Araştırma Makalesi
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A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES

Yıl 2022, Cilt: 20 Sayı: 3, 35 - 58, 08.10.2022
https://doi.org/10.11611/yead.1100824

Öz

Due to the Covid-19 epidemic, there was a significant increase in company bankruptcies in 2020. In this period, especially the energy sector has been one area where bankruptcies are the most seen. In this context, this study aims to build a model that can predict financially unsuccessful companies that have declared bankruptcy and successful companies operating in the energy sector in the U.S.A. For the study sample, 30 financial ratios of 23 energy companies that declared bankruptcy in the U.S.A. in 2020 and 30 financial ratios of 23 energy companies that were financially successful in the same peri-od were selected. The multiple discriminant analysis (M.D.A.) was chosen to differentiate between the groups. According to the research results, the accuracy rate of the created function was found to be 87.0%. According to the sensitivity and specificity (R.O.C.) results, testing the process’s performance to differ between unsuccessful and successful companies was found to be strong

Kaynakça

  • Akgüç, Ö. (2010). Mali Tablolar Analizi. Istanbul: Avcıol Publishing.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933.
  • Altman, E.I., & Narayanan, P. (1997). An International Survey of Business Failure Classification Models. Financial Markets, Institutions & Instruments, 6(2), 1-57.
  • Altman, E.I., Haldeman, R.G., & Narayanan, P. (1977). Zetatm analysis A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6.
  • Aly, I.M., Barlow, H.A., & Jones, R.W. (1992). The Usefulness of SFAS No. 82 (Current Cost) Information in Discriminating Business Failure: An Empirical Study. Journal of Accounting, Auditing & Finance, 7(2), 217–229. https://doi.org/10.1177/0148558X9200700209
  • Atiya, A.F., (2001). Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results, IEEE Transactions on Neural Networks, 12 (4): 929-935. (Accessed: 16.09.2014).
  • Aziz, A., Emanuel, D.C. and Lawson, G.H. (1988). Bankruptcy prediction – an investigation of cash flow-based models. Journal of Management Studies, 25(5), 419-37. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x
  • Back, B., Laitinen T., Sere K. (1996). Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications, 11(4), 407-413. https://doi.org/10.1016/S0957-4174(96)00055-3
  • Ballard, D.J., Strogatz, D.S., Wagner, E.H., Siscovick, D.S., James, S.A., Kleinbaum, D.G. & Ibrahim, M.A. (1988). Hypertension control in a rural southern community: medical care process and dropping out. American Journal of Preventive Medicine, 4 (3), 133-139.
  • Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
  • Bellovary, J., Giacomino, D. & Akers, M. (2006). A review of bankruptcy prediction studies: 1930 to present. Accounting Faculty Research and Publications, 33.
  • Berk, N. (1998). Finansal Yönetim. İstanbul: Türkmen Kitabevi.
  • Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 12(1), 1-25. https://doi.org/10.2307/2490525
  • Büyüköztürk, Ş. (2006). Sosyal bilimler için veri analizi elkitabı. Ankara: PegemA Yay.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: istatistik, araştırma deseni, SPSS uygulamaları ve yorum. Ankara: Pegem Akademi
  • Charitou, A., Neophytou, E. & Charalambous, C. (2004). Predicting Corporate Failure: Empirical Evidence for the U.K., European Accounting Review, 13(3), 465-497. https://doi.org/10.1080/0963818042000216811
  • Cho, M. (1994). Predicting business failure in the hospitality industry: An application of logit model (PhD Thesis), Polytechnic Institute and State University, Virginia.
  • Çolak, M. S. (2020). A new multivariate approach for assessing corporate financial risk using balance sheets, Borsa Istanbul Review, 21(3), 239-255. https://doi.org/10.1016/j.bir.2020.10.007.
  • Dayı, F. (2019). Vadesi Geçen Ticari Alacakların Net Kâra Etkisinin İncelenmesi: Borsa İstanbul’da Bir Uygulama. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9(2), 467-486.
  • Deakin, E. (1972) A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167-179. https://doi.org/10.2307/2490225.
  • Demir, E., Saatcioğlu, Ö. & İmrol, F. (2016). Uluslararası dergilerde yayımlanan eğitim araştırmalarının normallik varsayımları açısından incelenmesi, Current Research in Education, 2(3), 130 148.
  • Demireli, E. (2004). Alacak yönetiminde finans tekniği olarak faktöring yöntemi ve uygulaması, Yayımlanmamış Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü, İzmir.
  • Dietrich, Jk, & Sorensen, E. (1984). An application of logit analysis to prediction of merger targets. Journal of Business Research, 12 (3), 393-402. https://doi.org/10.1016/0148-2963(84)90020
  • Dimitras, Ai, Zanakis, Sh, & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90 (3), 487-513. https://doi.org/10.1016/0377-2217(95)00070-4
  • Dugan, M.T., Christine V. Zavgren. (1989, May). How a bankruptcy model could be incorporated as an analytical procedure. The C.P.A. Journal, 59(5), 64-65.
  • Edmister, R. (1972). An empirical test of financial ratio analysis for small business failure prediction, Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2329929.
  • Ertan, A. S. & Ersan, Ö. (2019). Finansal başarısızlığı belirleyen etkenler: Türkiye imalat sektörü örneği. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 40(2), 181-207.
  • Ezzamel, M., Cecilio Mm, & Alistair B., (1987). On the distributional properties of financial ratios. Journal of Business Finance and Accounting, 14, 463–81. https://doi.org/10.1111/j.1468-5957.1987.tb00107.x
  • Fitzpatrick, P.J. (1932). A comparison of ratios of successful industrial enterprises with those of failed firm. Certified Public Accountant, 6, 727-731.
  • Fu, M., & Shen, H. (2020). Covid-19 and corporate performance in the energy industry. Energy Research Letters, 1 (1). https://doi.org/10.46557/001c.12967.
  • Fulmer, J.G., Moon, J.E., Gavin, T.A.& Erwin, M.J. (1984). A bankruptcy classification model for small firms, Journal of Commercial Bank Lending, 66(11), 25-37.
  • Garcia-Gallego, A. & Mures-Quintana, M.J. (2012). Business failure prediction models: Finding the connection between their results and the sampling method. Economic Computation and Economic Cybernetics Studies and Research, 3, 157-168.
  • Gentry, J. & Newbold, P. & Whitford, D. (1987). Funds flow components, financial ratios, and bankruptcy. Journal of Business Finance & Accounting, 14(4), 595-606. https://doi.org/10.1111/j.1468-5957.1987.tb00114.x.
  • Grice, J.S., & Michael T.D. (2001). The limitations of bankruptcy prediction models: some cautions for researchers. Review of Quantitative Finance and Accounting, 17, 151–66. https://doi.org/10.1023/A:1017973604789.
  • Gu, Z. & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37–49. https://doi.org/ 10.1177/146735840000200108
  • Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: a multiple discriminant model. International Journal of Hospitality Management, 21. 25-42. https://doi.org/10.1016/S0278-4319(01)00013-5.
  • Hermes, E. (2021). İflaslar geri geliyor. Retrieved from https://www.eulerhermes.com/tr_TR/ekonomik-arastirmalar/ekonomik-gorunum-raporlari/iflaslar-geri-geliyor.html Accessed November 20, 2021.
  • Islam, Md. S. (2020). Predictive capability of financial ratios for forecasting of corporate bankruptcy. IOSR Journal of Business and Management (IOSR-JBM), 22(6), 13-57. https://doi.org/10.2139/ssrn.3637184.
  • Karels, G.V., & Prakash, A.J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14 (4), 573-593. https://doi.org/10.1111/j.1468-5957.1987.tb00113.x.
  • Kliestik, T. & Vrbka, J. & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium, 13, 569-593. https://doi.org/10.24136/eq.2018.028.
  • Knox, K., Blankmeyer, E., Trinidad, J., & Stutzman, J. (2009). Predicting bankruptcy in the Texas nursing facility industry. The Quarterly Review of Economics and Finance, 49(3), 1047-1064. https://doi.org/10.1016/j.qref.2008.08.004.
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  • Mihalovič, M. (2016), Performance Comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics and Sociology, 9(4), 101-118. https://doi.org/10.14254/2071-789X.2016/9-4/6.
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ENERJİ FİRMALARI İÇİN İFLAS TAHMİN MODELLEMESİ

Yıl 2022, Cilt: 20 Sayı: 3, 35 - 58, 08.10.2022
https://doi.org/10.11611/yead.1100824

Öz

Covid-19 salgını nedeniyle 2020 yılında şirket iflaslarında büyük artışlar yaşanmıştır. Bu dönemde özellikle enerji sektörü, en çok iflasların görüldüğü alanlardan bir tanesi olmuştur. Bu bağlamda bu çalışmanı amacı, ABD’de de enerji sektöründe faaliyet gösteren finansal açıdan başarısız firmalar (iflasını açıklamış firmalar) ile başarılı firmaları önceden tahmin edebilen bir model oluşturmaktır. Çalışmanın örneklemi için 2020 yılında ABD’de iflasını açıklayan 23 adet enerji şirketi ve aynı dönem-de finansal açıdan başarılı olan 23 adet enerji şirketi seçilmiştir. Gruplar arasındaki ayrımı yapmak için çoklu ayırma analizi metodu yöntem olarak seçilmiştir. Araştırmanın sonuçlarına göre, oluşturulan fonksiyonun başarı oranı %87,0 olarak bulunmuştur. Duyarlılık ve özgüllük testi sonuçlarına göre fonksiyonun başarısız ve başarılı şirketleri tespit etme performansı güçlü olarak bulunmuştur.

Kaynakça

  • Akgüç, Ö. (2010). Mali Tablolar Analizi. Istanbul: Avcıol Publishing.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933.
  • Altman, E.I., & Narayanan, P. (1997). An International Survey of Business Failure Classification Models. Financial Markets, Institutions & Instruments, 6(2), 1-57.
  • Altman, E.I., Haldeman, R.G., & Narayanan, P. (1977). Zetatm analysis A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6.
  • Aly, I.M., Barlow, H.A., & Jones, R.W. (1992). The Usefulness of SFAS No. 82 (Current Cost) Information in Discriminating Business Failure: An Empirical Study. Journal of Accounting, Auditing & Finance, 7(2), 217–229. https://doi.org/10.1177/0148558X9200700209
  • Atiya, A.F., (2001). Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results, IEEE Transactions on Neural Networks, 12 (4): 929-935. (Accessed: 16.09.2014).
  • Aziz, A., Emanuel, D.C. and Lawson, G.H. (1988). Bankruptcy prediction – an investigation of cash flow-based models. Journal of Management Studies, 25(5), 419-37. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x
  • Back, B., Laitinen T., Sere K. (1996). Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications, 11(4), 407-413. https://doi.org/10.1016/S0957-4174(96)00055-3
  • Ballard, D.J., Strogatz, D.S., Wagner, E.H., Siscovick, D.S., James, S.A., Kleinbaum, D.G. & Ibrahim, M.A. (1988). Hypertension control in a rural southern community: medical care process and dropping out. American Journal of Preventive Medicine, 4 (3), 133-139.
  • Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
  • Bellovary, J., Giacomino, D. & Akers, M. (2006). A review of bankruptcy prediction studies: 1930 to present. Accounting Faculty Research and Publications, 33.
  • Berk, N. (1998). Finansal Yönetim. İstanbul: Türkmen Kitabevi.
  • Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 12(1), 1-25. https://doi.org/10.2307/2490525
  • Büyüköztürk, Ş. (2006). Sosyal bilimler için veri analizi elkitabı. Ankara: PegemA Yay.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: istatistik, araştırma deseni, SPSS uygulamaları ve yorum. Ankara: Pegem Akademi
  • Charitou, A., Neophytou, E. & Charalambous, C. (2004). Predicting Corporate Failure: Empirical Evidence for the U.K., European Accounting Review, 13(3), 465-497. https://doi.org/10.1080/0963818042000216811
  • Cho, M. (1994). Predicting business failure in the hospitality industry: An application of logit model (PhD Thesis), Polytechnic Institute and State University, Virginia.
  • Çolak, M. S. (2020). A new multivariate approach for assessing corporate financial risk using balance sheets, Borsa Istanbul Review, 21(3), 239-255. https://doi.org/10.1016/j.bir.2020.10.007.
  • Dayı, F. (2019). Vadesi Geçen Ticari Alacakların Net Kâra Etkisinin İncelenmesi: Borsa İstanbul’da Bir Uygulama. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9(2), 467-486.
  • Deakin, E. (1972) A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167-179. https://doi.org/10.2307/2490225.
  • Demir, E., Saatcioğlu, Ö. & İmrol, F. (2016). Uluslararası dergilerde yayımlanan eğitim araştırmalarının normallik varsayımları açısından incelenmesi, Current Research in Education, 2(3), 130 148.
  • Demireli, E. (2004). Alacak yönetiminde finans tekniği olarak faktöring yöntemi ve uygulaması, Yayımlanmamış Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü, İzmir.
  • Dietrich, Jk, & Sorensen, E. (1984). An application of logit analysis to prediction of merger targets. Journal of Business Research, 12 (3), 393-402. https://doi.org/10.1016/0148-2963(84)90020
  • Dimitras, Ai, Zanakis, Sh, & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90 (3), 487-513. https://doi.org/10.1016/0377-2217(95)00070-4
  • Dugan, M.T., Christine V. Zavgren. (1989, May). How a bankruptcy model could be incorporated as an analytical procedure. The C.P.A. Journal, 59(5), 64-65.
  • Edmister, R. (1972). An empirical test of financial ratio analysis for small business failure prediction, Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2329929.
  • Ertan, A. S. & Ersan, Ö. (2019). Finansal başarısızlığı belirleyen etkenler: Türkiye imalat sektörü örneği. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 40(2), 181-207.
  • Ezzamel, M., Cecilio Mm, & Alistair B., (1987). On the distributional properties of financial ratios. Journal of Business Finance and Accounting, 14, 463–81. https://doi.org/10.1111/j.1468-5957.1987.tb00107.x
  • Fitzpatrick, P.J. (1932). A comparison of ratios of successful industrial enterprises with those of failed firm. Certified Public Accountant, 6, 727-731.
  • Fu, M., & Shen, H. (2020). Covid-19 and corporate performance in the energy industry. Energy Research Letters, 1 (1). https://doi.org/10.46557/001c.12967.
  • Fulmer, J.G., Moon, J.E., Gavin, T.A.& Erwin, M.J. (1984). A bankruptcy classification model for small firms, Journal of Commercial Bank Lending, 66(11), 25-37.
  • Garcia-Gallego, A. & Mures-Quintana, M.J. (2012). Business failure prediction models: Finding the connection between their results and the sampling method. Economic Computation and Economic Cybernetics Studies and Research, 3, 157-168.
  • Gentry, J. & Newbold, P. & Whitford, D. (1987). Funds flow components, financial ratios, and bankruptcy. Journal of Business Finance & Accounting, 14(4), 595-606. https://doi.org/10.1111/j.1468-5957.1987.tb00114.x.
  • Grice, J.S., & Michael T.D. (2001). The limitations of bankruptcy prediction models: some cautions for researchers. Review of Quantitative Finance and Accounting, 17, 151–66. https://doi.org/10.1023/A:1017973604789.
  • Gu, Z. & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37–49. https://doi.org/ 10.1177/146735840000200108
  • Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: a multiple discriminant model. International Journal of Hospitality Management, 21. 25-42. https://doi.org/10.1016/S0278-4319(01)00013-5.
  • Hermes, E. (2021). İflaslar geri geliyor. Retrieved from https://www.eulerhermes.com/tr_TR/ekonomik-arastirmalar/ekonomik-gorunum-raporlari/iflaslar-geri-geliyor.html Accessed November 20, 2021.
  • Islam, Md. S. (2020). Predictive capability of financial ratios for forecasting of corporate bankruptcy. IOSR Journal of Business and Management (IOSR-JBM), 22(6), 13-57. https://doi.org/10.2139/ssrn.3637184.
  • Karels, G.V., & Prakash, A.J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14 (4), 573-593. https://doi.org/10.1111/j.1468-5957.1987.tb00113.x.
  • Kliestik, T. & Vrbka, J. & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium, 13, 569-593. https://doi.org/10.24136/eq.2018.028.
  • Knox, K., Blankmeyer, E., Trinidad, J., & Stutzman, J. (2009). Predicting bankruptcy in the Texas nursing facility industry. The Quarterly Review of Economics and Finance, 49(3), 1047-1064. https://doi.org/10.1016/j.qref.2008.08.004.
  • Legault, J.C.A. & Score, A. (1987). CA-score, a warning system for small business failures, Bilanas, 29-31.
  • Li, A., Wu, J., Liu, Z. (2017). Market manipulation detection based on classification methods. Procedia Computer Science, (122), 788-795. https://doi.org/10.1016/j.procs.2017.11.438.
  • Mirza, N., Rahat, B., Naqvi, B. & Rizvi, Ska (2020). Impact of Covid-19 on corporate solvency and possible policy responses in the E.U. The Quarterly Review of Economics and Finance. https://doi.org/10.1016/j.qref.2020.09.002.
  • Mihalovič, M. (2016), Performance Comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics and Sociology, 9(4), 101-118. https://doi.org/10.14254/2071-789X.2016/9-4/6.
  • Monica-Violeta, A., Codruta, M. & Sorin, B. (2012). A statistical model of financial risk bankruptcy applied for Romanian manufacturing industry. Procedia Economics and Finance, 3, 132–137. https://doi.org/10.1016/S2212-5671(12)001131-1.
  • Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395.
  • Pitrova, K. (2011). Possibilities of the Altman Zeta model application to Czech Firms. E&M Economics and Management, 3.
  • Pongsatat, S., Ramage, J. & Lawrence, H. (2004). Bankruptcy prediction for large and small firms in Asia: a comparison of Ohlson and Altman. Journal of Accounting and Corporate Governance, 1(2), 1-13.
  • Rujoub, M. A., Cook, D. M., & Hay, L. E. (1995). Using cash flow ratios to predict business failures. Journal of Managerial Issues, 7(1), 75-90.
  • Qi, L. (2019). Analysis on zero inventory management of new energy enterprises. I.O.P. Conference Series: Materials Science and Engineering. 677, 032110. https://doi.org/10.1088/1757-899X/677/3/032110.
  • Reuters. (2020). US energy bankruptcy surge continues on credit, oil-price squeeze. Retrieved from https://www.reuters.com/article/us-north-america-oil/us-energy-bankruptcy -surge-continues-on-credit-oil-price-squeeze-idUSKCN25727W Accessed October 29, 2021.
  • Selimoğlu, S. & Orhan, A. (2015). Finansal başarısızlığın oran analizi ve diskriminant analizi kullanılarak ölçümlenmesi: bist’de işlem gören dokuma, giyim eşyası ve deri işletmeleri üzerine bir araştırma. Muhasebe ve Finansman Dergisi, (66), 21-40. https://doi.org/10.25095/mufad.396529
  • Sfakianakis, E. (2021). Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations, 18(2), 166-180. http://dx.doi.org/10.21511/imfi.18(2).2021.14.
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  • Tabachnick, B.G. & Fidell, L.S. (2001). Using multivariate statistics, Fourth Edition. Needham Heights, MA: Allyn & Bacon. ISBN 0-321-05677-9. hardcover.
  • Taffler, R. & Tisshaw, H. (1977). Going Going Gone-four factors which predict. Accountancy, 88(1003), 50-54.
  • Taffler, R.J. (1983). The assessment of company solvent and performance using a statistical model. Accounting and Business Research, 13(52), 295–308. https://doi.org/10.1080/00014788.1983.9729767.
  • Tavlin, E., Moncarz, E., & Dumont, D. (1989). Financial failure in the hospitality industry. F.I.U. Review, 7(1), 55–75.
  • Terzi, S. (2011). Finansal Rasyolar Yardımıyla Finansal Başarısızlık Tahmini: Gıda Sektöründe Ampirik Bir Araştırma. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15 (1).
  • Van Horne, J.C. (1998), Financial management and policy, Prentice Hall, Michigan University.
  • Wieprow, J., Agnieszka G. (2021). The use of discriminant analysis to assess the risk of bankruptcy of enterprises in crisis conditions using the example of the tourism sector in Poland. Risks, 9(78). http://dx.doi.org/10.3390/ risks9040078.
  • Wong, J.M.W., Thomas, N.G. S. (2010, April), Company failure in the construction industry: a critical review and a future research agenda, XXIV Fig. International Congress, Sydney, Australia.
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans
Bölüm Makaleler
Yazarlar

Gerçek Özparlak 0000-0002-8503-3199

Yayımlanma Tarihi 8 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 20 Sayı: 3

Kaynak Göster

APA Özparlak, G. (2022). A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES. Journal of Management and Economics Research, 20(3), 35-58. https://doi.org/10.11611/yead.1100824
AMA Özparlak G. A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES. Journal of Management and Economics Research. Ekim 2022;20(3):35-58. doi:10.11611/yead.1100824
Chicago Özparlak, Gerçek. “A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES”. Journal of Management and Economics Research 20, sy. 3 (Ekim 2022): 35-58. https://doi.org/10.11611/yead.1100824.
EndNote Özparlak G (01 Ekim 2022) A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES. Journal of Management and Economics Research 20 3 35–58.
IEEE G. Özparlak, “A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES”, Journal of Management and Economics Research, c. 20, sy. 3, ss. 35–58, 2022, doi: 10.11611/yead.1100824.
ISNAD Özparlak, Gerçek. “A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES”. Journal of Management and Economics Research 20/3 (Ekim 2022), 35-58. https://doi.org/10.11611/yead.1100824.
JAMA Özparlak G. A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES. Journal of Management and Economics Research. 2022;20:35–58.
MLA Özparlak, Gerçek. “A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES”. Journal of Management and Economics Research, c. 20, sy. 3, 2022, ss. 35-58, doi:10.11611/yead.1100824.
Vancouver Özparlak G. A NEW BANKRUPTCY FORECAST MODELLING FOR ENERGY COMPANIES. Journal of Management and Economics Research. 2022;20(3):35-58.