Provide a two-stage model for predicting corporate bankruptcy using data envelopment analysis

Document Type : Original Article

Authors
jalal jamali 1
Aliasghar Mottaghi 2
ahmad mohammadi 2
1 Ph.D. Student, Department of Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
2 Assistant Prof, Department of Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Abstract
Abstract
Objective: Predicting the bankruptcy of companies is one of the most basic activities in auditing risk and uncertainty of companies. Therefore, designing bankruptcy prediction models is essential for many decision-making processes. The purpose of this study is to provide a two-stage model for predicting corporate bankruptcy using data envelopment analysis.

Method: To conduct this research, first data envelopment analysis was used using market-oriented and accounting-based financial data as well as non-financial data to measure the scores of management efficiency and stock market efficiency of companies and then scores were used. The obtained performance has been used to predict the bankruptcy of companies and 9 models of bankruptcy prediction have been proposed. Also, the proposed models were performed using logistic regression of conditional fixed effects and the best model was selected using the ROC curve. The study period of this research is 13 years (1385-1397) and the number of samples of this research includes 184 companies and 2392 year-company observations.

Results: The results showed that the proposed two-stage model has a very good predictive power. The results also showed that management efficiency scores are directly related to bankruptcy. In other words, companies with lower management efficiency scores were in a bad position in terms of bankruptcy risk. Also, accounting-based data had better estimates of bankrupt companies.

Conclusion: The two-stage model presented in this study can be used with high confidence to predict bankrupt companies.
Keywords
Bankruptcy
Data Envelopment Analysis
Management Efficiency
Market Efficiency
ROC
صالحی، نازنین و مجید عظیمی یانچشمه. ) 1395 (. بررسی
تطبیقی مدل خطر و مدل های سنتی برای پیش بینی
ورشکستگی، فصلنامه حسابداری مالی؛ ش 30 ، ص 94 -
121
 واعظ قاسمی، محسن و سعید رمضان پور. ) 1397 (. پیش
بینی ورشکستگی شرکت های پذیرفته شده در سازمان
بورس و اوراق بهادار با استفاده از شبکه عصبی مصنوعی،
فصلنامه دانش سرمایه گذاری، ش 26 ، ص 277 - 296
 وظیفه دوست، حسین و طیبه زنگنه. ) 1394 (. ارائه مدل
پیش بینی ورشکستگی شرکت های تولیدی در بورس اوراق
بهادار تهران مبتنی بر مدل ترکیبی شبکه عصبی، فصلنامه
پژوهش های مدیریت راهبردی، ش 57 ، ص 83 - 100
 فیروزیان، محمود، جاوید، داریوش و نرگس نجم الدینی.
( 1390 کاربرد الگوریتم ژنتیک در پیش بینی « .)
ورشکستگی و مقایسه آن با مدل Z آلتمن در شرکت های
فصلنامه بررسی ،» پذیرفته شده در بورس اوراق بهادار تهران
های حسابداری و حسابرسی، ش 65 ، ص 99 .
 قانون تجارت ایران.
 Analysis for Pareto-Koopmans Efficient Empirical Production Functions", Journal of Econometrics, 30(1-2), 91-107. https://doi.org/10.1016/0304-4076(85)90133-2
 Andersen, P.; Petersen, N.C. (1993). "A procedure for ranking efficient units in data envelopment analysis", Management Science, 39, 1261-1264.
 Banker, R.; Morey, R. (1986). “Efficiency Analysis for Exogenously Fixed Inputs and Outputs”, Operations Research; 34; 513-521.
 Charnes, A.; Cooper, W.W.; Golany, B.; Seiford, L.; Stuz, J. (1985) "Foundations of Data Envelopment  Cielen, A., Peeters, L., & Vanhoof, K. (2004). “Bankruptcy prediction using a data envelopment analysis”. European Journal of Operational Research, Vol. 154, No. 2, pp. 526-532.
 Chen, S., and Xu, S. (2017). “Comparative study of bankruptcy prediction model”, Master Thesis in Finance, Lund University.  Cheng, G., Zervopoulos, P., & Qian, Z. (2013). “A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis”. European Journal of Operational Research, Vol. 225, No. 1, pp. 100-105.
 Cooper, W. W., Seiford, L. M., & Tone, K. (2006). “Introduction to data envelopment analysis and its uses: with DEA-solver software and references”. Springer Science & Business Media.  Feruś, A. (2010). “The application of DEA method in evaluating credit risk of companies”. Contemporary Economics, Vol. 4, No. 4, pp. 107-114.  Horváthová, J., & Mokrišová, M. (2018). “Risk of bankruptcy, its determinants and models”. Risks, Vol. 6, No. 4, pp. 1-22.  Jardin, P. (2009). “Bankruptcy prediction models: How to choose the most relevant variables?” Markets & Investors, Vol. 98, pp. 39-46.
 Liang, D., Lu, C.-C., Tsai, C.-F., Shih, G.-A., (2016). “Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study”. European Journal of Operational Research, Vol. 252, No. 2, pp. 561–572.  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, Vol. 42, pp. 64-75.  Mousavi, M. M., & Ouenniche, J. (2017). “Dynamic Evaluation of Corporate Distress Prediction Models”. Available on: https://www.efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2017-Athens/papers/EFMA2017_0411_fullpaper.pdf.  Mousavi, M. M., & Ouenniche, J. (2018). “Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions”. Annals of Operations Research, Vol. 271, No. 2, pp. 853-886.  Mousavi, M. M., Ouenniche, J., & Tone, K. (2019). “A comparative analysis of two-stage distress prediction models”. Expert Systems with Applications, Vol. 119, pp. 322-341.  Ogachi, D., Ndege, R., Gaturu, P., & Zoltan, Z. (2020). Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya. Journal of Risk and Financial Management, Vol. 13, No. 47, pp. 1-14.
 Piesse, J., Lee, C.-F., Kuo, H.-C., Lin, L., 2006. Corporate failure: Definitions, methods, and failure prediction models, in: Encyclopedia of Finance. Springer, pp. 477– 490.  Waqas, H., & Md-Rus, R. (2018). “Predicting financial distress: Importance of accounting and firm-specific market variables for Pakistan’s listed firms”. Cogent Economics & Finance, Vol. 6, No. 1, pp. 1545739.  Xu, X., & Wang, Y. (2009). “Financial failure prediction using efficiency as a predictor”. Expert Systems with Applications, Vol. 36, No. 1, pp. 366-373.  Yang, X., & Dimitrov, S. (2017). “Data envelopment analysis may obfuscate corporate financial data: using support vector machine and data envelopment analysis to predict corporate failure for nonmanufacturing firms” INFOR: Information
فصلنامه علمی پژوهشی دانش حسابداری و حسابرسی مدیریت – انجمن حسابداری مدیریت ایران
228 دوره 12 / شماره پیاپی 45 / بهار 1402
Systems and Operational Research, Vol. 55, No. 4, pp. 295-311.  Zhu, J. (1996). “Data Envelopment Analysis with Preference Structure”, The Journal of the Operational Research Society, 47(1), 136. DOI: 10.2307/2584258  Cooper W.W., Seiford L.M., Zhu J. (2011) Data Envelopment Analysis: History, Models, and Interpretations. In: Cooper W., Seiford L., Zhu J. (eds) Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 164. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6151-8_1  Portela, M.; Thanassoulis, E.; Simpson, G. (2004). "Negative data in DEA: a directional distance approach applied to bank branches", Journal of the Operational Research Society, 55: 1111-1121.