, P., Vanhoof, K., Ooghe, H., & Sierens, N. (1998). Credit classification: A comparison of logit models and decision trees. In Proceedings Notes of the Workshop on Application of Machine Learning and Data Mining in Finance, 10th European Conference on Machine Learning, 59-72, Chemnitz, Germany.
20.Kim, J. W. (1993). Expert systems for bond rating: A comparative analysis of statistical, rule-based and neural network systems. Expert Systems, 10, 167-171.
21.Lee, T. S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression spines. Expert Systems with Applications, 28, 743-752.
22.Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23, 245-254.
23.Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression. Computational Statistics & Data Analysis, 50, 1113-1130.
24.Li, S. T., Shiue, W., & Huang, M. H. (2006). The evaluation of consumer loans using support vector machines. Expert Systems with Applications, 30, 772-782.
25.Lippmann, R. P. (1987). An Introduction to Computing with Neural Net. IEEE ASSP Magazine, April, 4-22.
26.Lo, A. W. (1986). Logit versus discriminant analysis: A specification test and application to corporate bankruptcies. Journal of Econometrics, 31, 151-178.
27.Maher, J. J., & Sen, T. K. (1997). Predicting bond ratings using neural networks: a comparison with logistic regression. Intelligent systems in accounting, finance and management, 6, 59-72.
28.Malhotra, R., & Malhotra, D.K (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research, 136, 190-211.
29.Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131.
30.Orgler, Y. E. (1970). A Credit Scoring Model for Commercial Loans. Journal of Money, Credit and Banking, 2(4), 435-445.
31.Piramuthu, S. (1999). Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research, 112, 310-321.
32.Salchenberge, L. M., Cinar, E. M., & Lash N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, pp.899-916.
33.Sanchez, M. S., & Sarabia, L. A. (1995). Efficiency of multi-layered feedforward neural networks on classification in relation to linear discriminant analysis, quadratic discriminant analysis and regularized discriminant analysis. Chemometrics and Intelligent Laboratory Systems, 28, 287-303.
34.Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of the neural networks: The case of bank failure predictions. Management Science, 38 (7), 926–947.
35.Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). Neural networks in business: A survey of applications (1992–1998). Expert Systems with Applications, 17, 51-70.
36.West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27, 1131-1152.
37.Zhang, G., Hu, M. Y., Patuwo, B.E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116, 16-32.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔