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Financial Distress Prediction Using Support Vector Machines and Logistic Regression

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Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Part of the book series: Contributions to Economics ((CE))

Abstract

Financial distress and bankruptcies are highly costly and devastating processes for all parts of the economy. Prediction of distress is notable both for the functioning of the general economy and for the firm’s partners, investors, and lenders at the micro-level. This study aims to develop an effective prediction model with Support Vector Machine and Logistic Regression Analysis. As the field of the study, 172 firms that are traded in Borsa İstanbul, have been chosen. Besides, two basic prediction methods, LRA was also used as a feature selection method and the results of this model were compared. The empirical results show us, both methods achieve a good prediction model. However, the SVM model in which the feature selection phase is applied shows the best performance.

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  1. 1.

    Finnet: Financial Information News Network. Web: https://www.finnet.com.tr/FinnetStore/Tr/Urun/Fta40.

References

  • Agostini M (2018) Corporate financial distress: going concern evaluation in both international and US contexts. Springer

    Book  Google Scholar 

  • Akkaya GC, Demireli E, Yakut ÜH (2009) İşletmelerde Finansal Başarısızlık Tahminlemesi: Yapay Sinir Ağları Modeli ile IMKB Üzerine Bir Uygulama. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi 10(2):187–216

    Google Scholar 

  • Aktaş R (1993) Endüstri Işletmeleri Için Mali Başarısızlık Tahmini: Çok Boyutlu Model Uygulaması, T. İş Bankası Kültür Yayınları, Genel Yayin No 323, Ankara

    Google Scholar 

  • Alifiah MN (2014) Prediction of financial distress companies in the trading and services sector in Malaysia using macroeconomic variables. Procedia Soc Behav Sci 129:90–98

    Article  Google Scholar 

  • Altman EI, Zhang L, Yen J (2007) Corporate financial distress diagnosis in China. New York University Salomon Center Working Paper

    Google Scholar 

  • Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23(4):589–609

    Article  Google Scholar 

  • Altman EI (1993) Corporate financial distress and bankruptcy. Wiley, New York

    Google Scholar 

  • Atiya AF (2001) Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans Neural Netw 12(4):929–935

    Article  Google Scholar 

  • Beaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111

    Article  Google Scholar 

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  • Caesarendra W, Widodo A, Yang BS (2010) Application of relevance vector machine and logistic regression for machine degradation assessment. Mech Syst Signal Process 24(4):1161–1171

    Article  Google Scholar 

  • Chang CC, Lin CL (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1–27

    Article  Google Scholar 

  • Chen MY (2011) Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Syst Appl 38(9):11261–11272

    Article  Google Scholar 

  • Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press

    Book  Google Scholar 

  • Dambolena IG, Khoury SJ (1980) Ratio stability and corporate failure. J Financ 35(4):1017–1018

    Article  Google Scholar 

  • Deakin EB (1972) A discriminant analysis of predictors of business failure. J Account Res 10(1):167–179

    Article  Google Scholar 

  • Ding Y, Song X, Zeng Y (2008) Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Syst Appl 34:3081–3089

    Article  Google Scholar 

  • Doğan S, Koçak D, Atan M (2019) Support vector machines and logistic regression analysis on predicting financial distress model. In: International Conference on Data Science, Machine Learning and Statistics. pp 292–295

    Google Scholar 

  • Doğan S (2020) Optimal Parametre ve Özellik Seçimi ile Destek Vektör Makinesi Kullanılarak Finansal Başarısızlık Tahmini (Doktora Tezi), Gazi Üniversitesi, Ankara

    Google Scholar 

  • Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35(5–6):352–359

    Article  Google Scholar 

  • Etemadi H, Rostamy A, Dehkordi H (2009) A genetic programming model for bankruptcy prediction: empirical evidence from Iran. Expert Syst Appl 36:3199–3207

    Article  Google Scholar 

  • Fan A, Palaniswami M (2000) Selecting bankruptcy predictors using a support vector machine approach. In:Proceeding of the International Joint Conference on Neural Network vol 6, pp 354–359

    Google Scholar 

  • Fletcher D, Goss E (1993) Forecasting with neural networks: an application using bankruptcy data. Inf Manag 24(3):159–167

    Article  Google Scholar 

  • Gepp A, Kumar K, Bhattacharya S (2010) Business failure prediction using decision trees. J Forecast 29:536–555

    Article  Google Scholar 

  • Gepp A, Kumar K (2008) The role of survival analysis in financial distress prediction. Int Res J Financ Econ 16:1450–2887

    Google Scholar 

  • Gunn SR (1998) Support vector machines for classification and regression. ISIS Tech Rep 14(1):5–16

    Google Scholar 

  • Hashi I (1997) The economics of bankruptcy, reorganization, and liquidation: lessons for East European Transition Economies. Russ East Eur Financ Trade 33(4):6–34

    Google Scholar 

  • Hsu CW, Chang CC, Li CJ (2003) A practical guide to support vector classification. Available from http://www.csie.ntu.edu.tw/~cjlin/paper/guide/guide.pdf

  • Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240

    Article  Google Scholar 

  • Jo H, Han I, Lee H (1997) Bankruptcy prediction using case-based reasoning, neural network and discriminant analysis for bankruptcy prediction. Expert Syst Appl 13(2):97–108

    Article  Google Scholar 

  • Kumar P, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques. A Rev Eur J Oper Res 180:1–28

    Article  Google Scholar 

  • Kurt I, Ture M, Kurum AT (2008) Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl 34(1):366–374

    Article  Google Scholar 

  • Li H, Sun J, Wu J (2010) Predicting business failure using classification and regression tree: an empirical comparison with popular classical statistical methods and top classification mining methods. Expert Syst Appl 37(8):5895–5904

    Article  Google Scholar 

  • Li H, Sun J (2012) Forecasting business failure: the use of nearest-neighbour support vectors and correcting imbalanced samples: evidence from the chinese hotel industry. Tour Manage 33(3):622–634

    Article  Google Scholar 

  • Liang D, Lu CC, Tsai CF, Shih GA (2016) Financial ratios and corporate governance indicators in bankruptcy prediction: a comprehensive study. Eur J Oper Res 252:561–572

    Article  Google Scholar 

  • Liang L, Wu D (2005) An application of pattern recognition on scoring chinese corporations financial conditions based on backpropagation neural network. Comput Oper Res 32(5):1115–1129

    Article  Google Scholar 

  • Liao Y, Fang SC, Nuttle HLW (2004) A neural network model with bounded-weights for pattern classification. Comput Oper Res 31:1411–1426

    Article  Google Scholar 

  • Lin SW, Lee ZJ, Chen SC, Tseng TY (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8:1505–1512

    Article  Google Scholar 

  • Lin WY, Hu YH, Tsai CF (2012) Machine learning in financial crisis prediction: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):421–436

    Article  Google Scholar 

  • Lincoln M (1984) An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. J Bank Finance 8(2):321–340

    Article  Google Scholar 

  • Liu H, Motoda H (1998) Feature extraction, construction and selection: a data mining perspective. Springer Science & Business Media

    Google Scholar 

  • Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst, Man, Cybern Part B (Cybern) 39(2):539–550

    Google Scholar 

  • Meyer AD (1982) Adapting to environmental jolts. Adm Sci Q 515–537

    Google Scholar 

  • Meyer PA, Pifer H (1970) Prediction of bank failures. J Financ 25:853–868

    Article  Google Scholar 

  • Min JH, Lee YC (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614

    Article  Google Scholar 

  • Min SH, Lee J, Han I (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl 31:652–660

    Article  Google Scholar 

  • Odom M, Sharda R (1990) “A neural networks model for bankruptcy prediction”. In: Proceedings of The IEEE International Conference on Neural Network 2:163–168

    Google Scholar 

  • Ohlson J (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18(1):109–131

    Article  Google Scholar 

  • Özdemir FS (2011) Finansal Başarısızlık ve Finansal Tablolara Dayalı Tahmin Yöntemleri. Ank: Siyasal Kitapevi 82(33–37):106–108

    Google Scholar 

  • Pardo M, Sberveglieri G (2005) Classification of electronic nose data with support vector machines. SensS Actuators 107:730–737

    Article  Google Scholar 

  • Park C-S, Han I (2002) A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Syst Appl 23:255–264

    Article  Google Scholar 

  • Pindado J, Rodrigues L (2005) Determinants of financial distress costs. Fin Markets Portfolio Mgmt 19(4):343–359

    Article  Google Scholar 

  • Piramuthu S (2004) Evaluating feature selection methods for learning in data mining application. Eur J Oper Res 156:483–494

    Article  Google Scholar 

  • Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517

    Article  Google Scholar 

  • Salzberg SL (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min Knowl Discov 1:317–327

    Article  Google Scholar 

  • Shin KS, Lee TS, Kim HJ (2005) An applications support vector machines in bankruptcy prediction model. Expert Syst Appl 28:127–135

    Article  Google Scholar 

  • Shin KS, Lee YJ (2002) A genetic algorithm application in bankruptcy prediction modeling. Expert Syst Appl 23:321–328

    Article  Google Scholar 

  • Smola A, Schölkopf B (1997) On kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica 22:211–231

    Article  Google Scholar 

  • Srang G (1986) Introduction to applied mathematics. Wellesley-Cambridge Press, Wellesley, MA

    Google Scholar 

  • Sun J, Li H, Huang QH, He KY (2014a) Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowl Based Syst 57:41–56

    Article  Google Scholar 

  • Sun J, Li H (2011) Dynamic financial distress prediction using instance selection for the disposal of concept drift. Expert Syst Appl 38:2566–2576

    Article  Google Scholar 

  • Sun J, Li H, Huang QH, He KY (2014b) Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowl Based Syst 57:41–56

    Article  Google Scholar 

  • Tam K, Kiang M (1992) Managerial applications of neural networks: the case of bank failure predictions. Manage Sci 38(7):926–947

    Article  Google Scholar 

  • Tam K (1991) Neural network models and the prediction of bank bankruptcy. Omega 19(5):429–445

    Article  Google Scholar 

  • Tsai CF (2008) Financial decision support using neural networks and support vector machines. Expert Syst J Knowl Eng 25(4):380–393

    Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Book  Google Scholar 

  • Woods K, Bowyer KW (1997) Generating ROC curves for artificial neural networks. IEEE Trans Med Imaging 16(3):329–337

    Article  Google Scholar 

  • Wu CH, Tzeng GH, Goo YJ, Fang WC (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397–408

    Article  Google Scholar 

  • Wu W, Cheng V, Lee S, Tan TY (2006) Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry. Account Financ 46:327–345

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat Landslides (Tokat-Turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  • Zhang G, Hu MY, Patuwo BE, Indro DC (1999) Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur J Oper Reseach 116:16–32

    Article  Google Scholar 

  • Zhongsheng H, Yu W, Xiaoyan X, Bin Z, Liang L (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 33(2):434–440

    Article  Google Scholar 

  • Zhou L, Lu D, Fujita H (2015) The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowl-Based Syst 85:52–61

    Article  Google Scholar 

  • Zmijewski ME (1984) Methodological issues related to the estimation of financial distress prediction models. J Account Res 22:59–82

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Econometrics, Karamanoğlu Mehmetbey University, Karaman, Turkey

    Seyyide Doğan

  2. Department of Econometrics, Osmaniye Korkut Ata University, Osmaniye, Turkey

    Deniz Koçak

  3. Department of Econometrics, Ankara Hacı Bayram Veli University, Ankara, Turkey

    Murat Atan

Authors
  1. Seyyide Doğan
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  2. Deniz Koçak
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  3. Murat Atan
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Correspondence to Seyyide Doğan .

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Editors and Affiliations

  1. Faculty of Economics and Admin. Sciences, Trakya University, Edirne, Turkey

    M. Kenan Terzioğlu

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Doğan, S., Koçak, D., Atan, M. (2022). Financial Distress Prediction Using Support Vector Machines and Logistic Regression. In: Terzioğlu, M.K. (eds) Advances in Econometrics, Operational Research, Data Science and Actuarial Studies. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-85254-2_26

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