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Insolvency Prediction Model Using Multivariate Discriminant Analysis and Aartificial Neural Network for the Ffinance Industry in New Zealand

机译:新西兰金融业的多元判别分析与人工神经网络的破产预测模型

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Models of insolvency are important for managers who may not appreciate how serious the financial health of their company is becoming until it is too late to take effective action. Multivariate discriminant analysis and artificial neural network are utilized in this study to create an insolvency predictive model that could effectively predict any future failure of a finance company and validated in New Zealand . Financial ratios obtained from corporate balance sheets are used as independent variables while failed/non-failed company is the dependent variable. The results indicate the financial ratios of failed companies differ significantly from non-failed companies. Failed companies were also less profitable and less liquid and had higher leverage ratios and lower quality assets.
机译:破产模型对于可能不欣赏其公司财务健康的经济人员在为时已故取得有效行动的情况下,他们的经理对管理人员来说都很重要。本研究中利用多元判别分析和人工神经网络,以创造一种破产预测模型,可以有效地预测金融公司未来失败并在新西兰验证。从公司资产负债表获得的财务比率用作独立变量,而失败/非失败公司是从属变量。结果表明,失败公司的财务比例与非失败公司有显着不同。失败的公司也较少盈利,液体较少,杠杆率较高,质量较低。

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