BIST Sanayi Endeksi'nde Finansal Başarısızlık Tahmini Geleneksel Modellerin ve Kümelenme Tekniklerinin Değerlendirilmesi

Yıl 2023, , 660 - 680, 30.12.2023

Ömer Serkan Gülal , Gökhan Seçme , Eda Köse

https://doi.org/10.30784/epfad.1370893

Öz

Firmalar, kredi verenler, yatırımcılar ve bir bütün olarak ekonomi için bir firmanın iflas veya tasfiyesi ile sonuçlanabilecek finansal sıkıntı kavramı çok önemli bir konudur. Son dönemde yaşanan finansal krizler ve küresel ekonomik dalgalanmalar bu konunun önemini artırmıştır. Önceki çalışmalar göz önünde bulundurulduğunda, finansal sıkıntıyı öngörmek amacıyla Altman Z-skoru gibi yöntemlerin geliştirildiği görülmektedir. Fakat son dönemlerde makine öğrenmesi gibi yeni tekniklerin de bu amaçla kullandığı görülmektedir. Bu çalışmanın amacı, k-ortalamalar kümeleme algoritması ile Altman Z-skoru ve Springate S-skoru modellerinden faydalanarak, BIST Sanayi Endeksi (XUSIN) firmalarında finansal sıkıntıyı tahmin etmektir. Araştırmanın bulgularına göre iki firma 2011, 2012, 2015 ve 2017 yıllarında Altman z-skoru, Zꞌ-skoru, S-skoru ve mali durum kriterlerinin üçünü de karşılamaktayken, 2016 ve 2018 yıllarında 2 firma, 2013 ve 2014 yıllarında 5 firma, 2019 yılında 4 firma, 2020 yılındaysa 1 firma bu kriterleri karşılamaktadır. 2021 yılına bakıldığında hiçbir şirketin aynı gruplarda gruplanmadığı görülmektedir. Bu durum kullanılan yöntemlerin farklı sonuçlara ulaştığı anlamına gelmektedir. Özellikle k-means kümeleme algoritmasının, daha yüksek ayırıcı özelliği sayesinde ilgili taraflar için, diğer yöntemlere göre daha doğru kümeleme sonuçları verdiği tespit edilmiştir.

Anahtar Kelimeler

Finansal Başarısızlık, Finansal Sıkıntı, Altman Z-skor, S-skor Yöntemi, K-Ortalamalar Kümeleme

Predicting Financial Distress in the BIST Industrials Index: Evaluating Traditional Models and Clustering Techniques

Öz

Financial distress, which can lead to bankruptcy or liquidation, is important for companies, creditors, investors, and the economy. Recent financial crises and global economic fluctuations have brought this issue to the forefront. In an effort to foresee financial distress, methods like Altman's Z-score have been proposed while, recent developments have allowed for the incorporation of recent techniques like machine learning. The purpose of this study is to forecast the emergence of financial distress in BIST Industrials Index (XUSIN) companies by using the k-means clustering algorithm, Altman Z-score and Springate S-score models with firm level financial indicators where we investigated successful and unsuccessful companies. Our findings show that two companies met all three Altman Z-score, Zꞌ-score, S-score and financial situation criteria in 2011, 2012, 2015, and 2017; 2 companies in 2016 and 2018; 5 companies in 2013 and 2014; 4 companies in 2019; 1 company in 2020 where no companies are grouped in the same groups in 2021, which means the methods reach different results. It has been determined that the k-means clustering algorithm, particularly due to its higher separability, provides more accurate clustering results for the concerned parties compared to other methods.

Anahtar Kelimeler

Financial Distress, Altman Z-score, S-score Method, K-Means Clustering

Kaynakça

• Agustini, N.W. and Wirawati, N.G.P. (2019). Pengaruh rasio keuangan pada financial distress perusahaan ritel yang terdaftar di bursa efek Indonesia (BEI). E-Jurnal Akuntansi, 26(1), 251-280. https://doi.org/10.24843/eja.2019.v26.i01.p10
• Aker, Y. and Karavardar, A. (2023). Using machine learning methods in financial distress prediction: Sample of small and medium sized enterprises operating in Turkey. Ege Academic Review, 23(2), 145-162, https://doi.org/10.21121/eab.1027084
• Akyuz, K.C., Balaban, Y. and Yildirim, I. (2012). Bilanço oranları yardımıyla orman ürünleri sanayisinin finansal yapısının değerlendirilmesi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 9, 133-144. Retrieved from https://dergipark.org.tr/tr/pub/ulikidince/
• Al Zaabi, O.S.H. (2011). Potential for the application of emerging market Z-score in UAE Islamic banks. International Journal of Islamic and Middle Eastern Finance and Management, 4(2), 158-173. doi:10.1108/17538391111144498
• Alamsyah, A., Kristanti, N. and Kristanti, F.T. (2021). Early warning model for financial distress using artificial neural network. Paper presented at the IOP Conference Series: Materials Science and Engineering. Retrived from https://iopscience.iop.org/article/10.1088/1757-899X/1098/5/052103/meta
• Alexandra, H., Cosmin, J. and Gabriel, D.D. (2008). A cluster analysis of financial performance in central and eastern Europe. Annals of the University of Oradea, Economic Science Series, 17(3) 289-294, Retrieved from https://anale.steconomiceuoradea.ro/
• Almamy, J., Aston, J. and Ngwa, L.N. (2016). An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK. Journal of Corporate Finance, 36, 278-285. https://doi.org/10.1016/j.jcorpfin.2015.12.009
• Altinirmak, S. and Karamasa, C. (2016). Comparison of machine learning techniques for analyzing banks’ financial distress. Balıkesir University the Journal of Social Sciences Institute, 19(36), 291-303. https://doi.org/10.31795/baunsobed.645223
• 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/10.1111/j.1540-6261.1968.tb00843.x
• Altman, E.I. (2002). Revisiting credit scoring models in a Basel 2 environment (NYU Working Paper No. S-CDM-02-06). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1295815
• Altman, E.I., Haldeman, R.G. and Narayanan, P. (1977). ZETATM analysis a new model to identify bankruptcy risk of corporations. Journal Banking Finance, 1(1), 29–54. https://doi.org/10.1016/0378-4266(77)90017-6
• Altman, E.I. and Hotchkiss, E. (1993). Corporate financial distress and bankruptcy (3. ed.). New York: John Wiley & Sons.
• Altman, E.I., Iwanicz‐Drozdowska, M., Laitinen, E.K. and Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model. Journal of International Financial Management and Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053
• Ari, E.S., Ozkose, H., Doğan, A. ve Calp, M.H. (2016). İstanbul Borsası’nda işlem gören firmaların finansal performanslarının kümeleme analizi ile değerlendirilmesi. Bilişim Teknolojileri Dergisi, 9(1), 33-39. https://doi.org/10.17671/btd.55726
• Ariesta, R.W., Suhadak and Nuzula, N.F. (2015). The analysis of bank financial performance using Altman (Z-score) to predict bankruptcy. Jurnal Administrasi Bisnis (JAB), 26(1), 1-6. Retrieved from https://www.neliti.com/
• Awad, M. and Khanna, R. (2015). Machine learning. In M. Awad and R. Khanna (Eds.), Efficient learning machines (p. 1-18). Berkeley: Springer CA. https://doi.org/10.1007/978-1-4302-5990-9_1
• Bassetto, C.F. and Kalatzis, A.E.G. (2011). Financial distress, financial constraint and investment decision: Evidence from Brazil. Economic Modelling, 28(1–2), 264-271. https://doi.org/10.1016/j.econmod.2010.09.003
• Beaver, W.H. (1996). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
• Ben Jabeur, S., Stef, N. and Carmona, P. (2023). Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering. Computational Economics, 61(2), 715-741. https://doi.org/10.1007/s10614-021-10227-1
• Cleary, S. and Hebb, G. (2016). An efficient and functional model for predicting bank distress: In and out of sample evidence. Journal of Banking & Finance, 64(C), 101-111. doi:10.1016/j.jbankfin.2015.12.001
• Cohen, S., Doumpos, M., Neofytou, E. and Zopounidis, C. (2012). Assessing financial distress where bankruptcy is not an option: An alternative approach for local municipalities. European Journal of Operational Research, 218(1), 270–279. https://doi.org/10.1016/j.ejor.2011.10.021
• Dimitras, A.I., Zanakis, S.H. and 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
• Dube, F., Nzimande, N. and Muzindutsi, P.F. (2023). Application of artificial neural networks in predicting financial distress in the JSE financial services and manufacturing companies. Journal of Sustainable Finance & Investment, 13(1), 723-747. https://doi.org/10.1080/20430795.2021.2017257
• Fidan, M.E. (2021). BİST’te işlem gören tekstil, giyim eşyası ve deri sektörü işletmelerinin Altman-Z skor yöntemi ile finansal başarısızlık tahmini. İsletme Arastırmalari Dergisi, 13(3), 1945-1969. https://doi.org/10.20491/isarder.2021.1239
• Frydman, H., Altman, E.I. and Kao, D.L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance, 40(1), 269–291. https://doi.org/10.2307/2328060
• Gazel, S. and Akel, V. (2018). Borsa İstanbul’da sektör sınıflandırmasının kümeleme analizi ile belirlenmesi. Muhasebe ve Finansman Dergisi, 77, 147-164, doi:10.25095/mufad.401472
• Gestel, T.V., Baesens, B., Suykens, J.A.K., Van den Poel, D., Baestaens, D.E. and Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172(3), 979–1003. https://doi.org/10.1016/j.ejor.2004.11.009
• Guizani, M. and Abdalkrim, G. (2023). Does gender diversity on boards reduce the likelihood of financial distress? Evidence from Malaysia. Asia-Pacific Journal of Business Administration, 15(2), 287-306. https://doi.org/10.1108/APJBA-06-2021-0277
• Gunawan, B., Pamungkas, R. and Susilawati, D. (2017). Comparison of financial distress predictions with Altman, Grover and Zmijewski models. Journal of Accounting and Investment, 18(1), 119-127. https://doi.org/10.52970/grfm.v3i1.216
• Horobet, A., Joldes, C. and Gabriel, D.D. (2008). A cluster analysis of financial performance in central and eastern Europe. Economic Science Series, 3, 289-294. Retrieved from https://anale.steconomiceuoradea.ro/
• Islam, M.N., Li, S. and Wheatley, C.M. (2023). Accounting comparability and financial distress. Review of Accounting and Finance, 22(3), 353-373. doi:10.1108/RAF-07-2022-0207
• Jensen, M.C. (1989). The eclipse of the public corporation. Harvard Business Review (61), 66-68. Retrieved from https://hbr.org/1989/09/eclipse-of-the-public-corporation
• Joshi, M.K. (2020). Financial performance analysis of select Indian public sector banks using Altman’s Z-score model. Smart Journal of Business Management Studies, 16(2), 74-87. doi:10.5958/2321-2012.2020.00018.4
• Kalbuana, N., Taqi, M., Uzliawati, L. and Ramdhani, D. (2022). The effect of profitability, board size, woman on boards, and political connection on financial distress conditions. Cogent Business & Management, 9(1), 2142997. https://doi.org/10.1080/23311975.2022.2142997
• Kalfa, V.R. and Bekcioglu, S. (2013). İMKB’de işlem gören gıda, tekstil ve çimento sektörü şirketlerinin finansal oranlar yardımıyla kümelenmesi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, EYİ 2013(Özel Sayısı), 441-463. Retrieved from https://dergipark.org.tr/en/pub/dpusbe/
• Karaatlı, M. and Yıldız, E. (2021). Mevduat bankaların finansal yapılarının kümeleme analizi ile incelenmesi. Business & Management Studies: An International Journal, 9(1), 1-17. https://doi.org/10.15295/bmij.v9i1.1594
• Kiraci, K. (2021). Covid-19, financial risk and the airline industry. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(3), 1557-1581. https://doi.org/10.30798/makuiibf.911548
• Kristianto, H. and Rikumahu, B. (2019). A cross model telco industry financial distress prediction in indonesia: Multiple discriminant analysis, logit and artificial neural network. Paper presented at the 2019 7th International Conference on Information and Communication Technology, Kuala Lumpur, Malaysia. https://doi.org/10.1109/ ICoICT.2019.8835198
• Kristanti, F.T., Safriza, Z. and Salim, D.F. (2023). Are Indonesian construction companies financially distressed? A prediction using artificial neural networks. Investment Management and Financial Innovations, 20(2), 41-52. http://dx.doi.org/10.21511/imfi.20(2).2023.04
• Kulali, I. (2016). Altman Z-skor modelinin bist şirketlerinin finansal başarısızlık riskinin tahmin edilmesinde uygulanması. Uluslararası Yönetim İktisat ve İşletme Dergisi, 12(27), 283-291. https://doi.org/10.17130/10.17130/ijmeb.2016.12.27.1076
• Li, H. and Sun, J. (2009). Gaussian case-based reasoning for business-failure prediction with empirical data in china. Information Sciences, 179(1–2), 89–108. https://doi.org/10.1016/j.ins.2008.09.003
• Lloyd, S.P. (1957). Binary block coding. Bell System Technical Journal, 36(2), 517-535. https://doi.org/10.1002/j.1538-7305.1957.tb02410.x
• Mohammed, S. (2017). Bankruptcy prediction by using the Altman Z-score model in Oman: A case study of Raysut cement company SAOG and its subsidiaries. Australasian Accounting, Business and Finance Journal, 10(4), 70–80. https://doi.org/10.14453/aabfj.v10i4.6
• Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
• Opler, T.C. and Titman, S. (1994). Financial distress and corporate performance. The Journal of Finance, 49(3), 1015-1040. https://doi.org/10.1111/j.1540-6261.1994.tb00086.x
• Ozdemir, F.S. (2014). Halka açık ve halka açık olmayan işletmeler yönüyle tekdüzen muhasebe sistemi ve Altman Z-skor modellerinin uygulanabilirliği. Ege Akademik Bakış, 14(1), 147-161. doi:10.21121/eab.2014118075
• Ozkan, M. and Boran, L. (2014). Veri madenciliğinin finansal kararlarda kullanımı. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(1), 59-82. Retrieved from https://dergipark.org.tr/en/pub/ckuiibfd/
• Panigrahi, A. (2019). Validity of Altman’s “Z” score model in predicting financial distress of pharmaceutical companies. NMIMS Journal of Economic and Public Policy, 4(1), 65-73. Retrieved from https://papers.ssrn.com/
• Platt, H.D. and Platt, M.B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 60-72. https://doi.org/10.1007/BF02755985
• Pradhan, R. (2014). Z score estimation for Indian banking sector. International Journal of Trade, Economics and Finance, 5(6), 516-520. doi:10.7763/IJTEF.2014.V5.425
• Ravi Kumar, P. and Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review. European Journal of Operational Research, 180(1), 1–28. https://doi.org/10.1016/j.ejor.2006.08.043
• Saif, O. and Al Zaabi, H. (2011). Potential for the application of emerging market Z-score in UAE Islamic banks. International Journal of Islamic and Middle Eastern Finance and Management, 4(2), 158-173. doi:10.1108/17538391111144498
• Salur, M.N. (2021). Fi̇nansal başarısızlık tahmi̇ninde yapay si̇ni̇r ağları modeli̇nin kullanımı: Borsa İstanbul’da bir uygulama. Journal of Economics, Finance and Accounting, 8(1), 17-30. doi:10.17261/Pressacademia.2021.1375
• Shih, M.Y., Jheng, J.W. and Lai, L.F. (2010). A two-step method for clustering mixed categorical and numeric data. Tamkang Journal of Science and Engineering, 13(1), 11-19. https://doi.org/10.6180/jase.2010.13.1.02
• Shin, K.S. and Lee, Y.J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321–328. https://doi.org/10.1016/S0957-4174(02)00051-9 Springate, G.L.V. (1978). Predicting the possibility of failure in a Canadian firm (Unpublished doctoral dissertation). Eraser University, Burnaby.
• Susler, B. (2022). Finansal başarısızlığın yapay sinir ağları ve çok değişkenli istatistiksel analiz teknikleri ile tahmin edilmesi: Borsa İstanbul’da bir uygulama (Yayımlanmamış doktora tezi). Bursa Uludağ Üniversitesi, Sosyal Bilimler Enstitüsü.
• Taffler, R.J. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking and Finance, 8, 199–227. https://doi.org/10.1016/0378-4266(84)90004-9
• Tekin, B. and Temelli, F. (2021). Firmaların kümeleme analizi ile finansal oranlar temelinde finansal başarılarının değerlendirilmesi: Borsa Istanbul örneği. JOEEP: Journal of Emerging Economies and Policy, 6(1), 211-221. Retrieved from https://dergipark.org.tr/en/pub/joeep/
• Theodossiou, P., Kahya, E., Saidi, R. and Philippatos, G. (1996). Financial distress and corporate acquisitions: Further empirical evidence. Journal of Business Finance and Accounting, 23(5–6), 699–719. doi:10.1111/j.1468-5957.1996.tb01149.x
• Tsai, C.F. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46-58. https://doi.org/10.1016/j.inffus.2011.12.001
• Ullah, I., Shah, S.M., Khan, M.N. and Ali, M.D. (2023). Impact of corporate social responsibility on financial distress, a case of non-financial listed firms in Pakistan. International Review of Basic and Applied Sciences, 11(2), 319-331. Retrieved from https://irbas.academyirmbr.com/journalmain.php
• Ural, K., Gurarda, S. and Onemli, M.B. (2015). Lojistik regresyon modeli ile finansal başarısızlık tahminlemesi: Borsa İstanbul’da faaliyet gösteren gıda, içki ve tütün şirketlerinde uygulama. Muhasebe ve Finansman Dergisi, 67, 85-100. Retrieved from https://dergipark.org.tr/tr/pub/mufad/
• Wu, D., Ma, X. and Olson, D.L. (2022). Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems, 159, 113814. https://doi.org/10.1016/j.dss.2022.113814
• Zhang, Z., Wu, C., Qu, S. and Chen, X. (2022). An explainable artificial intelligence approach for financial distress prediction. Information Processing & Management, 59(4), 102988. https://doi.org/10.1016/j.ipm.2022.102988
• Zhu, L., Yan, D., Zhang, Z. and Chi, G. (2022). Financial distress prediction of Chinese listed companies using the combination of optimization model and convolutional neural network. Mathematical Problems in Engineering, 2022, 038992. https://doi.org/10.1155/2022/9038992
• Zmijewski, M.E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859
• Zopounidis, C. and Dimitras, A.I. (1998). Multicriteria decision aid methods for the prediction of business failure (12. Ed.). New York: Springer.
• Zopounidis, C. and Doumpos, M. (1999). A multicriteria decision aid methodology for sorting decision problems: The case of financial distress. Computational Economics, 14, 197-218. https://doi.org/10.1023/A:1008713823812