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

Abstract

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_x000D_ incorporating both governance and financial variables and examines the impact of major_x000D_ corporate governance attributes, i.e., ownership and board structures, on the likelihood of_x000D_ distress. The two widely documented methods, i.e., logit and neural network approaches are_x000D_ used. For an emerging market economy where ownership concentration is common, we show_x000D_ that not only financial factors but also corporate governance factors help determine the_x000D_ likelihood that a company will be in distress. Our prediction models perform relatively well._x000D_ 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_x000D_ consider the Type I error, on average the models have the Type I error of about 9%. Likewise,_x000D_ the neural network prediction models appear to have good results. Specifically, the average_x000D_ accuracy of the neural network prediction models ranges from approximately 84% to 87%_x000D_ with the average Type I error raging from about 10% to 16%. Such evidence indicates that the_x000D_ models serve as sound early warning signals and could thus be useful tools adding to_x000D_ supervisory resources. We also find that the presence of controlling shareholders and the_x000D_ board involvement by controlling shareholders reduce the probability of corporate financial_x000D_ distress. This evidence supports the monitoring/alignment hypothesis. Finally, our results suggest evidence of the benefits of business group affiliation in reducing the distress_x000D_ likelihood of member firms during the East Asian financial crisis.