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研究生:黃瑞卿
研究生(外文):Ruey-Ching Hwang
論文名稱:預測公司破產事件之研究
論文名稱(外文):On Bankruptcy Prediction
指導教授:李昭勝李昭勝引用關係
指導教授(外文):Jack C. Lee
學位類別:博士
校院名稱:國立交通大學
系所名稱:管理科學系所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2006
畢業學年度:95
語文別:英文
論文頁數:69
中文關鍵詞:個案控制資料離散型倖存模型區別分析模型KMV-Merton模型線性羅吉特模型追蹤性資料半母數羅吉特模型型I 誤差率型II 誤差率
外文關鍵詞:case-control datadiscrete-time survival modeldiscriminant analysis modelKMV-Merton modellinear logit modelprospective datasemiparametric logit modeltype I error ratetype II error rate
相關次數:
  • 被引用被引用:0
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  • 下載下載:133
  • 收藏至我的研究室書目清單書目收藏:7
本文使用半母數羅吉特模型(semiparametric logit model)建立一個公司破產事件的預測方法,並將之應用在追蹤性(prospective)或稱簡單隨機(simple random)資料,以及個案控制(case-control)或稱選擇性(choice-based)資料。我們使用區域概似方法(local likelihood approach)估計半母數羅吉特模型中未知參數,且研究這些估計式的漸近偏差量與變異數(asymptotic bias and variance)。我們證明當應用這個半母數羅吉特模型至前述兩種不同類型資料上,其所對應的破產預測方法是相同的。因此我們的預測方法可以直接應用到這兩種重要類型的資料。實證研究結果顯示,我們的預測方法較Altman (1968)的區別分析模型(discriminant analysis model)、Ohlson(1980)的線性羅吉特模型(linear logit model)、以及Merton (1974)與Bharath and Shumway (2004) 的KMV-Merton模型等所建立的預測方法,能夠產生較小的樣本外誤差率(out-of-sample error rate)。
另外,本文使用離散型倖存模型(discrete-time survival model; Allison, 1982),預測公司發生財務危機的機率。我們以最大概似法(maximum likelihood method)估計該模型的參數值,導出參數估計式的漸近常態分配(asymptotic normal distribution),進而估計公司發生財務危機的機率。藉由此機率估計值,我們可建立財務危機預警模型,並用以分析及預測台灣股票上市公司發生財務危機的機率。實證研究結果顯示,本文所介紹的離散型倖存模型對公司財務危機的預測,比線性羅吉特模型,有更好的樣本外預測能力。
Bankruptcy prediction methods based on a semiparametric logit model are proposed for prospective (simple random) and case-control (choice-based) data. The unknown quantities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. Our semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. Empirical studies demonstrate that our prediction method is more powerful than alternatives based on the discriminant analysis model (Altman 1968), the linear logit model (Ohlson 1980), and the KMV-Merton model (Merton 1974; Bharath and Shumway 2004), in the sense of yielding smaller out-of-sample error rates.
The discrete-time survival model (Allison 1982) is applied to predict the probability of financial distress. The maximum likelihood method is employed to estimate the values of parameters in the model. The resulting estimates are analyzed by their asymptotic normal distributions, and are used to estimate the probability of financial distress for each firm under study. Using such estimated probability, a strategy is developed to identify failing firms, and is applied to study the probability of financial distress for firms listed in Taiwan Stock Exchange. Empirical studies demonstrate that our strategy developed from the discrete-time survival model can yield more accurate out-of-sample forecasts than the alternative method based on the linear logit model in Ohlson (1980).
TABLE OF CONTENTS
ABSTRACT ( IN CHINESE) i
ABSTRACT ii
ACKNOWLEDGEMENTS iv
TABLE OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER I: INTRODUCTION TO BANKRUPTCY PREDICTION METHODS 1
1.1 Introduction 1
1.2 Three Sampling Schemes 4
1.3 The LLM 6
1.4 The SLM 9
1.5 The KMV 14
1.6 The DAM 17
1.7 The DSM 18
1.8 Bankruptcy Prediction Devices 22
1.9 Summary of Results 25
CHAPTER II: SEMIPARAMETRIC BANKRUPTCY PREDICTION METHODS 27
2.1 Introduction 27
2.2 Theoretical Results 29
2.3 A Real Data Example 33
2.4 A Simulation Study 41
2.5 Discussion 44
2.6 Sketches of the Proofs 45
CHAPTER III: DYNAMIC PREDICTION METHODS FOR BANKRUPTCY AND FINANCIAL DISTRESS 50
3.1 Introduction 50
3.2 Theoretical Results 51
3.3 A Real Data Example 52
3.4 Discussion 60
CHAPTER IV: CONCLUSIONS 62
BIBLIOGRAPHY 66
1. Allison, P. D., “Discrete-time methods for the analysis of event histories,” Sociological Methodology, 13, pp. 61-98, 1982.
2. Altman, E. I., “Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy,” Journal of Finance, 23, pp. 589-609, 1968.
3. Begley, J., Ming, J., and Watts, S., “Bankruptcy classification errors in the 1980s: An empirical analysis of Altman’s and Ohlson’s models,” Review of Accounting Studies, 1, pp. 267-284, 1996.
4. Bharath, S., and Shumway, T., “Forecasting default with the KMV-Merton model,” manuscript, University of Michigan, 2004.
5. Chava, S. and Jarrow, R. A., “Bankruptcy prediction with industry effects,” Review of Finance, 8, pp. 537-569, 2004.
6. Cox, D. R. and Oakes, D., Analysis of Survival Data, Chapman, New York, 1984.
7. Crosbie, P. J. and Bohn, J. R., “Modeling Default Risk,” KMV Corporation, http://www.kmv.com, 2001.
8. Fan, J., Gasser, T., Gijbels, I., Brookmann, M., and Engel, M., “Local polynomial fitting: A standard for nonparametric regression,” Discussion paper 9315, Institut de Statistique, Universite Catholique de Louvain, Belgium, 1993.
9. Fan, J., Heckman, N. E., and Wand, M. P., “Local polynomial kernel regression for generalized linear models and quasi-likelihood functions,” Journal of the American Statistical Association, 90, pp. 141-150, 1995.
10. Fernandez, C. and Steel, M. F. J., “On Bayesian modeling of fat tails and skewness,” Journal of the American Statistical Association, 93, pp. 359-371, 1998.
11. Frydman, H., Altman, E. I., and Kao, D. L., “Introducing recursive partitioning for financial classification: the case of financial distress,” Journal of Finance, 40, pp.
269-291, 1985.
12. Grice, J. S. and Dugan, M. T., “The limitations of bankruptcy prediction models: Some cautions for the research,” Review of Quantitative Finance and Accounting, 17, pp. 151-166, 2001.
13. Härdle W., Moro R. A, and Schäfer D., “Graphical data representation in bankruptcy analysis,” in Handbook of Computational Statistics, Härdle, W. (ed), Springer, Berlin, 2006.
14. Hastie, T. and Tibshirani, R., Generalized Additive Models, Chapman and Hall, London, 1990, .
15. Hosmer, D. J. and Lemeshow, S., Applied Logistic Regression, Wiley, NewYork, 1989.
16. Johnson, R. A. and Wichern, D. W., Applied Multivariate Statistical Analysis, Prentice Hall, New York, 2002.
17. Jones, M. C., Marron, J. S., and Sheather, S. J., “A brief survey of bandwidth selection for density estimation,” Journal of the American Statistical Association, 91, pp. 401-407, 1996.
18. Klein, J. P. and Moeschberger M. L., Survival Analysis: Techniques for Censored and Truncated Data, Springer, New York, 1997.
19. Koh, H. C. and Tan, S. S., “A neural network approach to the prediction of going concern status,” Accounting and Business Research, 29, pp. 211-216, 1999.
20. Lancaster, T., The Econometric Analysis of Transition Data, Cambridge University Press, New York, 1990.
21. Lane, W., Looney, S., and Wansley, J., “An application of the cox proportional hazards model to bank failure,” Journal of Banking and Finance, 10, pp. 511-531, 1986.
22. Lejeune, M. and Sarda, P., “Smooth estimators of distribution and density functions,” Computational Statistics and Data Analysis, 14, pp. 457-471, 1992.
23. Lindsay, D. H., and Campbell, A., “A chaos approach to bankruptcy prediction,” Journal of Applied Business Research, 12, pp. 1-9, 1996.
24. Little, R. J. A. and Rubin, D. B. Statistical Analysis with Missing Data, Wiley, New York, 2002.
25. Mckee, T. E., “Rough sets bankruptcy prediction models versus auditor signaling rates,” Journal of Forecasting, 22, pp. 569-586, 2003.
26. Messier, J. W. and Hansen, J. V., “Inducing rules for expert system development: an example using default and bankruptcy data,” Management Science, 34, pp. 1403-1415, 1988.
27. Merton, R. C., “On the pricing of corporate debt: The risk structure of interest rates,” Journal of Finance, 29, pp. 449-470, 1974.
28. Ohlson, J., “Financial ratios and the probabilistic prediction of bankruptcy,” Journal of Accounting Research, 18, pp. 109-131, 1980.
29. Pagano, M., Panetta, F., and Zingales, L., “Why do companies go public? An empirical analysis,” Journal of Finance, 53, pp. 27-64, 1998.
30. Prentice, R. L. and Pyke, R., “Logistic disease incidence models and case-control studies,” Biometrika, 66, pp. 403-411, 1979.
31. Serfling, R. J., Approximation Theorems of Mathematical Statistics, Wiley, NewYork, 1980.
32. Shumway, T., “Forecasting bankruptcy more accurately: a simple hazard model,” Journal of Business, 74, pp. 101-124, 2001.
33. Siegrist, K., “The Pareto distribution,” http://www.ds.unifi.it/ VL/VL_EN/
special/special12.html [3 February 2006], 2005.
34. Singer, J. D. and Willett, J. B., “It’s about time: using discrete-time survival analysis to study duration and the timing of events,” Journal of Educational Statistics, 18, pp. 155-195, 1993.
35. Stuart, A. and Ord, J. K., Kendall’s Advanced Theory of Statistics, Volume 1, Oxford University Press, New York, 1987.
36. Tibshirani, R., and Hastie, T., “Local likelihood estimation,” Journal of the American Statistical Association, 82, pp. 559-568, 1987.
37. Vassalou, M. and Xing, Y., “Default risk in equity returns,” Journal of Finance, 59, pp. 831-868, 2004.
38. Wand, M. P., and Jones, M. C., Kernel Smoothing, Chapman, London, 1995.
39. Zhao, L. P., Kristal, A. R., and White, E., “Estimating relative risk functions in case-control studies using a nonparametric logistic regression,” American Journal of
Epidemiology, 144, pp. 598-609, 1996.
40. Zmijewski, M. E., “Methodological issues related to the estimation of financial distress prediction models,” Journal of Accounting Research, 22, pp. 59-82, 1984.
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