Developing an Optimal Method for Financial Distress Prediction of the Firms (Case Study: Tehran Stock Exchange)

Subject Areas : Journal of Investment Knowledge

Mansour  Soufi ¹ (Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mahdi  Homayounfar ² (Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mehdi  Fadaei ³ (Assistant professor in Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)

Received: 2018-09-01 Accepted : 2018-10-20 Published : 2020-11-21

Keywords: Forecasting, Artificial Neural Network, Genetic algorithm, Financial Distress,

Abstract :

One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm.

References:

• رستمی، محمدرضا، فلاح شمس لیالستانی، میرفیض و اسکندری، لاله. (1390). "ارزیابی درماندگی مالی شرکت‌های پذیرفته­شده در بورس اوراق بهادار تهران: مطالعه مقایسه‌ای بین تحلیل پوششی داده‌ها و رگرسیون لجستیک". پژوهش‌های مدیریت در ایران، 15، 129-147.


• طالب­نیا، قدرت­اله، جهانشاد، آزیتا، پورزمانی، زهرا، (1388). "ارزیابی کارایی متغیرهای مالی و متغیرهای اقتصادی در پیش­بینی بحران مالی شرکت‌ها (مورد مطالعه شرکت‌های پذیرفته در بورس اوراق بهادار تهران)". بررسیهای حسابداری و حسابرسی، 16، 67-84.


• کردستانی، غلامرضا، تاتلی، رشید، کوثری، حمید. (1393). "ارزیابی توان پیش­بینی مدل تعدیل شده آلتمن از مراحل درماندگی مالی نیوتن و ورشکستگی شرکت­ها". دانش سرمایه­گذاری، 9، 83-99.


• کمیجانی، اکبر، سعادت فر، جواد. (1385). "کاربرد مدلهای شبکه عصبی در پیش­بینی ورشکستگی اقتصادی شرکتهای بازار بورس". جستارهای اقتصادی، 6، 11-44.


• محسنی، رضا، آقا بابایی، رضا، محمدقربانی، وحید، (1392). "پیش­بینی درماندگی مالی با بکار بردن کارایی به عنوان یک متغیر پیش­بینی­کننده". پژوهش­ها و سیاستهای اقتصادی، 65، 123-146.


• واعظ قاسمی، محسن، رمضانپور چهارده، سعید. (1397). "پیش­بینی ورشکستگی شرکت­های پذیرفته شده در سازمان بورس و اوراق بهادار با استفاده از شبکه عصبی مصنوعی". دانش سرمایه­گذاری، 27، 277-296.


• Altman, E. I. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”. Journal of Finance, 23, 589-609.


• Beaver, W. H. (1966). “Financial ratios as predictors of failure”. Journal of Accounting Research, 4, 71–111.


• Brockett, P. L., Golden, L. L. Jang, J., & Yang, C. C. (2006). “A comparison of neural network, statistical methods and variable choice for life insurers’ financial distress prediction". Journal of Risk & Insurance, 7, 397–419.


• Chen, W. S., & Du, Y. K. (2009). “Using neural networks and data mining techniques for the financial distress prediction model”. Expert Systems with Applications, 36, 4075-4086.


• Chen, J. H. (2012). Developing SFNN models to predict financial distress of construction companies. Expert Systems with Applications, 39, 823-827.


• Choi, H., Son, C., & Kim, C. (2018). “Predicting financial distress of contractors in the construction industry using ensemble learning”. Expert Systems with Applications, 110, 1-10.


• 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.


• Erdogan, B. E. (2013). “Prediction of bankruptcy using support vector machines: anapplication to bank bankruptcy”. Journal of Statistical Computation and Simulation, 83, 1543–1555.


• Geng, R. B., Bose, I., & Chen, X. (2015). “Prediction of financial distress: An empirical study of listed Chinese companies using data mining”. European Journal of Operational Research, 241, 236-247.


• Jayasekera, R. (2018). “Prediction of company failure: Past, present and promising directions for the future”. International Review of Financial Analysis, 55, 196-208.


• Koyuncugil, A., & Ozgulbas, N. (2012). “Financial early warning system model and data mining application for risk detection”. Expert Systems with Applications, 39, 6238-6253.


• Li, Z., Crook, J., & Andreeva, G. (2017). “Dynamic prediction of financial distress using malmquist DEA”. Expert Systems with Applications, 80, 94-106.


• Liang, D., Tsai, C. F., & Wu, H. T. (2015). “The effect of feature selection on financial distress prediction”. Knowledge based systems, 73, 289-297.


• Lin, T. H. (2009). “A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models”. Neurocomputing, 72, 3507–3516.


• Lin, F., Liang, D., Yeh, C. C., & Huang, J. C. (2014). “Novel feature selection methods to financial distress prediction”. Expert Systems with Applications, 41, 2472–2483.


• Mousavi, M. M., Ouenniche, J., & Xu, B. (2015). “Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework”. International Review of Financial Analysis, 42, 64–75.


• Mselmi, N., Lahiani, A., & Hamza, T. (2017). “Financial distress prediction: The case of French small and medium-sized firms”. International Review of Financial Analysis, 50, 67-80.


• Newton, G. W. (1998). “Bankruptcy insolvency accounting practice and procedure”. New Jersey, John Wiley & Sons Inc.


• Ninh, B. P. V., Thanh, T. D., & Hong, D. V. (2018). “Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam”. Economic Systems, (In Press).


• Premachandra, I. M., Chen, Y., & Watson, J. (2011). “DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment”. Omega, 39, 620–626.


• Ravi, V., Kurniawan, H., Thai, P. N. K., & Kumar, P. R. (2008). “Soft computing system for bank performance prediction”. Applied Soft Computing, 8, 305–315.


• Sayari, N., & Mugan, C. S. (2017). “Industry specific financial distress modeling”. Business Research Quarterly, 20, 45-62.


• Sun, J., He, K. Y., & Li, H., (2011). “SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams”. Knowledge based systems, 24, 1013–1023.


• Sun, J., & Li, H. (2008). “Data mining method for listed companies' financial distress prediction”. Knowledge based systems, 21, 1-5.


• Sun, J., & Li, H. (2009). “Financial distress early warning based on group decision making”. Computers & Operations Research, 36, 885–906.


• Tinoco, M. H., & Wilson, N. (2013). “Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables”. International Review of Financial Analysis, 30, 394-419.


• Wang, G., Chen, G., & Chu, Y. (2018). “A new random subspace method incorporating sentiment and textual information for financial distress prediction”. Electronic Commerce Research and Applications, 29, 30-49.


• Wang, G., Ma, J., & Yang, S. (2014). “An improved boosting based on feature selection for corporate bankruptcy prediction”. Expert Systems with Applications, 41, 2353-2361.


• Wanke, P., Barros, C. P., & Faria, J. R. (2014). “Financial distress drivers in Brazilian banks: A dynamic slacks approach”. European Journal of Operational Research, 240, 258-268.


• Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). “The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster–Shafer evidence theory”. Knowledge-Based Systems, 26, 196–206.


• Zhang, X., & Hu, L., (2016). “A nonlinear subspace multiple kernel ledarning for financial distress prediction of Chinese listed companies”. Neurocomputing, 177, 636-642.


• Zhou, L., Lai, K. K., & Yen, J. (2012). “Empirical models based on features ranking techniques for corporate financial distress prediction”. Computers and Mathematics with Applications, 64, 2484–2496.


• Zhou, L. G., Lu, D., & Fujita, H. (2015). “The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches”. Knowledge-Based Systems, 85, 52-61.