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Good Corporate Governance and Predicting Financial Distress Using Logistic and Probit Regression Model

机译:良好的公司治理和使用Logistic和probit回归模型预测财务困境

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摘要

The study aims to prove whether GCG is able to predict the probability of companies experiencing financial difficulties. Financial ratios that traditionally used for predicting bankruptcy remains used in this study. Besides, this study also compare logit and probit regression models, which are widely used in research related accounting bankruptcy prediction. Both models will be compared to determine which model is more superior. The sample in this study is the infrastructure, transportation, utilities trade, services and hotels companies experiencing financial distress in the period 2008-2011. The results show that GCG and other three variables control i.e DTA, CR and company category do not proven significantly predict the probability of companies experiencing financial difficulties. NPM, the only variable that proved significantly distinguishing healthy firms and distress. In general, logit and probit models do not result in different conclusions, both of the model confirm the goodness of fit of models and the results of hypothesis testing. In terms of classification accuracy, logit model proves more accurate predictions than the probit models.
机译:该研究旨在证明GCG是否能够预测公司遇到财务困难的可能性。传统上用于预测破产的财务比率在本研究中仍然使用。此外,本研究还比较了logit和probit回归模型,这些模型被广泛用于研究与会计破产预测相关的研究中。将比较这两个模型以确定哪个模型更好。本研究的样本是在2008年至2011年期间遭受财务困扰的基础设施,运输,公用事业贸易,服务和酒店公司。结果表明,没有证明GCG和其他三个变量控制DTA,CR和公司类别显着预测公司遇到财务困难的可能性。 NPM是唯一被证明可以明显区分健康公司和困境的变量。通常,对数模型和概率模型不会得出不同的结论,这两个模型都证实了模型拟合的良好性和假设检验的结果。就分类准确性而言,logit模型比概率模型具有更准确的预测。

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    Juniarti;

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  • 年度 2012
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