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

Juniarti, (2013) Good Corporate Governance and Predicting Financial Distress Using Logistic and Probit Regression Model. [UNSPECIFIED]

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Abstract

The study aims to prove whether good corporate governance (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 compares 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 prove significantly to 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 models 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.

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