Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine

The purpose of this article is to predict impending financial distress of the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) using a wide range of features including accrual accounting variables, cash-based accounting variables, market-based variables, corporate governance mechanisms, and macroeconomic indicators. 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 Support Vector Machine (SVM) model achieved a f1-score, MCC, recall and precision of respectively, 55%, 56%, 78% and 43% 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 50%, 50%, 68% and 40%, respectively.