Financial Distress Prediction Using Artificial Neural Network, Partial Least Squares Regression, Support Vector Machine Hybrid Model, and Logit Model

Document Type : Research Paper

Authors

1 Department of Financial Management and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

2 Faculty of Economics and Political Science, Shahid Beheshti University, Tehran, Iran

10.22059/ier.2024.350546.1007572

Abstract

Financial distress refers to the situation where a firm’s cash flows are insufficient to meet contractually required payments. This has caused concern among capital owners and compelled financial analysts to employ a variety of methods to assess companies’ equity and analyze the firm’s financial status. Assessing and predicting financial distress in a timely and accurate manner can aid decision-makers in finding the optimal solution and preventing it. Numerous models have been developed thus far to predict and evaluate financial distress. The prediction accuracy has been improved through the use of various innovative methods. Using financial ratios and market data as independent variables and obtaining patterns for the financial forecast is one of the most important methods for evaluating the financial stability of businesses. Therefore, the primary objective of this study is to evaluate the performance of five models in this field, compare their accuracy of prediction, and ultimately select the best model to predict financial distress for a specified period in Iran. Specifically, the logit model, artificial neural network (ANN), support vector machine (SVM), partial least squares regression (PLS), and a hybrid model of SVM and PLS were chosen, analyzed, and compared. The results of the average accuracy of prediction indicate that the SVM has the highest accuracy one year before the onset of financial distress. In addition, findings from the two years preceding the failure indicate that the SVM-PLS model provides the most accurate classification of financially distressed and non-distressed firms.

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Adubisi, O. D., Abdulkadir, A., & Adubisi, C. (In Press). The Superiority of the EGARCH-Odd Exponentiated Skew-t Model in Predicting Financial Returns Volatility. Iranian Economic Review, Retrieved from https://ier.ut.ac.ir/article_90679.html   
Aghaei, M., Kazemi, A., Moezzi, A. D., Rajabian, M., Beigi, M., & Asadollahi, A. (2013). Financial Distress and Bankruptcy Prediction in Subsidiaries of the Largest Business Holding in Iran Using the Model of Altman. Research Journal of Recent Sciences, 2(8), 40-46.
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589–609.
Ardalankia, J., Osoolian, M., Haven, E., & Jafari, G. R. (2020). Scaling Features of Price–Volume Cross Correlation. Physica A: Statistical Mechanics and Its Applications, 549(1), [124111].
Arnis, N., Karamanis, K., & Kolias, G. (2019). Detecting Creative Accounting in Businesses in Financial Distress. Accounting and Finance Research, 8(2), 232–244.
Back, B., Laitinen, T., Sere, K., & van Wezel, M. (1996). Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms. Turku Centre for Computer Science Technical Report, 40(2), 1–18.
Bae, J. K. (2012). Predicting Financial Distress of the South Korean Manufacturing Industries. Expert Systems with Applications, 39(10), 9159–9165.
Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A Review of Bankruptcy Prediction Studies: 1930 To Present. Journal of Financial Education, 1–42.
Beneish, M. D., Press, E., & Vargus, M. E. (2012). Insider Trading and Earnings Management in Distressed Firms. Contemporary Accounting Research, 29(1), 191–220.
Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 1, 1–25.
bonabi ghadim,  rahim. (2022). The Effect of Information Asymmetry and Financial Distress on the Relationship between Accounting Conservatism and Loan Quality Portfolio in Iran’s Banking System. Quarterly Studies in Banking Management and Islamic Banking, 8(18), 63–92.
Brigham, E. F., & Ehrhardt, M. C. (2013). Financial Management: Theory & Practice (Book Only). Boston: Cengage Learning.
Chen, M.-Y. (2011). Predicting Corporate Financial Distress Based on Integration of Decision Tree Classification and Logistic Regression. Expert Systems with Applications, 38(9), 11261–11272.
Chen, M.-Y. (2012). Visualization and Dynamic Evaluation Model of Corporate Financial Structure With Self-Organizing Map and Support Vector Regression. Applied Soft Computing, 12(8), 2274–2288.
Chen, M.-Y. (2013). A Hybrid ANFIS Model for Business Failure Prediction Utilizing Particle Swarm Optimization and Subtractive Clustering. Information Sciences, 220, 180–195.
Chien, F., Pantamee, A. A., Hussain, M. S., Chupradit, S., Nawaz, M. A., & Mohsin, M. (2021). Nexus Between Financial Innovation and Bankruptcy: Evidence From Information, Communication and Technology (ICT) Sector. The Singapore Economic Review, 21(1), 1–22.
Cleofas-Sánchez, L., García, V., Marqués, A. I., & Sánchez, J. S. (2016). Financial Distress Prediction Using the Hybrid Associative Memory With Translation. Applied Soft Computing, 44, 144–152.
Deakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167–179.
Dirman, A. (2020). Financial Distress: The Impacts of Profitability, Liquidity, Leverage, Firm Size, and Free Cash Flow. International Journal of Business, Economics and Law, 22(1), 17-25.
Farid, S., Mohsan, T., & Jan, M. W. (2022). Do Islamic Stocks Reinforce Real Economic Activity? Evidence from an Emerging Islamic Capital Market. Iranian Economic Review, 26(2), 421–433.
Farzin, F., Googerdchian, A., & Saffari, B. (2021). Anticipation of Currency Crisis in Iran Economy with the Use of an Early Warning System. Iranian Economic Review, 25(1), 69–83.
Hennessy, C. A., & Whited, T. M. (2005). Debt Dynamics. The Journal of Finance, 60(3), 1129–1165.
Hsiao, H.-F., Lin, S.-H., & Hsu, A.-C. (2010). Earnings Management, Corporate Governance, and Auditor’s Opinions: A Financial Distress Prediction Model. Investment Management and Financial Innovations, 7(3), 29–40.
Jayanthi, J., Suresh, J. K., & Vaishnavi, J. (2011). Bankruptcy Prediction Using Svm and Hybrid Svm Survey. International Journal of Computer Applications34(7), 39-45.
Kamaluddin, A., Ishak, N., & Mohammed, N. F. (2019). Financial Distress Prediction Through Cash Flow Ratios Analysis. International Journal of Financial Research, 10(3), 63–76.
Keasey, K., & McGuinness, P. (1990). The Failure of UK Industrial Firms for the Period 1976–1984, Logistic Analysis and Entropy Measures. Journal of Business Finance & Accounting, 17(1), 119–135.
Keasey, K., & Watson, R. (2019). Financial Distress Prediction Models: A Review of Their Usefulness (35–48). In G. Mars and D. T. H. Weir (Ed.), Risk Management. London: Routledge.
Khademolqorani, S., Zeinal Hamadani, A., & Mokhatab Rafiei, F. (2015). A Hybrid Analysis Approach to Improve Financial Distress Forecasting: Empirical Evidence from Iran. Mathematical Problems in Engineering, 2015(1), 1-9.
Koh, S., Durand, R. B., Dai, L., & Chang, M. (2015). Financial Distress: Lifecycle and Corporate Restructuring. Journal of Corporate Finance, 33, 19–33.
Koushki, A., Osoolian, M., & Sadeghi Sharif, S. J. (2022). An Uncertainty Measure Based on Pearson Correlation As Well as a Multiscale Generalized Shannon-Based Entropy With Financial Market Applications. International Journal of Nonlinear Sciences and Numerical Simulation, 24(5), 1821-1839.
Kwak, W., Shi, Y., & Kou, G. (2012). Predicting Bankruptcy After the Sarbanes-Oxley Act Using the Most Current Data Mining Approaches. Journal of Business & Economics Research (JBER), 10(4), 233–242.
Liang, D., Tsai, C.-F., Lu, H.-Y. R., & Chang, L.-S. (2020). Combining Corporate Governance Indicators With Stacking Ensembles for Financial Distress Prediction. Journal of Business Research, 120, 137–146.
Liang, L., & Wu, D. (2005). An Application of Pattern Recognition on Scoring Chinese Corporations Financial Conditions Based on Backpropagation Neural Network. Computers & Operations Research, 32(5), 1115–1129.
Lin, A. J. I. N. (2021). Volatility Contagion Among Stock, Currency, and Bulk Shipping Market During the China’s Stock Market Crash Crisis. The Singapore Economic Review, 69(6), 1995-2012.
López-Gutiérrez, C., Sanfilippo-Azofra, S., & Torre-Olmo, B. (2015). Investment Decisions of Companies in Financial Distress. BRQ Business Research Quarterly, 18(3), 174–187.
Manzaneque, M., Priego, A. M., & Merino, E. (2016). Corporate Governance Effect on Financial Distress Likelihood: Evidence From Spain. Revista de Contabilidad, 19(1), 111–121.
Maripuu, P., & Männasoo, K. (2014). Financial Distress and Cycle-Sensitive Corporate Investments. Baltic Journal of Economics, 14(1–2), 181–193.
Min, J. H., & Lee, Y.-C. (2005). Bankruptcy Prediction Using Support Vector Machine With Optimal Choice of Kernel Function Parameters. Expert Systems with Applications, 28(4), 603–614.
Mselmi, N., Lahiani, A., & Hamza, T. (2017). What Is the Best Way to Predict Financial Distress of Companies. International Review of Financial Analysis, 50, 67–80.
Muller, G. H., Steyn-Bruwer, B. W., & Hamman, W. D. (2012). What Is the Best Way to Predict Financial Distress of Companies. Retrieved from https://scholar.sun.ac.za/bitstream/10019.1/85389/2/muller_wat_2012.pdf 
Myšková, R., & Hájek, P. (2017). Comprehensive Assessment of Firm Financial Performance Using Financial Ratios and Linguistic Analysis of Annual Reports. Journal of International Studies, 10(4), 96-108.
Newton, G. W. (1976). Bankruptcy and Insolvency Accounting Practice and Procedure. The CPA Journal (Pre-1986), 46(000005), 1-24.
Oliveira, M., Kadapakkam, P.-R., & Beyhaghi, M. (2017). Effects of Customer Financial Distress on Supplier Capital Structure. Journal of Corporate Finance, 42, 131–149.
Osoolian, M., Fadaeinejad, M. E., Bagheri, M., & Ardalankia, J. (2022). Scaling Analysis of Price by Multi-Scale Shannon Entropy. International Journal of Modern Physics C, 34(03), [2350038].
Pindado, J., & Rodrigues, L. F. (2004). Parsimonious Models of Financial Insolvency in Small Companies. Small Business Economics, 22(1), 51–66.
Rafatnia, A. A., Suresh, A., Ramakrishnan, L., Abdullah, D. F. B., Nodeh, F. M., & Farajnezhad, M. (2020). Financial Distress Prediction Across Firms. Journal of Environmental Treatment Techniques, 8(2), 646–651.
Rosner, R. L. (2003). Earnings Manipulation in Failing Firms. Contemporary Accounting Research, 20(2), 361–408.
Sadehvand, M. J., Nikoomaram, H., Ghalibaf Asl, H., & Fallah Shams, M. F. (2022). Investigating and Comparing the Performance of Conventional and Hybrid Models of Predicting Financial Distress. Financial Research Journal, 24(2), 214–235.
Salehi, M., & Abedini, B. (2009). Financial Distress Prediction in Emerging Market: Empirical Evidences From Iran. Business Intelligence Journal, 2(2), 398–409.
Sayari, N., & Mugan, C. S. (2017). Industry-Specific Financial Distress Modeling. BRQ Business Research Quarterly, 20(1), 45–62.
Sun, J., & Li, H. (2011). Dynamic Financial Distress Prediction Using Instance Selection for the Disposal of Concept Drift. Expert Systems with Applications, 38(3), 2566–2576.
Sun, J., & Li, H. (2012). Financial Distress Prediction Using Support Vector Machines: Ensemble vs. Individual. Applied Soft Computing, 12(8), 2254–2265.
Sun, J., Li, H., Fujita, H., Fu, B., & Ai, W. (2020). Class-Imbalanced Dynamic Financial Distress Prediction Based on Adaboost-SVM Ensemble Combined With SMOTE and Time Weighting. Information Fusion, 54, 128–144.
Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating Textual and Management Factors Into Financial Distress Prediction: A Comparative Study of Machine Learning Methods. Journal of Forecasting, 39(5), 769–787.
Tarighi, H., Hosseiny, Z. N., Abbaszadeh, M. R., Zimon, G., & Haghighat, D. (2022). How Do Financial Distress Risk and Related Party Transactions Affect Financial Reporting Quality? Empirical Evidence From Iran. Risks, 10(3), 1-23.
Vapnik, V. N. (1995). The Nature of Statistical Learning. Theory. Berlin: Springer Science & Business Media.
Varahrami, V., & Javaherdehi, S. (2018). Predicting the Country Commodity Imports Using Mixed Frequency Data Sampling (MIDAS) Model. Iranian Economic Review, 22(4), 867–886.
Wang, H., & Yu, J. (2004). Application Study on Nonlinear Dynamic FIR Modeling Using Hybrid SVM-PLS Method. Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No. 04EX788), 4, 3479–3482.
Yu, L., Härdle, W. K., Borke, L., & Benschop, T. (2023). An AI Approach to Measuring Financial Risk. The Singapore Economic Review, 68(05), 1529-1549.
Zarei, H., Yazdifar, H., & Ghaleno, M. D. (2020). Predicting Auditors’ Opinions Using Financial Ratios and Non-financial Metrics: Evidence From Iran. Journal of Accounting in Emerging Economies, 10(3), 425–446.
Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116(1), 16–32.