The purpose of this article is to predict the potential financial distress of the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB). To do so, a wide range of features including accrual accounting variables, cash-based accounting variables, market-based variables, corporate governance mechanisms, and macroeconomic indicators have been identified to prospectively predict the financial distress in the companies.
The final sample includes 421 firms leading to 3,670 firm-year observations. The prepared data, was then split into a train and test data set using a 70/30 ratio.
In this research, various data pre-processing machine learning techniques i.e., Z-score standardization, one-hot encoding, stratified K-fold validation combined with feature engineering are applied to improve classifier performance. Stratified K-fold cross validation method, (with k = 5) was used for estimation of model prediction performance during training phase. During the training phase, hyper-parameter tuning of a model was carried out using a grid-search. Furthermore, SMOTE technique in conjunction with the proposed imbalance-oriented metric i.e., F1 score were used to overcome the extreme class imbalance issue.
Based on the experimental results, the tuned XGBoost model achieved a f1-score, MCC, recall and precision of respectively, 90%, 90%, 100% and 82% on the training set. Finally, the proposed model was tested on the hold-out test set which resulted in a f1-score, MCC, recall and precision of 52%, 52%, 73% and 41%, respectively. This information provides a powerful tool for predicting the financial distress of companies.