企業財務危機診斷模式之構建

Predicting Financial Business Failures

10.7118/JHSS.201206.0013

葉忠興(Chung-Hsing Yeh)

資料探勘 ； 企業財務危機 ； 資料包絡分析 ； 約略集合 ； 支援向量機 ； data mining ； financial distress ； data envelopment analysis ； rough set ； support vector machine

人文暨社會科學期刊

8卷1期（2012 / 06 / 01）

13 - 21

繁體中文

近年來，由於企業環境經營巨變，造成整體經濟所面臨的狀況更加艱鉅，而企業財務危機發生的可能性亦隨之提升。因此，建立一個有效的財務危機診斷模式，是當前學術界與實務界的一個重要課題，本研究整合資料探勘與資料包絡分析建構模式方法，建構企業危機診斷分類能力。此外，在探討企業危機的衡量指標上，本研究除了參考一般傳統財務性指標外，亦加入了經營績效指標，希望能藉由更完整多元的企業資訊，來幫助企業本身評估其自身的真實價值，並做出正確的決策。本研究經由約略集合理論針對所考量之衡量企業財務危機指標進行分析，得知企業發生財務危機的原因，除了受到傳統財務構面指標的影響外亦受到經營績效指標的影響。此外，整合資料探勘技術與經營績效指標所建構之企業財務危機診斷模式亦能確實有效降低企業財務危機診斷誤判的情況，是以無論在學術研究或實際工作上，實有其相當的助益。

Over the last few years, rapid changes in the global economic environment have increased the possibility of financial failures occurring. Therefore, constructing an appropriate financial distress diagnosis model has become a crucial task for the industry. The objective of this study is to investigate enterprise financial distress by integrating data mining with a data envelopment analysis (DEA) indicator. In addition to a financial indicator, the DEA indicator is also included in the model. The results indicate that the combined approach proposed in this study enables greater prediction accuracy and convergence speed compared to that of conventional data mining. Additionally, we discovered that the accurate diagnosis of enterprise financial distress is significantly influenced by both traditional financial indicators and the DEA indicator.

1. 蔡碧徽、黃鈺萍(2010)。財務危機預測模型之比較分析。當代會計，11(1)，51-78。
   連結：
2. Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines. Retrieved Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
3. Altman, E. I.(1983).Corporate financial distress: A complete guide to predicting, avoiding and dealing with bankruptcy.New York:John Wiley and Sons.
4. Altman, E. I.(1968).Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.Journal of Finance,36(1),589-609.
5. Banker, R. D.,Chanes, A.,Cooper, W. W.(1984).Some models for estimating technical and scale inefficiencies in data envelopment analysis.Management Science,30(9),1078-1092.
6. Beaver, W. H.(1966).Financial ratios as predictors of failure.Journal of Accounting Research,12(1),1-25.
7. Belkaoi, A.(1980).Industrial bond ratings: A new look.Financial Management, Autumn,9(3),44-51.
8. Bose, I.(2006).Deciding the financial health of dot-coms using rough sets.Information and Management,43(7),835-846.
9. Boussofiane, A.,Dyson, G.,Thanassoulis, E.(1991).Applied data envelopment analysis.European Journal of Operational Research,52,1-15.
10. Charnes, A.,Cooper, W. W.,Rhodes, E.(1978).Measuring the efficiency of decision making units.European Journal of Operational Research,1(2),429-444.
11. Ederington, L. H.(1985).Classification models and bond ratings.The Financial Review,20(4),237-262.
12. Farrell, M.(1957).The measurement of productive efficiency.Journal of the Royal Statistical Society General,120(3),253-281.
13. Gestel, T. V.,Baesens, B.,Suykens, J. A. K.,Van den Poel, D.,Baestaens, D. E.,Willekens, M.(2006).Bayesian kernel based classificationi for financial distress detection.European Journal of Operational Ressearch,172,979-1003.
14. Jostarndt, P.,Sautner, Z.(2008).Financial distress, corporate control, and management turnover.Journal of Banking & Finance,32(10),2188-2204.
15. Kim, K.(2003).Financial time series forecasting using support vector machines.Neurocomputing,55,307-319.
16. Koh, H.(1991).Model predictions and auditor assessments of going concern status.Accounting and Business Research,21(84),331-338.
17. Lau, A. H. L.(1987).A five-state financial distress prediction model.Journal of Accounting Research,25(1),127-138.
18. Martin, D.(1977).Early warning of banking failure.Journal of Banking and Finance,1(3),249-276.
19. Minoux, M.(1986).Mathematical programming: Theory and algorithms.New York:Wiley-Chichester.
20. Ohlson, J. A.(1980).Financial ratios and the probabilistic prediction of bankuptcy.Journal of Accounting Research,18(1),109-131.
21. Pawlak, Z.(1982).Rough sets.International Journal of Parallel Programming,11(5),341-356.
22. Pinches, G.,Mingo, K.(1975).The role of xubordination and industrial bond ratings.Journal of Finance,3,201-206.
23. Shin, K. S.,Lee, T. S.,Kim, H. J.(2005).An application of support vector machines in bankruptcy prediction model.Expert Systems with Applications,28(1),127-135.
24. Vapnik, V.(1995).The nature of statistical learning theory.New York:Springer-Verlag.
25. Xu, X.,Wang, Y.(2009).Financial failure prediction using efficiency as a preditor.Expert Systems with Applications,36(1),366-373.
26. Zmijewski, M. E.(1984).Methodological issues related to the estimation distress prediction model.Journal of Accounting Research,22,59-82.
27. 陳莉莉(1994)。新竹，國立交通大學管理科學研究所。
28. 陳肇榮(1982)。碩士論文(碩士論文)。台北，國立台灣大學商學所。

1. 林芳利(2016)。公司經營危機預警模型‐以財務比率與公司治理指標為例。臺中科技大學企業管理系碩士班學位論文。2016。1-66。