Browsing by Author "Cheng, K. F."

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A semiparametric method for predicting bankruptcy
(2007-08-01)
Hwang, Ruey-Ching
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Cheng, K. F.
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Lee, Jack C.
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管理科學系
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資訊管理與財務金融系註：原資管所+財金所
Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The 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. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright (C) 2007 John Wiley & Sons, Ltd.
On multiple-class prediction of issuer credit ratings
(2009-09-01)
Hwang, Ruey-Ching
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Cheng, K. F.
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Lee, Cheng-Few
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
For multiple-class prediction, a frequently used approach is based on ordered probit model. We show that this approach is not optimal in the sense that it is not designed to minimize the error rate of the prediction. Based upon the works by Altman (J. Finance 1968; 23:589-609), Ohlson (J. Accounting Res. 1980; 18:109-131), and Begley et al. (Ret Accounting Stud. 1996, 1:267-284) on two-class prediction, we propose a modified ordered probit model. The modified approach depends on an optimal cutoff value and can be easily applied in applications. An empirical study is used to demonstrate that the prediction accuracy rate of the modified classifier is better than that obtained from usual ordered probit model. In addition, we also show that not only the usual accounting variables are useful for predicting issuer credit ratings, market-driven variables and industry effects are also important determinants. Copyright (C) 2008 John Wiley & Sons, Ltd.