The purpose of this article is the detection of potential financial distress and early warnings of impending financial distress among 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, a cost-sensitive meta-learning approach 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 LASSO logistic model achieved a f1-score, MCC, recall and precision of respectively, 50%, 50%, 73% and 38% 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 51%, 51%, 73% and 39%, respectively.