在巴塞尔II框架下估计内部信用风险和破产预测的新方法
A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime
AI总结：
本文提出一种新的多变量非线性模型计算z分数，并开发一种新的信用风险指数，通过拟合Pearson类型III分布到转换的财务比率，提高了破产预测的准确性。
摘要：
信用估计和破产预测方法多年来一直在使用Altman的z分数方法。许多研究报告称z分数对会计数据的变化敏感。研究人员提出了不同的变体来改进预测准确性。本文开发了一种新的多变量非线性模型来计算z分数，并开发了一种新的信用风险指数，通过将Pearson类型III分布拟合到转换的财务比率。研究结果表明，新z分数的破产预测准确率为98.6%，优于Altman的z分数的93.5%。此外，判别分析显示，新转换的财务比率能以93.0%的准确率预测破产概率，优于使用Altman z分数权重的87.4%。
英文摘要：
Credit estimation and bankruptcy prediction methods have been utilizing Altman's $z$ score method for the last several years. It is reported in many studies that $z$ score is sensitive to changes in accounting figures. Researches have proposed different variations to conventional $z$ score that can improve the prediction accuracy. In this paper we develop a new multivariate non-linear model for computing the $z$ score. In addition we develop a new credit risk index by fitting a Pearson type-III distribution to the transformed financial ratios. The results from our study have shown that the new $z$ score can predict the bankruptcy with an accuracy of $98.6\%$ as compared to $93.5\%$ by the Altman's $z$ score. Also, the discriminate analysis revealed that the new transformed financial ratios could predict the bankruptcy probability with an accuracy of $93.0\%$ as compared to $87.4\%$ using the weights of Altman's $z$ score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
