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MAKİNE ÖĞRENME YÖNTEMLERİYLE KURUMSAL İFLASLARIN TAHMİN EDİLMESİ: ABD ÖRNEĞİ

Year 2022, , 1007 - 1031, 28.12.2022
https://doi.org/10.17130/ijmeb.1079688

Abstract

2019 yılı sonunda ortaya çıkan Covid-19 salgını ile ilgili beraber uluslararası hastanelerin ve sağlık kurumlarının birçoğu ciddi mali zorluklarla karşı karşıya kalmışlardır. Önümüzdeki yıllarda, bu mali zorlukların daha fazla sayıda iflasa sebep olacağı düşünülmektedir. Şirket iflaslarının önceden tahmin edilebilmesi şirket ortaklarını, yatırımcılarını, şirket alacaklılarını ve sağlık sektörünün devamlılığını korumak için önemlidir. Bu alandaki doğru tahminler sayesinde şirket yöneticileri gerekli tedbirleri alarak şirket iflaslarını önleyebilir ve yatırımcılar da zararlarını sınırlandırabilir. Bu çalışmanın amacı, yapay sinir ağlarını (YSA) kullanarak sağlık sektöründeki iflasları tahmin edebilen bir model oluşturmaktır. Bu çalışmanın örneklemi için, ABD’de 01.01.2018 ile 31.12.2020 tarihleri arasında sağlık sektöründe bulunan 23 adet iflasını açıklayan şirket ve kontrol grubu olarak da aynı dönemde ve aynı sektörde bulunan fakat finansal bir sıkıntısı bulunmayan 23 adet şirket seçilmiştir. Bu firmalara ait olan 30 adet finansal oran, araştırmanın girdi verisi olarak kullanılmıştır. Çalışmada, yapay sinir ağları (YSA) yöntem olarak seçilmiştir. Araştırmanın sonuçlarına göre, eğitim seti verisi kullanılarak oluşturulan yapay sinir ağları modellerinin doğru sınıflandırma oranı %100 olarak gerçekleşmiştir. Test seti verisi kullanılarak oluşturulan yapay sinir ağları modellerinin, doğru sınıflandırma oranı %90 olarak gerçekleşmiştir. Araştırma sonuçlarına göre, YSA’lar yüksek sınıflandırma başarıları ve kullanım kolaylıkları ile şirket iflaslarının tahmini için gelecek vaat etmektedirler. Bu nedenle hem araştırmacılar hem de yatırımcılar için kullanılmaları tavsiye edilmektedir.

References

  • Aktaş, R. (1997). Mali başarısızlık (işletme riski) tahmin modelleri. Ankara:Türkiye İş Bankası Kültür Yayınları.
  • Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23(4), 589–609.
  • Altman, E. I. & Narayanan, P. (1997). An international survey of business failure classification models. Financial Markets, Institutions & Instruments, 6(2), 1-57.
  • Altman, E. & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. Hoboken, N.J.: Wiley. https://doi.org/10.1002/9781118267806
  • Altman, E., Marco, G. & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505-529. https://doi.org/10.1016/0378-4266(94)90007-8
  • Aydın, N., Başar, M., Ve Coşkun, M. (2010), Finansal Yönetim,1. Baskı, Eskişehir: Detay Yayıncılık.
  • 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.
  • Ballard, D. J., Strogatz, D. S., Wagner, E. H., Sıscovick, 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.
  • Bazzoli, G. & Andes, S. (1995). Consequences of hospital financial distress. Journal of Healthcare Management, 1995. 40(4), 472.
  • Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
  • Bruynseels, L., & Willekens, M. (2012). The effect of strategic and operating turn around initiatives on audit reporting for distressed companies. Accounting, Organizations and Society, 27(4), 223-241. https://doi.org/10.1016/j.aos.2012.03.001
  • 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 UK, European Accounting Review, 13(3), 465-497.
  • Cho, M. (1994). Predicting business failure in the hospitality industry: An application of logit model (Phd), Polytechnic Institute and State University, Virginia.
  • Cleverley and P.C. Nutt, The decision process used for hospital bond rating--and its implications. Health Services Research, 1984. 19(5), 615-637.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
  • Davalos, S. & Gritta, R. D. & Adrangi, B. & Goodfriend, J., 2005. "The Use of a Genetic Algorithm in Forecasting Air Carrier Financial Stress and Insolvency," 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 208166, Transportation Research Forum.
  • Demir, G., Teker, S., (2019). Postponing bankruptcy and debt restructuring. PressAcademia Procedia (PAP), v9, 278-284
  • Dietrich, J. K., & Sorensen, E. (1984). An application of logit analysis to prediction of merger targets. Journal of Business Research, 12(3), 393-402.
  • Fletcher, D., & Goss, E. (1993). Forecasting with neural networks. Information & Management, 24(3), 159-167. https://doi.org/10.1016/0378-7206(93)90064-Z
  • Fulmer, J. G., Moon, J. E., Gavın, T. A. & Erwın, 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.
  • Gissel, J. L.; Giacomino, D.; and Akers, M. D., (2007) "A Review of Bankruptcy Prediction Studies: 1930-Present" (2007). Accounting Faculty Research and Publications. 25.
  • Grima, S., Dallı Gonzı, R., Thalassinos, I.E. 2020. The Impact of COVID-19 on Malta and its Economy and Sustainable Strategies. Available at SSRN: https://ssrn.com/abstract=3644833 http://dx.doi.org/10.2139/ssrn.3644833
  • Grice, J.S. and M.T. Dugan, Re-estimations of the Zmijewski and Ohlson bankruptcy prediction models. Advances in Accounting, 2003. 20, 77-93.
  • Grover, J., & Lavin, A. (2001). Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy: a Service Industry Extension of Altman’s Z-Score Model of Bankruptcy Prediction. Working Paper. Southern Finance Association Annual Meeting.
  • Gu, Z. & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37–49. http://dx.doi.org/10.1177/146735840000200108
  • Karels, G. V., & Prakash, A. J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14(4), 573-593.
  • Kaufman Hall (2021). American Hospital Association- National Hospital Flash Report Retrieved November 28, 2021, from https://www.kaufmanhall.com/consulting-services/national-hospital-flash-report.
  • Khan, S., Rabbani, R.M., Thalassinos, I.E., Atif, M. 2020. Corona Virus Pandemic Paving Ways to Next Generation of Learning and Teaching: Futuristic Cloud-Base Educational Model. Available at SSRN: https://ssrn.com/abstract=3669832.
  • Kınay, B. (2010). Ordered Logit Model approach for the determination of financial distress. Numéro Spécial, 119.
  • Kutlu B.Ve Badur B. (2009). Yapay sinir ağları ile borsa endeksi tahmini. Yönetim Dergisi:İstanbul Üniversitesi İşletme Fakültesi İşletme İktisadı Enstitüsü, 20(63), 25 - 40.
  • Landis, J. R. Ve Koch, G. G. (1977). The measurement of observer agreement for categorical data, Biometrics, 33, 159-174
  • Lee, K., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18(1), 63-72. https://doi.org/10.1016/0167-9236(96)00018-8
  • Mihalovič, M. (2016), Performance Comparison of multiple discriminant analysis and logit models in bankruptcy prediction, Economics and Sociology, 9(4), 101-118. http://dx.doi.org/10.14254/2071-789X.2016/9-4/6
  • Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109.
  • Özparlak, G. (2021). Finansal Tablo Manipülasyonlarının Tespitinde Yapay Sinir Ağlarının Kullanılması . Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (60), 331-357. http://dx.doi.org/10.18070/erciyesiibd.96146
  • Pan, W. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. https://doi.org/10.1016/j.knosys.2011.07.001
  • Pitrova, K. (2011). Possibilities of the Altman Zeta model application to Czech Firms. E&M Economics and Management, 3.
  • Puro, N., Borkowski, N., Hearld, L.R., Carroll, N.W., Byrd, J.D., Smith, D.G., & Ghiasi, A. (2019). Financial Distress and Bankruptcy Prediction: A Comparison of Three Financial Distress Prediction Models in Acute Care Hospitals. Journal of health care finance.
  • Richards, C.A., The effect of hospital financial distress on immediate breast reconstruction. 2014: Columbia University.
  • Ross, S., Westerfield, R., & Jaffe, J. (1999). Corporate finance (second ed.). Homewood IL: Irwin.
  • 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. 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.
  • Shi, Y., ve Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital, 15(2), 114-127. https://doi.org/10.3926/ic.1354
  • Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm: a discriminant analysis, (Yüksek Lisans Tezi), Simon Fraser Üniversitesi, Kanada.
  • Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modelling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006
  • Taffler, R., & Tisshaw H. (1977). Going, going, gone – four factors which predict. Accountancy, March, 50-54.
  • Tam, K., & Kiang, M. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38(7), 926-947. https://doi.org/10.1287/mnsc.38.7.926
  • United States Courts. (t.y). Chapter 11–Bankruptcy Basics. Retrieved December 12, 2021, from https://www.uscourts.gov/services-forms/bankruptcy/bankruptcy-basics/chapter-11-bankruptcy-basics.
  • United States Courts. (t.y). Chapter 7–Bankruptcy Basics. Retrieved December 12, 2021, from https://www.uscourts.gov/services-forms/bankruptcy/bankruptcy-basics/chapter-7-bankruptcy-basics.
  • 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). https://doi.org/10.3390/ risks9040078.
  • Wilson, R., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557. https://doi.org/10.1016/0167-9236(94)90024-8
  • Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting and Economics, 6(1), 34–45.
  • Yavuz, S. Ve Deveci, M. (2012). İstatiksel normalizasyon tekniklerinin yapay sinir ağın performansına etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 40, 167-187.
  • Zhang, G., Y.M, Hu, Patuwo, B.E., Indro, D.C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis, European Journal of Operational Research, 116(1). https://doi.org/10.1016/S0377-2217(98)00051-4.
  • Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, 22, 59-82.

CORPORATE BANKRUPTCY PREDICTION USING MACHINE LEARNING METHODS: THE CASE OF THE USA

Year 2022, , 1007 - 1031, 28.12.2022
https://doi.org/10.17130/ijmeb.1079688

Abstract

Many of the international hospitals and health institutions have faced serious financial difficulties with the Covid-19 epidemic that emerged at the end of 2019. In the coming years, these financial difficulties are expected to cause more bankruptcies. Being able to predict company bankruptcies is important to protect company partners, investors, company creditors and the continuity of the healthcare industry. Thanks to accurate forecasts in this area, company managers can prevent company bankruptcies by taking the necessary precautions and investors can limit their losses. This study aims to build a model that can predict bankruptcies in the health sector by using artificial neural networks (ANN). For the sample of this study, 23 companies in the health sector that declared bankruptcy between 01.01.2018 and 31.12.2020 in the USA, and 23 companies that were in the same period and the same sector but had no financial problems were selected as the control group. 30 financial ratios belonging to these companies were used as input data of the research. In the study, artificial neural networks (ANN) were chosen as the method. According to the results of the research, the correct classification rate of the artificial neural network models created using the training set data was 100%. The correct classification rate of artificial neural network models created using test set data was 90%. According to the results of the research, ANNs are promising for the prediction of company bankruptcies with their high classification success and ease of use. Therefore, it is recommended to be used by both researchers and investors.

References

  • Aktaş, R. (1997). Mali başarısızlık (işletme riski) tahmin modelleri. Ankara:Türkiye İş Bankası Kültür Yayınları.
  • Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23(4), 589–609.
  • Altman, E. I. & Narayanan, P. (1997). An international survey of business failure classification models. Financial Markets, Institutions & Instruments, 6(2), 1-57.
  • Altman, E. & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. Hoboken, N.J.: Wiley. https://doi.org/10.1002/9781118267806
  • Altman, E., Marco, G. & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505-529. https://doi.org/10.1016/0378-4266(94)90007-8
  • Aydın, N., Başar, M., Ve Coşkun, M. (2010), Finansal Yönetim,1. Baskı, Eskişehir: Detay Yayıncılık.
  • 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.
  • Ballard, D. J., Strogatz, D. S., Wagner, E. H., Sıscovick, 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.
  • Bazzoli, G. & Andes, S. (1995). Consequences of hospital financial distress. Journal of Healthcare Management, 1995. 40(4), 472.
  • Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
  • Bruynseels, L., & Willekens, M. (2012). The effect of strategic and operating turn around initiatives on audit reporting for distressed companies. Accounting, Organizations and Society, 27(4), 223-241. https://doi.org/10.1016/j.aos.2012.03.001
  • 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 UK, European Accounting Review, 13(3), 465-497.
  • Cho, M. (1994). Predicting business failure in the hospitality industry: An application of logit model (Phd), Polytechnic Institute and State University, Virginia.
  • Cleverley and P.C. Nutt, The decision process used for hospital bond rating--and its implications. Health Services Research, 1984. 19(5), 615-637.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
  • Davalos, S. & Gritta, R. D. & Adrangi, B. & Goodfriend, J., 2005. "The Use of a Genetic Algorithm in Forecasting Air Carrier Financial Stress and Insolvency," 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005 208166, Transportation Research Forum.
  • Demir, G., Teker, S., (2019). Postponing bankruptcy and debt restructuring. PressAcademia Procedia (PAP), v9, 278-284
  • Dietrich, J. K., & Sorensen, E. (1984). An application of logit analysis to prediction of merger targets. Journal of Business Research, 12(3), 393-402.
  • Fletcher, D., & Goss, E. (1993). Forecasting with neural networks. Information & Management, 24(3), 159-167. https://doi.org/10.1016/0378-7206(93)90064-Z
  • Fulmer, J. G., Moon, J. E., Gavın, T. A. & Erwın, 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.
  • Gissel, J. L.; Giacomino, D.; and Akers, M. D., (2007) "A Review of Bankruptcy Prediction Studies: 1930-Present" (2007). Accounting Faculty Research and Publications. 25.
  • Grima, S., Dallı Gonzı, R., Thalassinos, I.E. 2020. The Impact of COVID-19 on Malta and its Economy and Sustainable Strategies. Available at SSRN: https://ssrn.com/abstract=3644833 http://dx.doi.org/10.2139/ssrn.3644833
  • Grice, J.S. and M.T. Dugan, Re-estimations of the Zmijewski and Ohlson bankruptcy prediction models. Advances in Accounting, 2003. 20, 77-93.
  • Grover, J., & Lavin, A. (2001). Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy: a Service Industry Extension of Altman’s Z-Score Model of Bankruptcy Prediction. Working Paper. Southern Finance Association Annual Meeting.
  • Gu, Z. & Gao, L. (2000). A multivariate model for predicting business failures of hospitality firms. Tourism and Hospitality Research, 2(1), 37–49. http://dx.doi.org/10.1177/146735840000200108
  • Karels, G. V., & Prakash, A. J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14(4), 573-593.
  • Kaufman Hall (2021). American Hospital Association- National Hospital Flash Report Retrieved November 28, 2021, from https://www.kaufmanhall.com/consulting-services/national-hospital-flash-report.
  • Khan, S., Rabbani, R.M., Thalassinos, I.E., Atif, M. 2020. Corona Virus Pandemic Paving Ways to Next Generation of Learning and Teaching: Futuristic Cloud-Base Educational Model. Available at SSRN: https://ssrn.com/abstract=3669832.
  • Kınay, B. (2010). Ordered Logit Model approach for the determination of financial distress. Numéro Spécial, 119.
  • Kutlu B.Ve Badur B. (2009). Yapay sinir ağları ile borsa endeksi tahmini. Yönetim Dergisi:İstanbul Üniversitesi İşletme Fakültesi İşletme İktisadı Enstitüsü, 20(63), 25 - 40.
  • Landis, J. R. Ve Koch, G. G. (1977). The measurement of observer agreement for categorical data, Biometrics, 33, 159-174
  • Lee, K., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18(1), 63-72. https://doi.org/10.1016/0167-9236(96)00018-8
  • Mihalovič, M. (2016), Performance Comparison of multiple discriminant analysis and logit models in bankruptcy prediction, Economics and Sociology, 9(4), 101-118. http://dx.doi.org/10.14254/2071-789X.2016/9-4/6
  • Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109.
  • Özparlak, G. (2021). Finansal Tablo Manipülasyonlarının Tespitinde Yapay Sinir Ağlarının Kullanılması . Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (60), 331-357. http://dx.doi.org/10.18070/erciyesiibd.96146
  • Pan, W. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. https://doi.org/10.1016/j.knosys.2011.07.001
  • Pitrova, K. (2011). Possibilities of the Altman Zeta model application to Czech Firms. E&M Economics and Management, 3.
  • Puro, N., Borkowski, N., Hearld, L.R., Carroll, N.W., Byrd, J.D., Smith, D.G., & Ghiasi, A. (2019). Financial Distress and Bankruptcy Prediction: A Comparison of Three Financial Distress Prediction Models in Acute Care Hospitals. Journal of health care finance.
  • Richards, C.A., The effect of hospital financial distress on immediate breast reconstruction. 2014: Columbia University.
  • Ross, S., Westerfield, R., & Jaffe, J. (1999). Corporate finance (second ed.). Homewood IL: Irwin.
  • 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. 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.
  • Shi, Y., ve Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. Intangible Capital, 15(2), 114-127. https://doi.org/10.3926/ic.1354
  • Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm: a discriminant analysis, (Yüksek Lisans Tezi), Simon Fraser Üniversitesi, Kanada.
  • Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modelling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006
  • Taffler, R., & Tisshaw H. (1977). Going, going, gone – four factors which predict. Accountancy, March, 50-54.
  • Tam, K., & Kiang, M. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38(7), 926-947. https://doi.org/10.1287/mnsc.38.7.926
  • United States Courts. (t.y). Chapter 11–Bankruptcy Basics. Retrieved December 12, 2021, from https://www.uscourts.gov/services-forms/bankruptcy/bankruptcy-basics/chapter-11-bankruptcy-basics.
  • United States Courts. (t.y). Chapter 7–Bankruptcy Basics. Retrieved December 12, 2021, from https://www.uscourts.gov/services-forms/bankruptcy/bankruptcy-basics/chapter-7-bankruptcy-basics.
  • 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). https://doi.org/10.3390/ risks9040078.
  • Wilson, R., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557. https://doi.org/10.1016/0167-9236(94)90024-8
  • Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting and Economics, 6(1), 34–45.
  • Yavuz, S. Ve Deveci, M. (2012). İstatiksel normalizasyon tekniklerinin yapay sinir ağın performansına etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 40, 167-187.
  • Zhang, G., Y.M, Hu, Patuwo, B.E., Indro, D.C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis, European Journal of Operational Research, 116(1). https://doi.org/10.1016/S0377-2217(98)00051-4.
  • Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, 22, 59-82.
There are 56 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Articles
Authors

Gerçek Özparlak 0000-0002-8503-3199

Menevşe Özdemir Dilidüzgün This is me 0000-0002-5802-9274

Publication Date December 28, 2022
Submission Date February 28, 2022
Acceptance Date August 16, 2022
Published in Issue Year 2022

Cite

APA Özparlak, G., & Özdemir Dilidüzgün, M. (2022). CORPORATE BANKRUPTCY PREDICTION USING MACHINE LEARNING METHODS: THE CASE OF THE USA. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 18(4), 1007-1031. https://doi.org/10.17130/ijmeb.1079688