مقایسه معیارهای تشخیص شرکت‌های درمانده مالی با استفاده از رگرسیون لجستیک و روش‌های هوش مصنوعی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناسی ارشد حسابداری، دانشگاه ارومیه، ارومیه ایران.

2 دانشیار گروه حسابداری، دانشگاه ارومیه، ارومیه، ایران.

3 استادیار گروه حسابداری، دانشگاه ارومیه، ارومیه، ایران.

چکیده

در محیط رقابتی امروز و با تغییرات شرایط بازارها، احتمال درماندگی مالی شرکت‌ها افزایش یافته است. در این شرایط افراد، شرکت‌های سرمایه‌گذار و سازمان‌های مالی تلاش زیادی برای اطلاع از وضعیت فعلی و آتی شرکت‌های سرمایه‌پذیر در جهت محافظت از سرمایه خود انجام می‌دهند. ارزیابی و تشخیص صحیح وضعیت مالی شرکت‌ها و همچنین پیش‌بینی وضعیت مالی آتی آن‌ها نیازمند استفاده از معیارهای کارآمد با احتمال خطای کمتر است؛ بنابراین هدف این پژوهش رتبه‌بندی معیارهای منتخب در شناسایی بهتر شرکت‌های درمانده مالی است. بدین منظور پس از بررسی و شناسایی پرکاربردترین معیارها و مدل‌های تشخیص شرکت‌های درمانده، با استفاده از آن‌ها شرکت‌های درمانده «بورس اوراق بهادار تهران» طی سال‌های 1384 تا 1396 از شرکت‌های غیردرمانده (سالم) تفکیک و با استفاده از نتایج حاصل از رگرسیون لجستیک و روش‌های هوش مصنوعی و معیارهای ماده 141 قانون تجارت ایران، آلتمن (1968)، آلتمن (1995) و آسکویت و همکاران (1994) مقایسه شدند. نتایج نشان داد در دوره مورد ­بررسی و در شرایط حاکم بر شرکت‌های ایرانی مستقر در «بورس اوراق بهادار تهران»، معیار آسکویت و همکاران (1994)، بهترین روش برای شناسایی شرکت‌های درمانده مالی است و معیارهای آلتمن (1995)، ماده 141 قانون تجارت ایران و آلتمن (1968) در اولویت‌های بعدی از لحاظ شناسایی شرکت‌های درمانده قرار گرفتند.

کلیدواژه‌ها


عنوان مقاله [English]

Comparing the Identifying Criteria for Financially Distressed Companies using Logistic Regression and Artificial Intelligence Methods

نویسندگان [English]

  • Alireza Aliakbarlou 1
  • Gholamreza Mansourfar 2
  • Farzad Ghayour 3
1 MSc of Accounting, Urmia University, Urmia, Iran.
2 Associate Prof, Department of Accounting, Urmia University, Urmia, Iran
3 Assistant Prof, Department of Accounting, Urmia University, Urmia, Iran.
چکیده [English]

In current competitive environment, the risk of financial distress of companies has increased, in this situation, individuals and corporations and financial institutions make a lot of effort to learn about the status of Investee companies to protect their capital. The assessment and recognition of the firm's financial condition requires utilizing efficient measures with less error probability, Therefore, the purpose of this study is to compare the different criteria for identifying financially distressed companies. For this purpose, after reviewing and identifying the most important criteria and models for identifying distressed companies from non-distressed corporations, the distressed companies of Tehran Stock Exchange during the years 2006 to 2018 were separated from healthy firms and compared with the results of logistic regression and artificial intelligence methods, Article 141 of the trade law, Altman (1968), Altman (1995) and Asquith et al (1994). The results of this study showed that during the period under review and in the conditions of the Iranian companies based on Tehran Stock Exchange that Asquith et al (1994) criteria is the best way to identify distressed companies and to predict the financial position of companies and the Altman criteria (1995), Article 141 of the trade law and Altman (1968) are among the top priorities in identifying distressed enterprises.

کلیدواژه‌ها [English]

  • Distress Distinction Criteria’s
  • Logistic Regression
  • Artificial Intelligence Methods
  1. Abdolbaghi Ataabadi, A., Mirlohi, S. (2019). Life cycle, Corporate Failure and Restructuring Strategies: Evidences of Tehran Security Exchange. Journal of Accounting Advances, 11(1), 221-252. (In Persian)
  2. Alfiyanti, M. H., Damayanti, C. R., & Nurlaily, F. (2020). Analisis Financial Distress Dengan Menggunakan Metode Altman Z-Score Dan Springate S-Score (Studi pada Emiten Sektor Industri Barang Konsumsi Sub Sektor Food & Beverages yang Terdaftar di Bursa Efek Indonesia Tahun 2014-2018). Jurnal Administrasi Bisnis, 78(1), 76-85.
  3. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
  4. Altman, E. I., Eom, Y. H., & Kim, D. W. (1995). Failure prediction: evidence from Korea. Journal of International Financial Management & Accounting, 6(3), 230-249.‏
  5. 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.‏
  6. Asquith, P., Gertner, R., & Scharfstein, D. (1994). Anatomy of financial distress: An examination of junk-bond issuers. The Quarterly Journal of Economics, 109(3), 625-658.‏
  7. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.‏
  8. Botshekan, M. H., Salimi, M., & Falahatgar Mottahedjoo, S. (2018). Developing a hybrid approach for financial distress prediction of listed companies in Tehran stock exchange. Financial Research Journal, 20(2), 173-192. (In Persian)
  9. 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.‏
  10. Chiou, J., & Lin, Y. (2005). The structure of corporate ownership: A comparison of China and Taiwan’s security markets. Journal of American Academy of Business, 6(2), 123-127.‏
  11. Damoori (Ph.D), D., Hozhabrie, F. (2019). Impact of Life Cycle on Corporate Restructuring while in Financial Distress. Journal of Accounting Knowledge, 10(2), 113-135. (In Persian)
  12. Fan, J. P., Huang, J., & Zhu, N. (2013). Institutions, ownership structures, and distress resolution in China. Journal of Corporate Finance, 23, 71-87.‏
  13. Fulmer, J. G., Moon, J. E., Gavin, T. A., & Erwin, J. M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11), 25-37.
  14. Gameel, M. S., & El-Geziry, K. (2016). Predicting financial distress: multi scenarios modeling using neural network. International Journal of Economics and Finance, 8(11), 159.‏
  15. Gitman, L.J. (1998). Principle of Managerial Finance. Working Paper. New York. Harper Collins College.
  16. Gordon, M. J. (1971). Towards a theory of financial distress. The Journal of Finance, 26(2), 347-356.‏
  17. heidari, M., mansourfar, G., ghasemzade, M. (2018). Determinants of Capital Structure and Moderating Role of Financial Distress; Structural Equations Modeling (SEM) Approach. Journal of Financial Accounting Research, 10(2), 23-44. (In Persian)
  18. Heydary Farahany, M., ghayour, F., mansourfar, G. (2019). The effect of management behavioral strains on financial distress. Journal of Financial Accounting Research, 11(3), 117-134. (In Persian)
  19. Jia, J., Hutchinson, M., & Hogarth, K. (2016). Does firm’s human capital in risk management reduce the likelihood of financial distress?. AFAANZ Conference.
  20. Juniarti, J. (2013). Good Corporate Governance and Predicting Financial Distress Using Logistic and Probit Regression Model. Jurnal Akuntansi dan Keuangan, 15(1), 43-50.‏
  21. Khajavi, S., Ghadirian-Arani, M. (2018). The role of managerial ability in financial distress prediction. Journal of Financial Accounting Research, 9(4), 83-102. (In Persian)
  22. Kihooto, E., Omagwa, J., & Ronald, M. W. E. (2016). Financial distress in commercial and services companies listed at Nairobi Securities Exchange, Kenya. European Journal of Business and Management, 8(27), 86-89.
  23. Kordestani, G., Tatli, R., Kosari Far, H. (2014). The evaluate ability of altman adjusted model to prediction stages of financial distress newton and bankruptcy. Journal of Investment Knowledge, 3(9), 83-100. (In Persian)
  24. Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169(2), 677-697.‏
  25. Li, H., & Sun, J. (2011). Predicting business failure using forward ranking-order case-based reasoning. Expert Systems with Applications, 38(4), 3075-3084.‏
  26. Mamo, A. Q. (2011). Applicability of Altman (1968). Model in predicting financial distress of commercial banks in Kenya. PhD diss.‏
  27. Mansourfar, G., Ghayour, F., Lotfi, B. (2015). The ability of support vector machine (SVM) in fnancial distress prediction. Empirical Research in Accounting, 5(1), 177-195. (In Persian)
  28. Mansourfar, G., Ziaei, R. (2013). Real and accounting earnings management and the level of conservatism in successful and unsuccessful firms. Journal of Financial Accounting Research, 5(3), 19-40. (In Persian)
  29. Mecaj, A., & Bravo, M. I. G. (2014). CSR actions and financial distress: Do firms change their CSR behavior when signals of financial distress are identified? Modern Economy, 5(4), 259.‏
  30. Megginson, W. L., Meles, A., Sampagnaro, G., & Verdoliva, V. (2019). Financial distress risk in initial public offerings: how much do venture capitalists matter? Journal of Corporate Finance, 59, 10-30.
  31. Mehrani, S., Kamyabi, Y., Ghayour, F. (2017). Reviewing the effectiveness of earnings quality indices on the power of financial distress prediction models. Accounting and Auditing Review, 24(1), 103-126. (In Persian)
  32. Newton, G. W. (2009). Bankruptcy and Insolvency Accounting, Volume 1: Practice and Procedure (Vol. 1). John Wiley & Sons.
  33. Osama, E. A., & Bassam, L. Predicting Financial Distress for Listed MENA Firms. (2019). International Journal of Accounting and Financial Reporting, 2(9). 51-75.
  34. Piñeiro-Sanchez, C., de Llano Monelos, P., & Lopez, M. R. (2013). A parsimonious model to forecast financial distress, based on audit evidence. Contaduria y Administracion, 58(4), 151-173.
  35. Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184-199.‏
  36. Pourheydari, O., & KOOPAEE, H. M. (2010). Predicting of firms financial distress by use of linear discriminant function the model. Financial Accounting Researches, 2(1), 33-46. (In Persian)
  37. Rađen, D. (2015). The analysis of the effects of financial distress on the top management in the republic of serbia/analiza uticaja finansijskih teškoća na top menadžment u republici srbiji. The European Journal of Applied Economics, 12(1), 19-25.
  38. Raei, R., & Falahpour, S. (2008). Support vector machines application in financial distress prediction of companies using financial ratios. Accounting and Auditing Review, 15(4), 17-34. (In Persian)
  39. Rahnamaie Roodposhti, F., Alikhani, R., & Maranjory, M. (2009). Applicational investigation of Altman and Fulmer bankruptcy prediction models in Tehran stock exchange. Journal of Accounting and Auditing Review, 16(2), 19-34. (In Persian)
  40. Russell, S. J., & Norvig, P. (2002). Artificial intelligence: a modern approach (International Edition). Prentice-Hall.
  41. Salehi, A., Elhaeisahar, M., Savari, A. (2017). Investment decisions of firms under financial distress. Financial Management Perspective, 6(16), 31-49. (In Persian)
  42. Sanchez, C. P., de Llano Monelos, P., & Lopez, M. R. (2013). A parsimonious model to forecast financial distress, based on audit evidence. Contaduria y Administracion, 58(4), 151-173.
  43. Sengupta, R., & Faccio, M. (2011). Corporate response to distress: evidence from the Asian financial crisis. Federal Reserve Bank of St. Louis Review, 93(2), 127-154.‏
  44. Setayesh, M., kazemnezhad, M., hallaj, M. (2016). The usefulness of random forest classifier and relief features selection in financial distress prediction: empirical evidence of companies listed on Tehran stock exchange. Journal of Financial Accounting Research, 8(2), 1-24. (In Persian)
  45. Shilpa, N. C., & Amulya, M. (2017). Corporate financial distress: analysis of Indian automobile industry. SDMIMD Journal of Management, 8(1), 85-93.
  46. Shleeva, N. (2014). What is a relation between hedging and risk of financial distress? available at http://lup.lub.lu.se/student-papers/record/4519474
  47. taj mazinani, M., fallahpour, S., bajalan, S. (2015). The use of feature selection method (HARC) in predicting financial distress in Tehran stock exchange. Financial Management Strategy, 3(2), 77-106. (In Persian)
  48. Tinoco, M. H., Holmes, P., & Wilson, N. (2018). Polytomous response financial distress models: The role of accounting, market and macroeconomic variables. International Review of Financial Analysis.
  49. Vakilifard, H., Ahmadvand, M., Sadehvand, M. (2018). The relationship between financial distress risk and momentum anomaly in Tehran stock exchange. Financial Knowledge of Securities Analysis, 11(38), 43-55. (In Persian)
  50. Van Gestel, T., Baesens, B., Suykens, J. A., Van den Poel, D., Baestaens, D. E., & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172(3), 979-1003.
  51. Weston, J.F., & Copeland, T.E. (1992). Managerial Finance. Dryden press, 9th edition.
  52. Whitaker, R. B. (1999). The early stages of financial distress. Journal of Economics and Finance, 23(2), 123-132.