Analyzing the performance of DEA models for bankruptcy prediction in the energy sector: with emphasis on Dynamic DEA approach

Advances in Mathematical Finance and Applications

مقاله 14، دوره 8، شماره 4، آذر 2023، صفحه 1353-1370   

نوع مقاله: َApplied-Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2023.1967064.1794

نویسندگان

Mohammad Ali Khorami؛ Seyed Babak Ebrahimi^* ؛ Majid Mirzaee Ghazani

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

چکیده

Predicting bankruptcy risk is one of the most critical issues in corporate financial decision-making. Investors always try to predict the bankruptcy of a firm to reduce the risk of losing their assets, so they are looking for ways by which they can predict the risk of bankruptcy. We predict the position of companies active in the oil and gas industry based on their financial health in the 2020 ranking of S&P global up to three years before 2020. This study uses three data envelopment analysis models (CCR, BCC, and DDEA) and the traditional Altman model for forecasting. We have shown that dynamic data envelopment analysis is a powerful tool for predicting bankruptcy risk.

کلیدواژه‌ها

Bankruptcy Risk؛ Data Envelopment Analysis؛ Bankruptcy Prediction Models؛ Dynamic Data Envelopment Analysis

مراجع

[1] Olson, D.L., Delen, D., and Meng, Y., Comparative analysis of data mining methods for bankruptcy prediction, Decision Support Systems, 2012, 52(2), P. 464-473. Doi: 10.1016/j.dss.2011.10.007

[2] Bernstein, P.L., The new religion of risk management. Harvard business review, 1996, 74(2), P. 47.

[3] Aven, T., On some recent definitions and analysis frameworks for risk, vulnerability, and resilience. Risk Analysis: An International Journal, 2011, 31(4), P. 515-522. Doi: 10.1111/j.1539-6924.2010.01528.x

[4] Šotić, A. and R. Rajić, The review of the definition of risk, Online Journal of Applied Knowledge Management, 2015, 3(3), P. 17-26.

[5] Lowrance, W.W., Of accepTable risk: Science and the determination of safety, 1976, Doi: 10.1149/1.2132690

[6] Fetisovová, E., Nagy, L., and Vlachynsky, K., Aktuálne trendy vo financiách malých a stredných podnikov. 2014, Bratislava, Slovak Republic: Vydavateľstvo EKONÓM.

[7] Campbell, S., Determining overall risk. Journal of risk research, 2005, 8(7-8), P.569-581.

 Doi: 10.1080/13669870500118329

[8] Horváthová, J., Mokrišová, M.,  Risk of bankruptcy, its determinants and models. Risks, 2018, 6(4), P. 117, Doi: 10.3390/risks6040117

[9] Agustia, D., Muhammad, N.P.A., and Permatasari, Y., Earnings management, business strategy, and bankruptcy risk: evidence from Indonesia. Heliyon, 2020,  6(2), P. e03317. Doi: 10.1016/j.heliyon.2020.e03317

[10] Campbell, J.Y., Hilscher, J., and Szilagyi, J., In search of distress risk. The Journal of Finance, 2008, 63(6), P. 2899-2939. Doi: 10.1111/j.1540-6261.2008.01416.x

[11] Sánchez, C.P., de Llano Monelos, P., and López, M.R., A parsimonious model to forecast financial distress, based on audit evidence, Contaduría y Administración, 2013, 58(4), P.151-173. Doi:10.1016/S0186-1042(13)71237-3

[12] KORDESTANI, G., TATLI, R., and KOSARI, F.H., The evaluate ability of altman adjusted model to prediction stages of financial distress newton and bankruptcy, 2014.

[13] Altman, E.I., and  Hotchkiss, E., Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt, 2010, 289, John Wiley & Sons. Doi: 10.1002/9781118267806

[14] rostami, m.r., fallahshams, m., eskandari, f., The assessment of financial distress in Tehran stock exchange: a comparative study between data envelopment analysis (DEA) and logistic regression (LR), 2011.

[15] Winakor, A., Smith, S.,  A Test Analysis of Unsuccessful Industrial Companies, Bulletn, nr 31, University of Illinois, za: Korol T., 2013. Nowe podejście do analizy wskaźnikowej w przedsiębiorstwie, 1930.

[16] Fitzpatrick, P.J., A comparison of the ratios of successful industrial enterprises with those of failed companies. 1932.

[17] Merwin, C.L., Financing small corporations in five manufacturing industries, 1926-1936. 1942, National Bureau of Economic Research, New York.

[18] Beaver, W.H., Financial ratios as predictors of failure. Journal of accounting research, 1966, P. 71-111. Doi: 10.2307/2490171

[19] Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 1968, 23(4), P. 589-609. Doi: 10.1111/j.1540-6261.1968.tb00843.x

[20] 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. 207-232. Doi: 10.22034/amfa.2020.1910507.1491

[21] Ohlson, J.A., Financial ratios and the probabilistic prediction of bankruptcy, Journal of accounting research, 1980, P. 109-131. Doi: 10.2307/2490395

[22] West, R.C., A factor-analytic approach to bank condition. Journal of Banking & Finance, 1985, 9(2), P. 253-266. Doi: 10.1016/0378-4266(85)90021-4

[23] Atiya, A.F., Bankruptcy prediction for credit risk using neural networks: A survey and new results, IEEE Transactions on neural networks, 2001, 12(4), P. 929-935. Doi: 10.1109/72.935101

[24] Rezaei, N., Javaheri, M., The Predictability Power of Neural Network and Genetic Algorithm from Fiems’ Financial crisis, Advances in Mathematical Finance and Applications, 2020, 5(2), P.183-196.

Doi: 10.22034/amfa.2019.1863963.1195

[25] Min, J.H., Lee, Y.-C., Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert systems with applications, 2005. 28(4), P. 603-614. Doi: 10.1016/j.eswa.2004.12.008

[26] Jebelli, N., Dadashi, I., and Zare Bahnamiri, M. J., Presenting a New Bankruptcy Prediction Model Based on Adjusted Financial Ratios According to the General Price Index, Advances in Mathematical Finance and Applications, 2021, 6(4), P. 717-732. Doi: 10.22034/AMFA.2020.1884838.1339

[27] Stefko, R., Gavurova, B., Kocisova, K., Healthcare efficiency assessment using DEA analysis in the Slovak Republic, Health economics review, 2018, 8(1), P. 1-12. Doi: 10.1186/s13561-018-0191-9

[28] Lukason, O., Camacho-Miñano, M.-d.-M., Bankruptcy risk, its financial determinants and reporting delays: do managers have anything to hide? Risks, 2019, 7(3), P.77. Doi: 10.3390/risks7030077

[29] Woods, M., Dowd, K.,and Humphrey, C., The value of risk reporting: a critical analysis of value-at-risk disclosures in the banking sector,  International Journal of Financial Services Management, 2008, 3(1), P. 45-64. Doi: 10.1504/IJFSM.2008.016698

[30] Altman, E.I., et al., Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model, Journal of International Financial Management & Accounting, 2017, 28(2), P.131-171. Doi: 10.1111/jifm.12053

[31] Aziz, M.A., Dar, H.A., Predicting corporate bankruptcy: where we stand? Corporate Governance: The international journal of business in society, 2006.

[32] Bărbuță-Mișu, N., and Madaleno, M., Assessment of bankruptcy risk of large companies: European countries evolution analysis, Journal of Risk and Financial Management, 2020, 13(3), P. 58. Doi: 10.3390/jrfm13030058

[33] Beaver, W.H., Alternative accounting measures as predictors of failure, The accounting review, 1968, 43(1), P.113-122.

[34] Horrigan, J.O., A short history of financial ratio analysis, The Accounting Review, 1968, 43(2), P. 284-294.

[35] Mohammadzadeh, A., and Noferesti, M., The Application of Altman & Springate Models In Bankruptcy Prediction of Accepted Companies In Tehran Stock Exchange, 2010.

[36] Altman, E.I.,  and H.J. Suggitt, Default rates in the syndicated bank loan market: A mortality analysis. Journal of Banking & Finance, 2000, 24(1-2), P. 229-253. Doi: 10.1016/S0378-4266(99)00058-8

[37] Altman, E.I., Haldeman, R.G., and Narayanan, P., ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of banking & finance, 1977, 1(1), P. 29-54. Doi: 10.1016/0378-4266(77)90017-6

[38] Zmijewski, M.E., Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 1984, P. 59-82. Doi: 10.2307/2490859

[39] Frydman, H., E.I. Altman, and D.L. Kao, Introducing recursive partitioning for financial classification: the case of financial distress. The journal of finance, 1985, 40(1), P. 269-291.

 Doi: 10.1111/j.1540-6261.1985.tb04949.x

[40] Chen, M.-Y., Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches. Computers & Mathematics with Applications, 2011, 62(12),  P. 4514-4524. Doi: 10.1016/j.camwa.2011.10.030

[41] Odom, M.D., Sharda, R., A neural network model for bankruptcy prediction. in 1990 IJCNN International Joint Conference on neural networks, 1990, IEEE. Doi: 10.1109/IJCNN.1990.137710

[42] Iturriaga, F.J.L., and Sanz, I.P., Bankruptcy visualization and prediction using neural networks: A study of US commercial banks, Expert Systems with applications, 2015, 42(6), P. 2857-2869.

[43] Son, H., et al., Data analytic approach for bankruptcy prediction, Expert Systems with Applications, 2019. 138, P. 112816. Doi: 10.1016/j.eswa.2019.07.033

[44] Balcerzak, A.P., et al., Non-parametric approach to measuring the efficiency of banking sectors in European Union Countries. Acta Polytechnica Hungarica, 2017, 14(7), P. 51-70.

[45] Mousavi, M.M., Ouenniche, J., and Xu, B., Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework, International Review of Financial Analysis, 2015, 42, P.64-75. Doi: 10.1016/j.irfa.2015.01.006

[46] 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

[47] Izadikhah, M., Using goal programming method to solve DEA problems with value judgments, Yugoslav Journal of Operations Research, 2016, 24 (2), P: 1-16, doi: 10.2298/YJOR121221015I

 

[48] Kao, C., Dynamic data envelopment analysis: A relational analysis, European Journal of Operational Research, 2013, 227(2), P. 325-330. Doi: 10.1016/j.ejor.2012.12.012

[49] Moshabbaki, A., Mombeyni, H., Bakhshizadeh, A., Analysis of bankruptcy of companies listed in Stock Exchange by using of two methods of Discriminant Analysis and DEA–Additive analysis, Journal of Investment Knowledge, 2014, 4, P. 85-106.

[50] Li, Z., Crook, J., and Andreeva, G., Dynamic prediction of financial distress using Malmquist DEA, Expert Systems with Applications, 2017, 80, P. 94-106. Doi: 10.1016/j.eswa.2017.03.017.

[51] Štefko, R., Horváthová, J., Mokrišová, M., Bankruptcy prediction with the use of Data Envelopment Analysis: An empirical study of Slovak Businesses, Journal of Risk and Financial Management, 2020, 13(9), P. 212.

Doi: 10.3390/jrfm13090212.

[52] Setiawan, C., predicting financial distress using dea and altman’s model on steel and iron industry indonesia. jurnal manajemen dan bisnis sriwijaya, 2020, 18(1), P. 57-70.

[52] Rahimi, H., Minouei, M., Fathi, M.,  Financial distress of companies listed on the Tehran stock exchange using the dynamic worst practice frontier-based DEA model, Advances in Mathematical Finance and Applications, 2022, 7(2), P. 507-525. Doi: 10.22034/amfa.2021.1931684.1597

[53] Zamani, S., Zanjirdar, M., Lalbar, A. The effect of information disclosure on market reaction with meta-analysis approach. Advances in Mathematical Finance and Applications, 2022, 7(3), P. 629-644. Doi: 10.22034/amfa.2021.1937478.1625

 

[54] Zanjirdar, M., Overview of Portfolio Optimization Models. Advances in Mathematical Finance and Applications, 2020. 5(4), P.419-435. Doi: 10.22034/amfa.2020.674941.

 

[55] Emrouznejad, A., Yang, G.-l.,  A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016,  Socio-economic planning sciences, 2018, 61, P. 4-8. Doi: 10.1016/j.seps.2017.01.008.

[56] Huang, Z., et al., The Empirical Study on the Film's Network Exposure Effectiveness based on Several DEA Models. in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 2018. IEEE. Doi: 10.1109/IAEAC.2018.8577566.

[56] Razipour-GhalehJough, S., et al., Benchmarking bank branches: A dynamic DEA approach. Journal of Information and Optimization Sciences, 2021, P. 1-34. Doi: 10.1080/02522667.2020.1833441

[57] Mavi, R.K., and Mavi, N.K., National eco-innovation analysis with big data: A common-weights model for dynamic DEA, Technological Forecasting and Social Change, 2021, 162, P. 120369.

[58] Kao, C., and Hwang, S.-N., Efficiency measurement for network systems: IT impact on firm performance, Decision Support Systems, 2010, 48(3), P.437-446. Doi: 10.1016/j.dss.2009.06.002.

[59] Kao, C., Efficiency decomposition and aggregation in network data envelopment analysis, European Journal of Operational Research, 2016, 255(3), P. 778-786. Doi: 10.1016/j.ejor.2016.05.019.

[60] Lim, D.J., Kim, M.S., and Lee, K.W., A revised dynamic data envelopment analysis model with budget constraints, International Transactions in Operational Research, 2020. Doi: 10.1111/itor.12810.