Accounting and Auditing Research

Accounting and Auditing Research

Using Machine Learning to Provide a Model for Predicting Bankruptcy

Document Type : Original Article

Authors
afshin hashemi gelsefidi 1
Zahra Lashgari 2
zohreh hajiha 3
1 Department of Accounting , Kish International Branch, Islamic Azad University, Kish Island, Iran
2 Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Associate Professor in Accounting, Islamic Azad university of Tehran East Branch, Tehran, Iran
10.22034/iaar.2022.168271
Abstract
The use of traditional forecasting tools and methods has a high error and has a poorer performance compared to newer methods and nonlinear models. One of the most widely used methods and algorithms in predicting the use of machine learning. The main purpose of this study is to investigate the application of machine learning in providing a model for predicting the bankruptcy of 308 companies listed on the Tehran Stock Exchange in the period 1389 to 1398 (3080 years - company) to test the hypotheses of multiple regression of composite data. In order to implement the Medians-K clustering algorithm and related calculations, R statistical calculation software was used. The results show that among the financial ratios identified in the first model, only the ratio of net income to total assets and the ratio of market value of equity to total market value can improve the ability of the Altman bankruptcy prediction model. Also, in the second model, the specified financial ratios have the ability to improve the bankruptcy forecast model, and by adding the Devscore variable for groups based on industry and size, the modified model improves the bankruptcy forecast, The results shows that a company is more likely to go bankrupt if it has bankruptcy-related financial ratios that are lower than the average of its cluster peers..
Keywords
Machine Learning
Clustering Algorithm
Bankruptcy Prediction
  1. آشتاب، علی، حقیقت، حمید، کردستانی، غلامرضا. (1396). "مقایسۀ دقت مدل‌های پیش‌بینی بحران مالی و تأثیر آن بر ابزارهای مدیریت سود". بررسی‏‌های حسابداری و حسابرسی، دوره 24، شماره2، ص ص 147-172.
  2. اعتبار، شکوفه، دارابی، رویا، حمیدیان، محسن، جعفری، سیده محبوبه. (1398). "مقایسه توانایی الگوریتم‌های یادگیری ماشین آدابوست و طبقه‌بندی احتمالی بیزین در پیش‌بینی بیش‌اطمینانی مدیران شرکت‌های بازار سرمایه ایران". مهندسی مالی و مدیریت اوراق بهادار. دوره 10، شماره 39، ص ص 1-25.
  3. اژدری، فاطمه، رهنما رودپشتی، فریدون، حمیدیان، محسن، جعفری، سیده محبوبه، باغانی، علی. (1398). "انتخاب پرتفوی سهام جهت سرمایه گذاری و شناسایی شرکت های برتر با روش محدودیت ال و با استفاده از روش یادگیری ماشین". راهبرد مدیریت مالی، (در حال انتشار). doi: 10.22051/jfm.2019.24505.1968
  4. سلیمانی امیری، غلامرضا (1389). "بررسی شاخص های پیش بینی کننده ورشکستگی در شرایط محیطی ایران"، پایان نامه دکتری، رشته حسابداری، دانشگاه تهران.
  5. شاهوردیانی، شادی، خواجه زاده، سامیران. (1397). "تحلیل نوسان قیمت سهام شرکت های پذیرفته شده در بورس اوراق بهادار تهران با استفاده از روش یادگیری ماشین". سیاست گذاری پیشرفت اقتصادی، دوره ، شماره 1، ص ص 69-91.
  6. قلی زاده سالطه، توحید؛ اقبال‌نیا، محمد؛ آقابابائی، محمد ابراهیم. (1398). "پیش‌بینی ورشکستگی با مدل یادگیری ماشین سریع مبتنی بر کرنل بهینه‌شده با الگوریتم گرگ خاکستری"، تحقیقات مالی، دوره 21، شماره 2، ص ص 187-212.
  7. Altman, Edward I. 1968. "Financial ratios, discriminant analysis and the prediction of corporate " The journal of finance 23 (4):589-609.Alpaydin, Ethem. 2009. Introduction to machine learning: MIT press.
  8. Amit, Raphael, and Joshua Livnat. 1990. "Grouping of Conglomerates by Their Segments’ Economic Attributes: Towards a More Meaningful Ratio Analysis." Journal of Business Finance & Accounting 17 (1):85-100.
  9. Clarke, Richard N. 1989. "SICs as delineators of economic markets." Journal of Business:17-31.
  10. Chen, Ming-Syan, Jiawei Han, and Philip S. Yu. 1996. "Data mining: an overview from a database perspective." IEEE Transactions on Knowledge and data Engineering 8 (6):866-883.
  11. De Franco, Gus, Ole-Kristian Hope, and Stephannie Larocque. 2015. "Analysts’ choice of peer " Review of Accounting Studies 20 (1):82-109.
  12. Elvis Hernandez-Perdomo , Yilmaz Guney , Claudio M. Rocco . (2018). A reliability model for assessing corporate governance using machine learning techniques, Reliability Engineering and System Safety, doi: https://doi.org/10.1016/j.ress.2018.12.027
  13. Fan, Joseph PH, and Larry HP Lang. 2000. "The measurement of relatedness: An application to corporate diversification." The Journal of Business 73 (4):629-660.
  14. Felipe Dias Paiva , Rodrigo Tom«as Nogueira Cardoso , Gustavo Peixoto Hanaoka , Wendel Moreira Duarte . (2018). Decision-Making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Selection, Expert Systems With Applications, doi: https://doi.org/10.1016/j.eswa.2018.08.003.
  15. Guha, Sudipto, Adam Meyerson, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan. "Clustering data streams: Theory and practice." IEEE transactions on knowledge and data engineering 15(3): 515-528.
  16. Gupta, Manak C, and Ronald J Huefner. 1972. "A cluster analysis study of financial ratios and industry characteristics." Journal of Accounting Research:77-95.
  17. Hoberg, Gerard, and Gordon Phillips. 2010. "Product market synergies and competition in mergers and acquisitions: A text-based analysis." The Review of Financial Studies 23 (10):3773-3811.
  18. Hoberg, Gerard, and Gordon Phillips. 2016. "Text-based network industries and endogenous product differentiation." Journal of Political Economy 124 (5):1423-1465.
  19. Holmes, Thomas J, and John J Stevens. 2004. "Spatial distribution of economic activities in North America." In Handbook of regional and urban economics, 2797-2843. Elsevier.
  20. Jensen, Robert E. 1971. "A cluster analysis study of financial performance of selected business " The Accounting Review 46 (1):36-56.
  21. Krishnan, Jayanthi, and Eric Press. 2003. "The north american industry classification system and its implications for accounting research." Contemporary Accounting Research 20 (4):685-717.
  22. Kothari, Sagar P, Andrew J Leone, and Charles E Wasley. 2005. "Performance matched discretionary accrual measures." Journal of Accounting and Economics 39 (1):163-197.
  23. C. Cavalcante, R.C. Brasileiro, V.L.F. Souza, J.P. Nobrega, A.L.I. Oliveira. (2016), Computational intelligence and financial markets: A survey and future directions, Expert Systems with Applications, 55 , pp 194-211.
  24. Ramnath, Sundaresh. 2002. "Investor and analyst reactions to earnings announcements of related firms: An empirical analysis." Journal of Accounting Research 40 (5):1351-1376.
  25. Shumway, Tyler. 2001. "Forecasting bankruptcy more accurately: A simple hazard model." The journal of business 74 (1):101-124.
  26. Ting-Hsuan Chen, Ruey-Jenn Ho, Yi-Wei Liu. (2018). Investigating the Predictive Power of Investor Personality in Forecasting Investment Performance using machine learning models, Computers in Human Behavior, doi: 10.1016/j.chb.2018.09.027
  27. van Liebergen, Bart. 2017. Machine learning: A revolution in risk management and compliance? Journal of Financial Transformation 45: 60-67.
  28. Vlad, M., Tulvinschi, M., & Chiriţă, I. (2011). The consequences of fraudulent financial reporting. The USV Annals of Economics and Public Administration, 11(1), 264-268.
  29. Wu, Xindong, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J McLachlan, Angus Ng, Bing Liu, and S Yu Philip. 2008. "Top 10 algorithms in data mining." Knowledge and information systems 14 (1):1-37.
  30. Young, Steven, and Yachang Zeng. 2015. "Accounting comparability and the accuracy of peerbased valuation models." The Accounting Review 90 (6):2571-2601.
  31. -P. Huang and M.-F. Yen. (2019). A new perspective of performance comparison among machine learning algorithms for financial distress prediction, Applied Soft Computing Journal, doi: https://doi.org/10.1016/j.asoc.2019.105663.