標題: Financial Distress Prediction Using Discrete-time Hazard Model and Rating Transition Matrix Approach
作者: Tsai, Bi-Huei
Chang, Chih-Huei
管理科學系
Department of Management Science
關鍵字: Discrete-time hazard model;Probit-AR(1)-GARCH(1,1) model;Nonlinear regression;Financial distress;Statistics;Type I error;Maximum likelihood function;Rating transition matrix;Credit cycle index;Social Science
公開日期: 2009
摘要: Previous studies used constant cut-off indicator to distinguish distressed firms from non-distressed ones in the one-stage prediction models. However, distressed cut-off indicator must shift according to economic prosperity, rather than remains fixed all the time. This study focuses on Taiwanese listed firms and develops financial distress prediction models based upon the two-stage method. First, this study employs the firm-specific financial ratio and market factors to measure the probability of financial distress based on the discrete-time hazard models. Second, this paper further focuses on macroeconomic factors and applies rating transition matrix approach to determine the distressed cut-off indicator. The prediction models are developed by using the training sample from 1987 to 2004, and their levels of accuracy are compared with the test sample from 2005 to 2007. As for the one-stage prediction model, the model in incorporation with macroeconomic factors does not perform better than that without macroeconomic factors. This suggests that the accuracy is not improved for one-stage models which pool the firm-specific and macroeconomic factors together. In regards to the two stage models, the negative credit cycle index implies the worse economic status during the test period, so the distressed cut-off point is adjusted to increase based on such negative credit cycle index. After the two-stage models employ such adjusted cut-off point to discriminate the distressed firms from non-distressed ones, their error of misclassification becomes lower than that of one-stage ones. The two-stage models presented in this paper have incremental usefulness in predicting financial distress.
URI: http://hdl.handle.net/11536/13501
ISBN: 978-0-7354-0685-8
ISSN: 0094-243X
期刊: COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING, VOL 2: ADVANCES IN COMPUTATIONAL SCIENCE
Volume: 1148
起始頁: 467
結束頁: 470
顯示於類別:會議論文