PREDICTING FINANCIAL DISTRESS USING SUPERVISED MACHINE LEARNING ALGORITHMS: AN APPLICATION ON BORSA ISTANBUL

Year 2023, Volume: 10 Issue: 4, 217 - 223, 31.12.2023

Seyfullah Selimefendigil

Abstract

Purpose- The main purpose of this study is to identify the most significant variables to detect financial distress earlier and to find the best machine learning algorithm model.
Methodology-This study has used Support Vector Machine, Logistic Regression, Random Forest and K-nearest neighbors method techniques to predict the financial distress prediction for the companies of Turkey between 2012 and 2021.
Findings- As a result of the study, it has been determined that Random Forest provides the best results in terms of precision, accuracy, and recall. Further, this study has found the most important five independent variables to determine the financial distress status of the firms. In this way, it has been found that Current Assets/ Current Liabilities, Working Capital / Total Assets, Gross profit / Revenue, Retained Earnings / Total Assets and Sales growth rate are the most useful variables to determine financial distress status of Turkish firms earlier.
Conclusion- This study has concluded that cash ratios and profitability ratios and sales growth are the most important independent variables to determine financial distress one-year ahead. Furthermore, it has been found that random forest is the best machine learning method among other supervised machine learning methods used in this study.

Keywords

Financial distress, support vector machine, logistic regression, random forest, k-nearest neighbors

References

• Aker, Y., & Karavardar, A. (2023). Using machine learning methods in financial distress prediction: sample of small and medium sized enterprises operating in Turkey. Ege Akademik Bakis, 23(2), 145-162
• Aksoy, B., & Boztosun, D. (2018). Financial failure prediction by using discriminant and logistics regression methods: evidence from BIST manufacturing sector. Finans Politik & Ekonomik Yorumlar, 646, 9–32.
• Zhang, L., Altman, E. I. & Yen, J. (2010). Corporate financial distress diagnosis model and application in credit rating for listing firms in China. Frontiers of Computer Science in China, 4, 220-236.
• Altman Edward I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 189–209.
• Oude Avenhuis, J. (2013). Testing the generalizability of the bankruptcy prediction models of Altman, Ohlson and Zmijewski for Dutch listed and large non-listed firms (Master's thesis, University of Twente).
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.
• Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
• DeAngelo, H., & DeAngelo, L. (1990). Dividend policy and financial distress: an empirical investigation of troubled NYSE firms. Journal of Finance, 45(5), 1415-1431.
• Elhoseny, M., Metawa, N., Sztano, G., & El-Hasnony, I. M. (2022). Deep learning-based model for financial distress prediction. Annals of Operations Research, 11(2), 1-23.
• Hill, N. T., Perry, S. E., & Andes, S. (1996). Evaluating firms in financial distress: An event history analysis. Journal of Applied Business Research (JABR), 12(3), 60-71.
• İçerli, M. Y. (2005). Prediction of Financial Failure in Businesses and an Application (PhD thesis, University of Dokuz Eylül)
• Institute of International Finance. (2021). Global Debt Monitor COVID Drives Debt Surge — Stabilization Ahead ?
• Kinay, B. (2010). Ordered Logit Model approach for the determination of financial distress. Numéro spécial, 119-131.
• Li, H., & Sun, J. (2008). Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems, 21(8), 868-878.
• Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102.
• Malik, H., Fatema, N., & Iqbal, A. (2021). Intelligent data-analytics for condition monitoring: smart grid applications. Academic Press.
• Nicodemus, K. K. (2011). On the stability and ranking of predictors from random forest variable importance measures. Briefings in Bioinformatics, 12(4), 369-373.
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 5(2), 109-131.
• Oribel, T., & Hanggraeni, D. (2021). An application of machine learning in financial distress prediction cases in Indonesia. International Journal of Business and Technology Management, 3(2), 98-110.
• Oz, I. O., & Simga-Mugan, C. (2018). Bankruptcy prediction models' generalizability: Evidence from emerging market economies. Advances in Accounting, 41, 114-125.
• Oz, I. O., & Yelkenci, T. (2017). A theoretical approach to financial distress prediction modeling. Managerial Finance, 43(2), 212-230.
• Özlem, Ş., & Tan, O. F. (2022). Predicting cash holdings using supervised machine learning algorithms. Financial Innovation, 8(1), 1-19.
• Özparlak, G., & Dilidüzgün, M. Ö. (2022). Corporate bankruptcy prediction using machine learning methods: the case of the USA. Uluslararası Yönetim İktisat ve İşletme Dergisi, 18(4), 1007-1031.
• Qian, H., Wang, B., Yuan, M., Gao, S., & Song, Y. (2022). Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree. Expert Systems with Applications, 190, 116202.
• Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229.
• Tsai, C. F., Hsu, Y. F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing, 24, 977-984.
• Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 213-229.
• Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 3(4), 59-82