Intelligent hybrid model for financial crisis prediction using machine learning techniques

Abstract

Financial crisis prediction (FCP) plays a vital role in the economic phenomenon. The precise prediction of the number and possibility of failing firms acts as an index of the growth and strength of a nation’s economy. Traditionally, several methods have been presented for effective FCP. On the other hand, the classification performance and prediction accuracy and data legality is not good enough for practical applications. In addition, many of the developed methods perform well for some of the particular dataset but not adaptable to different dataset. Hence, there is a requirement to develop an efficient prediction model for better classification performance and adaptable to diverse dataset. This paper presents a cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based classification model. In the first stage, an improved K-means algorithm is devised to eliminate the wrongly clustered data. Then, a rule-based model is selected to design to fit the given dataset. At the end, FSCGACA is employed for seeking the optimal parameters of the rule-based model. The proposed algorithm is employed to a collection of three benchmark dataset which include qualitative bankruptcy dataset, Weislaw dataset and Polish dataset. A detailed statistical analysis of the dataset is also given. The results analysis ensured that the presented FCP model is superior to other classification model based on the different measures and also found to be more appropriate for diverse dataset.

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References

  1. Abellán J, Mantas CJ (2014) Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 41(8):3825–3830

    Article  Google Scholar 

  2. Ala’raj M, Abbod MF (2016) Classifiers consensus system approach for credit scoring. Knowl Based Syst 104:89–105

    Article  Google Scholar 

  3. 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 

  4. Chauhan N, Ravi V, Chandra DK (2009) Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst Appl 36(4):7659–7665

    Article  Google Scholar 

  5. Chen HL et al (2011) An adaptive fuzzy K-nearest neighbor method based on parallel particle swarm optimization for bankruptcy prediction. In: Huang J, Cao L, Srivastava J (eds) Advances in knowledge discovery and data mining. Springer, Berlin, pp 249–264

    Chapter  Google Scholar 

  6. Evans R, Pfahringer B, Holmes G (2011) Clustering for classification. In: 2011 7th international conference on information technology in Asia (CITA 11). IEEE, pp 1–8

  7. Fedorova E, Gilenko E, Dovzhenko S (2013) Bankruptcy prediction for Russian companies: application of combined classifiers. Expert Syst Appl 40(18):7285–7293

    Article  Google Scholar 

  8. Guojun G, Chaoqu M, Jianhong W (2007) Data clustering: theory, algorithm and application, 1st edn. ASA-SIAM, Philadelphia

    Google Scholar 

  9. Kim MJ, Han I (2003) The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Syst Appl 25(4):637–646

    Article  Google Scholar 

  10. Korsunsky AM, Constantinescu A (2006) Work of indentation approach to the analysis of hardness and modulus of thin coatings. Mater Sci Eng A 423(1–2):28–35

    Article  Google Scholar 

  11. Martin VA, Balaji S, Lakshmi TM, Venkatesan VP (2012) An analysis on qualitative bankruptcy prediction using fuzzy ID3 and ant colony optimization algorithm. In: International conference on pattern recognition, informatics and medical engineering, pp 416–421

  12. Martin A, Uthayakumar J, Nadarajan M (2014) Qualitative_Bankruptcy data set. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/qualitative_bankruptcy. Accessed 1 Oct 2018

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

    Article  Google Scholar 

  14. Naveen N et al (2010) Differential evolution trained radial basis function network: application to bankruptcy prediction in banks. Int J BioInspir Comput 2(3–4):222–232

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Paramjeet, Ravi V (2011) Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks. Int J Data Anal Tech Strateg 3(3):261–280

    Article  Google Scholar 

  17. Pietruszkiewicz W (2008) Dynamical systems and nonlinear Kalman filtering applied in classification. In: Proceedings of the 7th IEEE international conference on cybernetic intelligent systems, CIS 2008

  18. Ravi V, Pramodh C (2008) Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks. Appl Soft Comput 8:1539–1548

    Article  Google Scholar 

  19. Ravisankar P, Ravi V (2009) Failure prediction of banks using threshold accepting trained kernel principal component neural network. In: Proceedings of the IEEE world congress on nature & biologically inspired computing, NaBIC 2009

  20. Reddy KN, Ravi V (2013) Differential evolution trained kernel principal component WNN and kernel binary quantile regression: application to banking. Knowl Based Syst 39:45–56

    Article  Google Scholar 

  21. Sarkar S, Sriram RS (2001) Bayesian models for early warning of bank failures. Manag Sci 47(11):1457–1475

    Article  Google Scholar 

  22. Sharma N, Arun N, Ravi V (2013) An ant colony optimisation and Nelder–Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks. Int J Inf Decis Sci 5(2):188–203

    Google Scholar 

  23. Shin K-S, Lee TS, Kim H-J (2005) An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28(1):127–135

    Article  Google Scholar 

  24. Sun L, Shenoy PP (2007) Using Bayesian networks for bankruptcy prediction: some methodological issues. Eur J Oper Res 180(2):738–753

    Article  Google Scholar 

  25. Tkaczyk ER, Mauring K, Tkaczyk AH et al (2008) Control of the blue fluorescent protein with advanced evolutionary pulse shaping. Biochem Biophys Res Commun 376(4):733–737

    Article  Google Scholar 

  26. Tomczak S (2016) Polish companies bankruptcy data data set. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data. Accessed 15 Sept 2018

  27. Tsai C-F, Wu J-W (2008) Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 34(4):2639–2649

    Article  Google Scholar 

  28. ul Hassan E, Zainuddin Z, Nordin S (2017) A review of financial distress prediction models: logistic regression and multivariate discriminant analysis. Indian Pac J Account Finance 1(3):13–23

    Google Scholar 

  29. Vasu M, Ravi V (2011) Bankruptcy prediction in banks by principal component analysis threshold accepting trained wavelet neural network hybrid. In: Proceedings of the international conference on data mining, USA

  30. Vieira SM, Sousa JMC, Runkler TA (2010) Two cooperative ant colonies for feature selection using fuzzy models. Expert Syst Appl 37(4):2714–2723

    Article  Google Scholar 

  31. Wang Y, Li B, Weise T (2010) Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems. Inf Sci 180(12):2405–2420

    Article  Google Scholar 

  32. West RC (1985) A factor-analytic approach to bank condition. J Bank Finance 9(2):253–266

    Article  Google Scholar 

  33. Zhang G et al (1999) Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur J Oper Res 116(1):16–32

    Article  Google Scholar 

  34. Zhang Y, Wu L, Wang S (2011) UCAV path planning based on FSCABC. Information 14(3):687–692

    Google Scholar 

  35. Zhang Y, Wu L, Wang S (2013a) UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Math Probl Eng 2013:9

    Google Scholar 

  36. Zhang Y, Wang S, Ji G (2013b) A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm. Math Probl Eng 2013:10

    Google Scholar 

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Correspondence to Noura Metawa.

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Uthayakumar, J., Metawa, N., Shankar, K. et al. Intelligent hybrid model for financial crisis prediction using machine learning techniques. Inf Syst E-Bus Manage 18, 617–645 (2020). https://doi.org/10.1007/s10257-018-0388-9

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Keywords

  • FCP
  • K-means algorithm
  • Genetic algorithm
  • Ant colony optimization