Browse > Article
http://dx.doi.org/10.13088/jiis.2018.24.4.155

Predicting Corporate Bankruptcy using Simulated Annealing-based Random Fores  

Park, Hoyeon (Department of MIS, Graduate School, Dongguk University_Seoul)
Kim, Kyoung-jae (Department of MIS, Graduate School, Dongguk University_Seoul)
Publication Information
Journal of Intelligence and Information Systems / v.24, no.4, 2018 , pp. 155-170 More about this Journal
Abstract
Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.
Keywords
Simulated Annealing; Random Forests; Bankruptcy Prediction; Feature Selection; Business Analytics;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Nanni, L., and A. Lumini, "An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring", Expert Systems with Applications, Vol.36, No.2(2009), 3028-3033.   DOI
2 Ohlson, J. A., "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, Vol.18, No.1(1980), 109-131.   DOI
3 Ok, J. K. and K. Kim, "Integrated corporate bankruptcy prediction model using genetic algorithms", Journal of Intelligent Information System, Vol.15, No.4(2009), 99-120.
4 Serrano-Cinca, C., "Self-organizing neural networks for financial diagnosis", Decision Support Systems, Vol.17, No.3(1996), 227-238.   DOI
5 Serrano-Cinca, C., "Feedforward neural networks in the classification of financial information", The European Journal of Finance, Vol.3, No.3(1997), 183-202.   DOI
6 Shin, K., and Y. J. Lee, "A genetic algorithm application in bankruptcy prediction modeling", Expert Systems with Applications, Vol.23, No.3(2002), 321-328.   DOI
7 Shin, K., T. S. Lee, and H. J. Kim, "An application of support vector machines in bankruptcy prediction model", Expert Systems with Applications, Vol.28, No.1(2005), 127-135.   DOI
8 Tam, K. Y., and M. Y. Kiang, "Managerial applications of neural networks: the case of bank failure predictions", Management Science, Vol.38, No.7(1992), 926-947.   DOI
9 Tsai, C. F., Y. F. Hsu, and D. C. Yen, "A comparative study of classifier ensembles for bankruptcy prediction", Applied Soft Computing, Vol.24(2014), 977-984.   DOI
10 Wang, G., J. Ma, and S. Yang, "An improved boosting based on feature selection for corporate bankruptcy prediction", Expert Systems with Applications, Vol.41, No.5 (2014), 2353-2361.   DOI
11 Wilson, R. L., and R. Sharda, "Bankruptcy prediction using neural networks", Decision support systems, Vol.11, No.5(1994), 545-557.   DOI
12 Zhang, G., M. Y. Hu, B. E. Patuwo, and D. C. Indro, "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis", European journal of operational research, Vol.116, No.1(1999), 16-32.   DOI
13 Barboza, F., H. Kimura, and E. Altman, "Machine learning models and bankruptcy prediction", Expert Systems with Applications, Vol.83(2017), 405-417.   DOI
14 Ahn, H., and K. Kim, "Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach", Applied Soft Computing, Vol.9, No.2(2009), 599-607.   DOI
15 Altman, E. I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, Vol.23 No.4(1968), 589-609   DOI
16 Altman, E. I., G. Marco, and F. Varetto, "Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience)", Journal of Banking and Finance, Vol.18, No.3(1994), 505-529.   DOI
17 Hong, S. H., and K. Shin, "Using GA based input selection method for artificial neural network modeling: application to bankruptcy prediction", Journal of Intelligence and Information Systems, Vol.9, No.1(2003), 227-249.
18 Boritz, J. E., and D. B. Kennedy, "Effectiveness of neural network types for prediction of business failure", Expert Systems with Applications, Vol.9, No.4(1995), 503-512.   DOI
19 Boritz, J. E., D. B. Kennedy, and A. D. M. E. Albuquerque, "Predicting corporate failure using a neural network approach", Intelligent Systems in Accounting, Finance and Management, Vol.4, No.2(1995), 95-111.   DOI
20 Heo, J. Y., and J. Y. Yang, "Bankruptcy forecasting model using AdaBoost: a focus on construction companies", Journal of Intelligence and Information Systems, Vol.20, No.1(2014), 35-48.   DOI
21 Jo, H., and I. Han, "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction", Expert Systems with Applications, Vol.11, No.4(1996), 415-422.   DOI
22 Jo, H., I. Han, and H. Lee, "Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis", Expert Systems with Applications, Vol.13, No.2(1997), 97-108.   DOI
23 Kim, K., "Data mining using instance selection in artificial neural networks for bankruptcy prediction", Journal of Intelligent Information System, Vol.10, No.1(2004), 109-123.
24 Kiviluoto, K., "Predicting bankruptcies with the self-organizing map", Neurocomputing, Vol.21(1998), 191-201.   DOI
25 Kim, S. H., and J. W. Kim, "SOHO bankruptcy prediction using modified bagging predictors", Journal of Intelligence and Information Systems, Vol.13, No.2(2007), 15-26.
26 Kim, T., and H. Ahn, "A hybrid under-sampling approach for better bankruptcy prediction", Journal of Intelligent Information System, Vol.21, No.2(2015), 173-190.   DOI
27 Kirkpatrick, S., C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing", Science, Vol.220, No.4598(1983), 671-680.   DOI
28 Kwon, H., D. Lee, and M. Shin, "Dynamic forecasts of bankruptcy with recurrent neural network model", Journal of Intelligent Information System, Vol.23, No.3(2017), 139-153.
29 Lopez. I. F. J., and I. P. Sanz, "Bankruptcy visualization and prediction using neural networks: A study of US commercial banks", Expert Systems with Applications, Vol.42, No.6(2015), 2857-2869.   DOI
30 Lee, K. C., I. Han, and Y. Kwon, "Hybrid neural network models for bankruptcy predictions", Decision Support Systems, Vol.18, No.1(1996), 63-72.   DOI
31 Martin-del-Brio, B., and C. Serrano-Cinca, "Self-organizing neural networks for the analysis and representation of data: Some financial cases", Neural Computing & Applications, Vol.1, No.3(1993), 193-206.   DOI