Browsing by Author "Hwang, Ruey-Ching"

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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.
A Two-Stage Probit Model for Predicting Recovery Rates
(2016-12)
Hwang, Ruey-Ching
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Chung, Huimin
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Chu, C. K.
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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
We propose a two-stage probit model (TPM) to predict recovery rates. By the ordinal nature of the three categories of recovery rates: total loss, total recovery, and lying between the two extremes, we first use the ordered probit model to predict the category that a given debt belongs to among the three ones. Then, for the debt that is classified as lying between the two extremes, we use the probit transformation regression to predict its recovery rate. We use real data sets to support TPM. Our empirical results show that macroeconomic-, debt-, firm-, and industry-specific variables are all important in determining recovery rates. Using an expanding rolling window approach, our empirical results confirm that TPM has better and more robust out-of-sample performance than its alternatives, in the sense of yielding more accurate predicted recovery rates.
Assessing bankruptcy prediction models via information content of technical inefficiency
(2011-12-01)
Hwang, Ruey-Ching
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Siao, Jhao-Siang
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Chung, Huimin
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Chu, C. K.
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資訊管理與財務金融系註：原資管所+財金所
We use a stochastic frontier model with firm-specific technical inefficiency effects in a panel framework (Battese and Coelli in Empir Econ 20:325-332, 1995) to assess two popular probability of bankruptcy (PB) measures based on Merton model (Merton in J Financ 29:449-470, 1974) and discrete-time hazard model (DHM; Shumway in J Bus 74:101-124, 2001). Three important results based on our empirical studies are obtained. First, a firm with a higher PB generally has less technical efficiency. Second, for an ex-post bankrupt firm, its PB tends to increase and its technical efficiency of production tends to decrease, as the time to its bankruptcy draws near. Finally, the information content about firm's technical inefficiency provided by PB based on DHM is significantly more than that based on Merton model. By the last result and the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagan in Q J Bus Econ 41:27-41, 2002), we conclude that PB based on DHM is a better credit risk proxy of firms.
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.
Predicting issuer credit ratings using a semiparametric method
(2010-01-01)
Hwang, Ruey-Ching
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Chung, Huimin
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Chu, C. K.
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model. (C) 2009 Elsevier B.V. All rights reserved.
Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market
(2013-06-01)
Hwang, Ruey-Ching
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Chung, Huimin
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Ku, Jiun-Yi
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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
The dynamic logit model (DLM) with autocorrelation structure (Liang and Zeger Biometrika 73:13-22, 1986) is proposed as a model for predicting recurrent financial distresses. This model has been applied in many examples to analyze repeated binary data due to its simplicity in computation and formulation. We illustrate the proposed model using three different panel datasets of Taiwan industrial firms. These datasets are based on the well-known predictors in Altman (J Financ 23:589-609, 1968), Campbell et al. (J Financ 62:2899-2939, 2008), and Shumway (J Bus 74:101-124, 2001). To account for the correlations among the observations from the same firm, we consider two different autocorrelation structures: exchangeable and first-order autoregressive (AR1). The prediction models including the DLM with independent structure, the DLM with exchangeable structure, and the DLM with AR1 structure are separately applied to each of these datasets. Using an expanding rolling window approach, the empirical results show that for each of the three datasets, the DLM with AR1 structure yields the most accurate firm-by-firm financial-distress probabilities in out-of-sample analysis among the three models. Thus, it is a useful alternative for studying credit losses in portfolios.