Financial Distress of Companies Listed on the Tehran Stock Exchange using the Dynamic Worst Practice Frontier-based DEA Model

محورهای موضوعی : Financial and Economic Modelling

Hamid Rahimi ¹ (Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Mehrzad Minouei ² (Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
MohammadReza Fathi ³ (Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran)

تاریخ دریافت : 1400/03/05 تاریخ پذیرش : 1400/06/20 تاریخ انتشار : 1401/01/12

کلید واژه: Improvement Solutions, and Exchange Organization, Securities, Worst-Practice-Frontier DEA, Financial Distress,

چکیده مقاله :

One of the main concerns of financial institutions for investing in companies is to evaluate financial performance and, most importantly, the financial distress of organizations applying for investment. Therefore, various approaches and tech-niques are used in this evaluation. Financial decision-making has always been associated with the risk of uncertainty. One way to help investors is to provide forecasting models for the overall corporate prospect. It is noteworthy that in all these approaches, various criteria are used to identify corporate financial distress. In this study, a dynamic worst-practice-frontier DEA model was used to identify financially distressed decision-making units over several time-periods. Another feature of the model presented in this study was to provide some improvement solutions for financially distressed decision-making units. Finally, a new ranking approach was introduced to evaluate companies based on the inefficiency trend over several time-periods. The study's approach provides decision-makers with the ability to evaluate the inefficient DMUs during each time-period according to the relationships between these time-periods. The efficiency slope can also be evaluated over time-periods, and companies can be ranked based on this slope. Finally, it is suggested to use this model to dynamically predict financial distress in various industries, including metals, rubber, automobiles, etc., so that compa-nies are informed of their financial distress promptly and take appropriate measures to prevent bankruptcy.

منابع و مأخذ:

References

 

[1] Altman, E., Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy, The journal of finance, 1968, 4(23), P.589-609. Doi: 10.2307/2978933.

[2] Badami, M.h., Predicting corporate financial distress using the ELECTRE TRI method in Tehran Stock Exchange, The University of Tehran, Faculty of Management, Tehran, M.Sc. Thesis, 2014, P.65-66. (in Persian)

[3] Banker, R. D., Charnes, A., Cooper, W. W., Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 1984, 30(9), P. 1078-1092.

 Doi: 10.1287/mnsc.30.9.1078.

[4] Charnes, A., Cooper, W. W., Rhodes, E.,  Measuring the efficiency of decision-making units, European journal of operational research, 1978, 2(6), P. 429-444. Doi: 10.1016/0377-2217(78)90138-8.

[5] Charnes, A., Cooper, W. W., Golany, B., Seiford, L., Stutz, J., Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions, Journal of econometrics, 1985, 30(1), P. 91-107.

 Doi: 10.1016/0304-4076(85)90133-2.

[6] Chen, C., Van Dalen, H., Measuring dynamic efficiency: Theories and an integrated methodology, European Journal of Operational Research, 2010, 3(203), P.749-760. Doi: 10.1016/j.ejor.2009.09.001.

[7] Chen, J., Developing SFNN models to predict financial distress of construction companies, Expert Systems with Applications, 2012, 1(39), P. 823-827. Doi: 10.1016/j.eswa.2011.07.080.

[8] Chen, Y., Liang, L., Zhu, J. Equivalence in two-stage DEA approaches, European Journal of Operational Research, 2009, 2(193), P.600-614. Doi: 10.1016/j.ejor.2007.11.040.

[9] Cooper, W. W., Park, K. S., Pastor, RAM, J. T., a range adjusted measure of inefficiency for use with additive models and relations to other models and measures in DEA. Journal of Productivity Analysis, 1999, 11(1), P. 5-42. Doi:10.1023/A:1007701304281.

[10] Cooper, W., Seiford, L., Toone, K., Introduction to data envelopment analysis and its uses: with DEA-solver software and references, Springer Science and Business Media, 2006, 5(2), P. 125-139.

[11] Deprins, D., Simar, L., Measuring Labor Efficiency in Post Offices, In Marchand M., P. Pestieau and H. Tulkens (eds.), The Performance of Public Enterprises: Concepts and Measurements, 1984. Doi: 10.1007/978-0-387-25534-7_16.

[12] Ebrahimi Kordlar, J., Jafarzadeh, A., Aliahmadi, MH., Proposing a Model of Efficiency Evaluation Based on the Adjusted Range Measurement Model and Weight Restrictions (Case Study: Branches of Iran Insurance Company), Industrial Management Journal, 2018, 10(2), P. 161-182 (in Persian).

Doi: 10.22059/IMJ.2018.262152.1007463.

[13] Fallahpour, S., Eram, A. Predicting Companies Financial Distress by Using Ant Colony Algorithm, Financial Research Journal, Tehran: The University of Tehran, Faculty of Management, 2016, 2(18), P. 347-368 (in Persian).

[14] Färe R, Lovell CAK. Measuring the technical efficiency of production, J Econ Theory 19, 1978, P.150–162.

[15] Farrell, M. J., The measurement of productive efficiency, Journal of the Royal Statistical Society Series A (General), 1957, 120, P. 253–290. Doi: 10.2307/3439197.

[16] Gordon, M. J. Towards a theory of financial distress. The Journal of Finance, 1971, 26(2), P. 347-356.

Doi: https://doi.org/10.2307/2326050.

 [17] Heydary Farahany, M., ghayour, F., mansourfar, G., The effect of management behavioral strains on financial distress, Journal of Financial Accounting Research, 2019, 3(11), P. 117-134.

 Doi: 10.22108/FAR.2020.119602.1534.

[18] Izadikhah, M., Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model, Advances in Mathematical Finance and Applications, 2021, 6(2), P.1-30. Doi: 10.22034/amfa.2020.1896273.1399.

[19] Jebelli, N., Dadashi, I., Zare Bahnamiri, MJ., Presenting a New Bankruptcy Prediction Model Based on Adjusted Financial Ratios According to the General Price Index, Advances in Mathematical Finance and Applications, 2020, Doi: 10.22034/AMFA.2020.1884838.1339.

[20] Khajavi, S., Ghadirian-Arani, M., The role of managerial ability in financial distress prediction, Journal of Financial Accounting Research, 2019, 9(4), P.83-102. Doi: 10.22108/far.2018.107709.1172.

[21] Li, Z., Crook, J., Andreeva, G., Dynamic prediction of financial distress using Malmquist DEA, Expert Systems with Applications, 2017, 6(80), P.9. Doi: 10.1016/j.eswa.2017.03.017.

[22] Liu, F. H. F., Chen, C. L., The worst-practice DEA model with slack-based measurement. Computers and Industrial Engineering, 2009, 57(2), P.496-505. Doi: 10.1016/j.cie.2007.12.021.

[23] Mehrani, S., Mehrani, K., Monsefi, Y., Karami, Gh., A Practical Study of Zmijewski and Shirata Bankruptcy Prediction Models in Companies Listed on the Tehran Stock Exchange, Journal of Accounting and Auditing Reviews, 2005, 5, P. 105-131. (in Persian). Doi: 10.1016/j.ribaf.2016.02.001.

[24] Mehregan, MR., Safari, H., Jafarzadeh, AH., Efficiency evaluation of Iran Insurance Company using Data Envelopment Analysis, Financial Research Journal, 2016, 17(2), P.393-414. (in Persian).

 Doi: 10.22059/JFR.2015.57318.

[25] Megginson, W., Meles, A., Sampagnaro, G., Verdoliva, V., Financial distress risk in initial public offerings: how much do venture capitalists matter?, Journal of Corporate Finance, 2019, 25(1), P.10-30.

 Doi: 10.1016/j.jcorpfin.2016.09.007.

 [26] Mirarab BAagi, S.A., Mokari, H., Azariyon, A., Dynamic forecasting of financial bankruptcy using the Malm Quest method (Case Study: companies listed on the Tehran Stock Exchange), Karafan.2020,  17, P. 203-220. (In Persian).

[27] Moradi, M., Shafiei Sardasht, M., Ebrahimpour, M. Financial distress prediction using support vector machine models and multiple discriminate analysis. Quarterly Journal Stock Exchange, 2012, 18(5), P. 113-136. (in Persian). Doi: 10.22059/JFR.2016.61596.

[28] Mousavi, M., Ouenniche, J., Tone, K. A comparative analysis of two-stage distress prediction, Expert Systems with Applications, 2019, 119, P. 322-341. Doi: 10.1016/j.eswa.2018.10.053.

[29] Osama, E. A., Bassam, L., Predicting Financial Distress for Listed MENA Firms, International Journal of Accounting and Financial Reporting, 2019, 2(9), P. 51-75. Doi: 10.5296/ijafr.v9i2.14542.

[30] Paradi, Joseph C., Mette Asmild, and Paul C., Simak., Using DEA and worst practice DEA in credit risk evaluation, Journal of Productivity Analysis, 2004, 21(2), P. 153-165.

 Doi: 10.1023/B:PROD.0000016870.47060.0b.

[31] Parkan, C., Wang, Y.M., Worst Efficiency Analysis Based on Inefficient Production Frontier, Information Sciences, 2000, 175, P. 20-29.

[32] Raei. R., Fallahpour, S., Corporate financial distress prediction using artificial neural networks, Journal of Financial Research. 2004, 17, P. 39-69. (in Persian)

[33] Raei R., Fallahpour, S. Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios, Accounting and Auditing Review, 2008, 4(15), P. 17-34 (in Persian).

[34] Rahimi, H., Minouei, M., Fathi, M.R., Explaining the effective financial variables in predicting financial distress: the use of artificial neural network, Iranian Rubber Industry, 2021, 25(101), P. 65-84. (In Persian).

[35] Rahimi, H., Minouei, M., Fathi, M.R., Forecasting the financial distress of companies listed on the Tehran Stock Exchange using DEA-DA and artificial neural network, Karafan, Accepted. 2021, (In Persian).

[36] Ramezanzadeh Zeidi, A., Faghani Makarani, K., Jafari, A., The Role of Earnings Management in Theoretical Development and Improving the Efficiency of Accounting-Based Financial Distress Prediction Models, Advances in Mathematical Finance and Applications, 2021 Jul 1, 6(3), P.1-8. Doi: 10.22034/AMFA.2019.1879394.1300.

[37] Tone, K., A slacks-based measure of efficiency in data envelopment analysis, European journal of operational research, 2001, 130(3), P.498-509. Doi: 10.1016/S0377-2217(99)00407-5.

[38] Tsai, C., Feature selection in bankruptcy prediction. Knowledge-Based Systems, 2009, 2(22), P. 120-127. Doi: 10.1016/j.knosys.2008.08.002.

[39] Vadiei, MH., Miresmaeili, SH., Bankruptcy Prediction Using Ohlson Logit Analysis Models and Fulmer Multiple Discriminant Analysis and Comparison, Journal of Accounting and Auditing Research, 2012, 2, P. 172-164. (in Persian)

[40] Xu, z., Wang., Financial failure prediction using efficiency as a predictor, Expert Systems with Applications, 2009, 2(22), P. 366-373. Doi: 10.1016/j.eswa.2007.09.040.