Karafan Journal

Karafan Journal

Dynamic forecasting of financial bankruptcy using the Malm Quest method (Case Study: companies listed on the Tehran Stock Exchange )

Document Type : Original Article

Authors
Alireza mirarab bayegi 1
hashem mokari 2
Arash Azariyon 3
1 Assistant Professor, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
2 PhD Student of Financial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
3 PhD Student of Industrial Management - Financial Orientation, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
10.48301/kssa.2020.124675
Abstract
It is important to assess the financial difficulties of companies because the failure of the company has many direct and indirect costs for stakeholders. Timely and accurate assessment and forecasting can help decision makers find the optimal solution and prevent financial distress. So far, paragliding has been used to assess financial distress. The patterns used in this field are very useful in the decisions of financial market participants. Efforts have always been made to accurately predict and evaluate these patterns using more advanced methods. In this regard, Lee et al. demonstrated in their recent study that using dynamic methods can be more accurate than previous methods. The MalmQuist model has been used to explain the financial helplessness of companies listed on the stock exchange, which shows that this method has a high ability to recognize the financial helplessness of companies and solves the inefficiency of previous methods. Accordingly, in order to further investigate the subject matter of the research, the problem is presented and the necessity of conducting the research is discussed.
Keywords
Financial helplessness
inefficiency
bankruptcy
neural network
laziness probation
References
Ahmadpour, A., & Mirzaie Asrami, H. (2013). Compared with Multiple Discriminate Analysis Model and neural network Models in Predicting Bankruptcy of the listed Companies in Tehran Stock Exchange. Accounting and Auditing Research, 5(19), 4-21. https://doi.org/ 10.22034/iaar.2013.104532
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609. https://doi.org/10.2307/297893 3
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model. Journal of International Financial Management & Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053
Anjum, S. (2012). Business bankruptcy prediction models: A significant study of the Altman’s Z-score model. Asian Journal Of Management Research 3(1), 212-219. https://doi.org/10.21 39/ssrn.2128475
Anvary Rostami, A. A., Tajvidi, E., & Jahangard, M. (2015). Empirical Investigation on the Effect of Capital Structure and Board Compensation on Investment Efficiency. Empirical Research in Accounting, 5(4), 109-130. https://doi.org/10.22051/jera.201 5.2111
Du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy prediction. Expert systems with Applications, 75, 25-43. https://doi.org/10.1016/j.eswa.2017.01.016
Du, X., & Lai, S. (2018). Financial Distress, Investment Opportunity, and the Contagion Effect of Low Audit Quality: Evidence from China. Journal of Business Ethics, 147(3), 565-593. https://doi.org/10.1007/s10551-015-2986-5
Emami Meybodi, A., & Jaidari, F. (2014). Measurement of bioefficiency of Iranian oil refineries using data envelopment analytical method. Quarterly Journal of Economic Research, 14(4), 79-96. http://ensani.ir/file/download/article/201504291 41131-9461-109.pdf
Erdogan, B., Egrioglu, E., & Akdeniz, E. (2017). Support vector machines vs multiplicative neuron model neural network in prediction of bank failures. American Journal of Intelligent Systems, 7(5), 125-131. http://article.sapub.org/10.5923.j.ajis.20170705. 01.html
Esmaeili, S., & Googerdchian, A. (2017). Predicting Corporation Bankruptcy Using Cash Flow Statement: Applying Artificial Neural Network. Organizational Culture Management, 15(4), 879-901. https://doi.org/10.22059/jomc.2018.115408.100624 5
Ghanbari, Y. (2011). Investigating the usefulness of using data mining techniques in predicting companies' financial distress [Master, Ministry of Science, Research and Technology]. Tahran, Iran.
Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of accounting studies, 9(1), 5-34. https://doi.org/10.1023/B:RAS T.0000013627.90884.b7
Hosseini, S. M., & Rashidi, Z. (2013). Predicting the financial distress of companies listed on the Tehran Stock Exchange using the decision tree and logistic regression. Financial Accounting Research, 5(3), 105-130. http://ensani.ir/file/download/articl e/20140608115928-9466-56.pdf
Ismaili, E. (2018). Using computational intelligence tools to predict bankruptcy and financial crisis [Master, Isfahan University of Technology]. Iran.
Iyer, E., & Murti, V. K. (2015). Comparison of logistic regression and artificial neural network based bankruptcy prediction models. International Journal of Business Analytics and Intelligence, 3(1), 23-31. http://www.publishingindia.com/ijbai/54/comparison-of-logis tic-regression-and-artificial-neural-network-based-bankruptcy-prediction-models/375/ 2691/
Jones, S., & Hensher, D. A. (2004). Predicting Firm Financial Distress: A Mixed Logit Model. The Accounting Review, 79(4), 1011-1038. https://doi.org/10.2308/accr.2004.79.4.1011
Keener, M. H. (2013). Predicting the financial failure of retail companies in the United States. Journal of Business & Economics Research, 11(8), 373-380. https://doi.org/10.1903 0/jber.v11i8.7982
Laane, K. (2017). Corporate Bankruptcy Prediction Of Estonian Firms [Master, Tartu]. Estonia. https://dspace.ut.ee/bitstream/handle/10062/57904/laane_kerttu.pdf?sequence=1&isAllowed=y
Li, Z., Crook, J., & Andreeva, G. (2017). Dynamic prediction of financial distress using Malmquist DEA. Expert systems with Applications, 80, 94-106. https://doi.org/10.1016/j.eswa.2017.0 3.017
Maleki, A., Zare, A., NiKoumaram, H., & Shahverdiani, S. (2020). Credit risk optimization model for crowdfunding process by using Neural Network(MLP). Financial Engineering and Portfolio Management, 11(43), 271-290. http://fej.iauctb.ac.ir/arti cle_676332_67dfdcfb125abe89a7b29715826a00f7.pdf
Onakoya, A. B., & Olotu, A. E. (2017). Bankruptcy and insolvency: An exploration of relevant theories. International Journal of Economics and Financial Issues, 7(3), 706-712. https:/ /dergipark.org.tr/en/pub/ijefi/issue/32021/354317?publisher=http-www-cag-edu-tr-ilha n-ozturk
Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review. European Journal of Operational Research, 180(1), 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The journal of business, 74(1), 101-124. http://homes.chass.utoronto.ca/~szhou/print/Fo rcastingBankruptcyHazardModel.pdf
Sun, J., Fujita, H., Chen, P., & Li, H. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge-Based Systems, 120, 4-14. https://doi.org/10.1016/j. knosys.2016 .12.019
Taheri, S. M. (2012). Designing a Conceptual Model for Predicting the Failure Behavior and Success of Medium and Small Project-Based Companies Using a Dynamic Systems Model with a Financial Approach [Master, Shahroud]. Iran. https://www.vi rascience.co m/thesis/696645/
Zanna, M. P., Olson, J. M., & Fazio, R. H. (1980). Attitude–behavior consistency: An individual difference perspective. Journal of personality and social psychology, 38(3), 432-440. htt ps://doi.org/10.1037/0022-3514.38.3.432
Karafan Journal
Volume 17, Issue 3 - Serial Number 49
Management and Entrepreneurship
Autumn 2020
Pages 211-229

  • Receive Date 16 June 2020
  • Revise Date 05 December 2020
  • Accept Date 08 December 2020