Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 허준영, "적응형 부스팅을 이용한 파산 예측 모형: 건설업을 중심으로" 한국지능정보시스템학회 20 (20): 35-48, 2014

2 홍승현, "유전자 알고리즘을 활용한 인공신경망 모형 최적입력변수의 선정: 부도예측 모형을 중심으로" 한국지능정보시스템학회 9 (9): 227-249, 2003

3 옥중경, "유전자 알고리즘 기반의 기업부실예측 통합모형" 한국지능정보시스템학회 15 (15): 99-121, 2009

4 김경재, "기업부도예측을 위한 인공신경망 모형에서의 사례선택기법에 의한 데이터 마이닝" 한국지능정보시스템학회 10 (10): 109-123, 2004

5 Martin-del-Brio, B., "Self-organizing neural networks for the analysis and representation of data: Some financial cases" 1 (1): 193-206, 1993

6 Serrano-Cinca, C., "Self-organizing neural networks for financial diagnosis" 17 (17): 227-238, 1996

7 권혁건, "RNN(Recurrent Neural Network)을 이용한 기업부도예측모형에서 회계정보의 동적 변화 연구" 한국지능정보시스템학회 23 (23): 139-153, 2017

8 Boritz, J. E., "Predicting corporate failure using a neural network approach" 4 (4): 95-111, 1995

9 Kiviluoto, K., "Predicting bankruptcies with the self-organizing map" 21 : 191-201, 1998

10 Kirkpatrick, S., "Optimization by simulated annealing" 220 (220): 671-680, 1983

11 김승혁, "Modified Bagging Predictors를 이용한 SOHO 부도 예측" 한국지능정보시스템학회 13 (13): 15-26, 2007

12 Tam, K. Y., "Managerial applications of neural networks: the case of bank failure predictions" 38 (38): 926-947, 1992

13 Barboza, F., "Machine learning models and bankruptcy prediction" 83 : 405-417, 2017

14 Jo, H., "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction" 11 (11): 415-422, 1996

15 Lee, K. C., "Hybrid neural network models for bankruptcy predictions" 18 (18): 63-72, 1996

16 Altman, E. I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy" 23 (23): 589-609, 1968

17 Ohlson, J. A., "Financial ratios and the probabilistic prediction of bankruptcy" 18 (18): 109-131, 1980

18 Serrano-Cinca, C., "Feedforward neural networks in the classification of financial information" 3 (3): 183-202, 1997

19 Boritz, J. E., "Effectiveness of neural network types for prediction of business failure" 9 (9): 503-512, 1995

20 Altman, E. I., "Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience)" 18 (18): 505-529, 1994

21 López. I. F. J., "Bankruptcy visualization and prediction using neural networks: A study of US commercial banks" 42 (42): 2857-2869, 2015

22 Wilson, R. L., "Bankruptcy prediction using neural networks" 11 (11): 545-557, 1994

23 Jo, H., "Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis" 13 (13): 97-108, 1997

24 Ahn, H., "Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach" 9 (9): 599-607, 2009

25 Zhang, G., "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis" 116 (116): 16-32, 1999

26 Wang, G., "An improved boosting based on feature selection for corporate bankruptcy prediction" 41 (41): 2353-2361, 2014

27 Nanni, L., "An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring" 36 (36): 3028-3033, 2009

28 Shin, K., "An application of support vector machines in bankruptcy prediction model" 28 (28): 127-135, 2005

29 Shin, K., "A genetic algorithm application in bankruptcy prediction modeling" 23 (23): 321-328, 2002

30 Tsai, C. F., "A comparative study of classifier ensembles for bankruptcy prediction" 24 : 977-984, 2014

31 김태훈, "A Hybrid Under-sampling Approach for Better Bankruptcy Prediction" 한국지능정보시스템학회 21 (21): 173-190, 2015