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

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NAMAZI MOHAMMAD; EBRAHIMI SHAHLA

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Journal Paper

Paper Information

Journal: JOURNAL OF FINANCIAL MANAGEMENT STRATEGY Year:2021 | Volume:9 | Issue:32 Page(s): 115-132

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Information Journal Paper

Title

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

Author(s)

NAMAZI MOHAMMAD | EBRAHIMI SHAHLA | Issue Writer Certificate

Keywords

Financial Distress Prediction

Support Vector Machine (SVM)

Machine Learning

Data Mining

Tehran Stock Exchange

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 costsensitive 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.

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APA: Copy

NAMAZI, MOHAMMAD, & EBRAHIMI, SHAHLA. (2021). Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine. JOURNAL OF FINANCIAL MANAGEMENT STRATEGY, 9(32 ), 115-132. SID. https://sid.ir/paper/956696/en

Vancouver: Copy

NAMAZI MOHAMMAD, EBRAHIMI SHAHLA. Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine. JOURNAL OF FINANCIAL MANAGEMENT STRATEGY[Internet]. 2021;9(32 ):115-132. Available from: https://sid.ir/paper/956696/en

IEEE: Copy

MOHAMMAD NAMAZI, and SHAHLA EBRAHIMI, “Financial Distress Prediction of the Listed Companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) Using Support Vector Machine,” JOURNAL OF FINANCIAL MANAGEMENT STRATEGY, vol. 9, no. 32 , pp. 115–132, 2021, [Online]. Available: https://sid.ir/paper/956696/en

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