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Namazi, M., Ebrahimi, S. (2021). Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine. Glory of Art (Jelve-y Honar) Alzahra Scientific Quarterly Journal, 9(1), 115-132. doi: 10.22051/jfm.2020.25973.2077
Mohammad Namazi; Shahla Ebrahimi. "Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine". Glory of Art (Jelve-y Honar) Alzahra Scientific Quarterly Journal, 9, 1, 2021, 115-132. doi: 10.22051/jfm.2020.25973.2077
Namazi, M., Ebrahimi, S. (2021). 'Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine', Glory of Art (Jelve-y Honar) Alzahra Scientific Quarterly Journal, 9(1), pp. 115-132. doi: 10.22051/jfm.2020.25973.2077
Namazi, M., Ebrahimi, S. Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine. Glory of Art (Jelve-y Honar) Alzahra Scientific Quarterly Journal, 2021; 9(1): 115-132. doi: 10.22051/jfm.2020.25973.2077

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

راهبرد مدیریت مالی
Article 6, Volume 9, Issue 1 - Serial Number 32, April 2021, Pages 115-132    PDF (4.61 M)
Document Type: Research Paper
DOI: 10.22051/jfm.2020.25973.2077
Authors
Mohammad Namazi1; Shahla Ebrahimi* 2
1Professor of Accounting, Shiraz University .Shiraz, Iran.
2PhD Student in Accounting, Shiraz University, Shiraz, Iran.
Abstract
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 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 f1-score, MCC, recall and precision of 50%, 50%, 68% and 40%, respectively.
Keywords
Financial Distress Prediction; Support Vector Machine (SVM); Machine Learning; Data Mining; Tehran Stock Exchange
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