Corporate financial distress prediction in a transition economy

Forecasting financial distress of corporations is a difficult task in economies undergoing transition, as data are scarce and are highly imbalanced. This research tackles these difficulties by gathering reliable financial distress data in the context of a transition economy and employing the synthetic minority oversampling technique (SMOTE). The study employs seven different models, including linear discriminant analysis (LDA), logistic regression (LR), support vector machines (SVMs), neural networks (NNs), decision trees (DTs), random forests (RFs), and the Merton model, to predict financial distress among publicly traded companies in Vietnam between 2011 and 2021. The first six models use accounting-based variables, while the Merton model utilizes market-based variables. The findings indicate that while all models perform fairly well in predicting results for nondelisted firms, they perform somewhat poorly in predicting results for delisted firms in terms of various measures including balanced accuracy, Matthews correlation coefficient (MCC), precision, recall, and $F_1$ score. The study shows that the models that incorporate both Altman's and Ohlson's variables consistently outperform those that only use Altman's or Ohlson's variables in terms of balanced accuracy. Additionally, the study finds that NNs are generally the most effective models in terms of both balanced accuracy and MCC. The most important variable in Altman's variables as well as the combination of Altman's and Ohlson's variables is “reat” (retained earnings on total assets), whereas “ltat” (total liabilities on total assets) and “wcapat” (working capital on total assets) are the most important variables in Ohlson's variables. The study also reveals that in most cases, the models perform better in predicting results for big firms than for small firms and typically better than in good years than for bad years.