标题: | 预测公司破产事件之研究 On Bankruptcy Prediction |
作者: | 黄瑞卿 Ruey-Ching Hwang 李昭胜 Jack C. Lee 管理科学系所 |
关键字: | 个案控制资料;离散型幸存模型;区别分析模型;KMV-Merton模型;线性罗吉特模型;追踪性资料;半母数罗吉特模型;型I 误差率;型II 误差率;case-control data;discrete-time survival model;discriminant analysis model;KMV-Merton model;linear logit model;prospective data;semiparametric logit model;type I error rate;type II error rate |
公开日期: | 2006 |
摘要: | 本文使用半母数罗吉特模型(semiparametric logit model)建立一个公司破产事件的预测方法,并将之应用在追踪性(prospective)或称简单随机(simple random)资料,以及个案控制(case-control)或称选择性(choice-based)资料。我们使用区域概似方法(local likelihood approach)估计半母数罗吉特模型中未知参数,且研究这些估计式的渐近偏差量与变异数(asymptotic bias and variance)。我们证明当应用这个半母数罗吉特模型至前述两种不同类型资料上,其所对应的破产预测方法是相同的。因此我们的预测方法可以直接应用到这两种重要类型的资料。实证研究结果显示,我们的预测方法较Altman (1968)的区别分析模型(discriminant analysis model)、Ohlson(1980)的线性罗吉特模型(linear logit model)、以及Merton (1974)与Bharath and Shumway (2004) 的KMV-Merton模型等所建立的预测方法,能够产生较小的样本外误差率(out-of-sample error rate)。 另外,本文使用离散型幸存模型(discrete-time survival model; Allison, 1982),预测公司发生财务危机的机率。我们以最大概似法(maximum likelihood method)估计该模型的参数值,导出参数估计式的渐近常态分配(asymptotic normal distribution),进而估计公司发生财务危机的机率。藉由此机率估计值,我们可建立财务危机预警模型,并用以分析及预测台湾股票上市公司发生财务危机的机率。实证研究结果显示,本文所介绍的离散型幸存模型对公司财务危机的预测,比线性罗吉特模型,有更好的样本外预测能力。 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). |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009131803 http://hdl.handle.net/11536/56756 |
显示于类别: | Thesis |
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