政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/36926

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/36926

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题名:	多期邏輯斯迴歸模型應用在企業財務危機預測之研究 Forecasting corporate financial distress：using multi-period logistic regression model
作者:	卜志豪 Pu, Chih-Hao
贡献者:	翁久幸 Weng,Ruby Chiu-Hsing 卜志豪 Pu, Chih-Hao
关键词:	離散型風險模型多期邏輯斯迴歸模型財務危機存活分析 discrete-time hazard model multi-period logistic regression model financial distress survival analysis
日期:	2008
上传时间:	2009-09-18 20:10:30 (UTC+8)
摘要:	本研究延續Shumway (2001) 從存活分析(Survival Analysis)觀點切入，利用離散型風險模型(Discrete-time Hazard Model)──亦即Shumway 所稱之多期邏輯斯迴歸模型(Multi-period Logistic Regression Model)，建立企業財務危機預警模型。研究選取1986 年至2008 年間718 家上市公司，其中110 家發生財務危機事件，共計6,782 公司/年資料 (firm-year)。有別於Shumway 提出的Log 基期風險型式，本文根據事件發生率圖提出Quadratic 基期風險型式，接著利用4組（或基於會計測量，或基於市場測量）時間相依共變量 (Time-dependent Covariate)建立2 組離散型風險模型(Log 與Quadratic)，並與傳統僅考量單期資料的邏輯斯迴歸模型比較。實證結果顯示，離散型風險模型的解釋變數與破產機率皆符合預期關係，而傳統邏輯斯迴歸模型則有時會出現不符合預期關係的情況；研究亦顯示離散型風險模型預測能力絕大多數情況下優於傳統邏輯斯迴歸模型，在所有模型組合中，以Quadratic 基期風險型式搭配財務變數、市場變數的解釋變數組合而成的離散型風險模型，擁有最佳預測能力。 <br>Based on the viewpoint of survival analysis from Shumway (2001), the presentthesis utilizes discrete-time hazard model, also called multi-period logistic regression model, to forecast corporate financial distress. From 1986 to 2008, this research chooses 718 listed companies within, which includes 110 failures, as the subjects, summing to 6,782 firm-year data. Being different from Shumway’s log baseline hazard form,we proposed to use quadratic baseline hazard form according to empirical evidence. Then, four groups of time-dependent covariates, which are accounting-based measure or market-based measure, are applied to build two sets of discrete-time hazard model, which is compared with the single-period logistic regression model. The results show that there exists the expected relationship between covariates and predict probability in discrete-time hazard model, while there sometimes lacks it in single-period logistic regression model. The results also show that discrete-time hazard model has better predictive capability than single-period logistic regression model. The model, which combines quadratic baseline hazard form with market and accounting variables, has the best predictive capability among all models.
參考文獻:	Allison, P. D. (1982). Discrete-time methods for the analysis of event histories. Sociological methodology , 12, pp. 61-98. Altman, E. (1968). Financial ratios, discriminant analysis, and the prediction of corporate. Journal of Finance , 23, pp. 589-609. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research , 4, Supplement, pp. 71-111. Begley, J., Ming, J., & Watts, S. (1996). Bankruptcy Classification Errors in the 1980s: An Empirical Analysis of Altman`s and Ohlson`s Models. Review of Accounting Studies , 1, pp. 267-284. Brown, C. C. (1975). On the use of indicator variables for studying the time-dependence of parameters in a response-time model. Biometrics , 31, pp. 863-872. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B , 34, pp. 187-202. Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics , 83, pp. 635-665. Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies , 9, pp. 5-34. Laird, N., & Oliver, D. (1981). Covariance analysis of censored survival data using log-linear analysis techniques. Journal of the American Statistical Association , 76, pp. 231-240. Lane, W. R., Looney, S. W., & Wansley, J. W. (1986). An application of the cox proportional hazards model to bank failure. Journal of Banking and Finance , 10, pp. 511-531. Lawless, J. F. (2003). Statistical Model and Methods for Lifetime Data. New York: John Wiley & Sons. Lee, S. H., & Urrutia, J. L. (1996). Analysis and prediction of insolvency in the property-liability insurance industry: A comparison of logit and hazard models. The Journal of Risk and Insurance , 63, pp. 121-130. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research , 19, pp. 109-131. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business , 74, pp. 101-124. Singer, J. D., & Willett, J. B. (1993). 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. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. New York: 48 Oxford University Press. Tutz, G., & Pritscher, L. (1996). Nonparametric estimation of discrete hazard functions. Lifetime Data Analysis , 2, pp. 291-308. Wheelock, D. C., & Wilson, P. W. (2000). Why do banks disappear? The determinants of US bank failures and acquisitions. Review of Economics and Statistics , 82, pp. 127–138. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress. Journal of Accounting Research , 22, pp. 59-82. 林妙宜 (2002)，公司信用風險之衡量，國立政治大學金融研究所碩士論文徐美珍 (2004)，企業財務危機之預測，國立政治大學統計學系碩士論文莊鎮嶽 (2004)，財務比率建立財務危機預警模型之實證研究－合併財務報表與母公司財務報表之比較，國立臺灣大學會計學研究所碩士論文蘇心盈 (2004)，以財務比率預測未來盈餘及股價異常報酬之研究－比較母公司財務報表與合併財務報表，國立政治大學會計學研究所碩士論文蘇敏賢 (2000)，合併財務報表、母公司財務報表之比較及其與企業風險之關聯性，國立臺灣大學會計學研究所碩士論文
描述:	碩士國立政治大學統計研究所 95354019 97
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0095354019
数据类型:	thesis
显示于类别:	[統計學系] 學位論文

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