Corporate Distress Prediction Models Using Governance and Financial Variables: Evidence from Thai Listed Firms during the East Asian Economic Crisis

Predicting corporate distress can have a significant impact on the economy because it serves as an efficient early warning signal. This study develops distress prediction models incorporating both governance and financial variables and examines the impact of major corporate governance attributes, i.e., ownership and board structures, on the likelihood of distress. The two widely documented methods, i.e., logit and neural network approaches are used. For an emerging market economy where ownership concentration is common, we show that not only financial factors but also corporate governance factors help determine the likelihood that a company will be in distress. Our prediction models perform relatively well. Specifically, in our logit models that incorporate governance and financial variables, more than 85% of non-financial listed firms are correctly classified in our models. When we consider the Type I error, on average the models have the Type I error of about 9%. Likewise, the neural network prediction models appear to have good results. Specifically, the average accuracy of the neural network prediction models ranges from approximately 84% to 87% with the average Type I error raging from about 10% to 16%. Such evidence indicates that the models serve as sound early warning signals and could thus be useful tools adding to supervisory resources. We also find that the presence of controlling shareholders and the board involvement by controlling shareholders reduce the probability of corporate financial distress. This evidence supports the monitoring/alignment hypothesis. Finally, our results suggest evidence of the benefits of business group affiliation in reducing the distress likelihood of member firms during the East Asian financial crisis.