AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies

Due to the important role of financial distress prediction (FDP) for enterprises, it is crucial to improve the accuracy of FDP model. In recent years, classifier ensemble has shown promising advantage over single classifier, but the study on classifier ensemble methods for FDP is still not comprehensive enough and leaves to be further explored. This paper constructs AdaBoost ensemble respectively with single attribute test (SAT) and decision tree (DT) for FDP, and empirically compares them with single DT and support vector machine (SVM). After designing the framework of AdaBoost ensemble method for FDP, the article describes AdaBoost algorithm as well as SAT and DT algorithm in detail, which is followed by the combination mechanism of multiple classifiers. On the initial sample of 692 Chinese listed companies and 41 financial ratios, 30 times of holdout experiments are carried out for FDP respectively one year, two years, and three years in advance. In terms of experimental results, AdaBoost ensemble with SAT outperforms AdaBoost ensemble with DT, single DT classifier and single SVM classifier. As a conclusion, the choice of weak learner is crucial to the performance of AdaBoost ensemble, and AdaBoost ensemble with SAT is more suitable for FDP of Chinese listed companies.

[1]  Parag C. Pendharkar,et al.  A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem , 2005, Comput. Oper. Res..

[2]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[3]  Hui Li,et al.  Financial distress prediction based on serial combination of multiple classifiers , 2009, Expert Syst. Appl..

[4]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[5]  Dae-Ki Kang,et al.  Ensemble with neural networks for bankruptcy prediction , 2010, Expert Syst. Appl..

[6]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[7]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[8]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[9]  Wooju Kim,et al.  Combination of multiple classifiers for the customer's purchase behavior prediction , 2003, Decis. Support Syst..

[10]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[11]  Jie Sun,et al.  An Application of Support Vector Machine to Companies' Financial Distress Prediction , 2006, MDAI.

[12]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[13]  Ömer Kaan Baykan,et al.  Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey , 2009, Expert Syst. Appl..

[14]  David A. Elizondo,et al.  Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks , 2008, Decis. Support Syst..

[15]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[16]  Esteban Alfaro Cortés,et al.  Multiclass Corporate Failure Prediction by Adaboost.M1 , 2007 .

[17]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[18]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[19]  Jae Kwon Bae,et al.  An integrative model with subject weight based on neural network learning for bankruptcy prediction , 2009, Expert Syst. Appl..

[20]  Hui Li,et al.  Data mining method for listed companies' financial distress prediction , 2008, Knowl. Based Syst..

[21]  Yongsheng Ding,et al.  Forecasting financial condition of Chinese listed companies based on support vector machine , 2008, Expert Syst. Appl..

[22]  Bogdan Gabrys,et al.  Classifier selection for majority voting , 2005, Inf. Fusion.

[23]  Hui Li,et al.  Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers , 2008, Expert Syst. Appl..

[24]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[25]  Chihli Hung,et al.  A selective ensemble based on expected probabilities for bankruptcy prediction , 2009, Expert Syst. Appl..

[26]  Ingoo Han,et al.  Integration of Case-based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction , 1996 .

[27]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[28]  Ingoo Han,et al.  An evolutionary approach to the combination of multiple classifiers to predict a stock price index , 2006, Expert Syst. Appl..

[29]  Hui Li,et al.  Dynamic financial distress prediction using instance selection for the disposal of concept drift , 2011, Expert Syst. Appl..

[30]  Hui Li,et al.  Majority voting combination of multiple case-based reasoning for financial distress prediction , 2009, Expert Syst. Appl..