Empirical models based on features ranking techniques for corporate financial distress prediction

Accurate prediction of corporate financial distress is very important for managers, creditors and investors to take correct measures to reduce loss. Many quantitative methods have been employed to develop empirical models for predicting corporate bankruptcy. However, there is so much information disclosed in the companies' financial statements, what information should be selected for building the empirical models with objective to maximize the predictive accuracy. In this study, more than 20 models based on six features ranking strategies are tested on North American companies and Chinese listed companies. The experimental results are helpful to develop financial models by choosing the proper quantitative methods and features selection strategy.

[1]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[2]  Kyoung-jae Kim,et al.  Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach , 2009, Appl. Soft Comput..

[3]  Gonzalo Martínez-Muñoz,et al.  Switching class labels to generate classification ensembles , 2005, Pattern Recognit..

[4]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[5]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[6]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

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

[10]  Loris Nanni,et al.  An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring , 2009, Expert Syst. Appl..

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

[12]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[13]  T. Schneider.  Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values. , 2001 .

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

[15]  Ingoo Han,et al.  Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis , 1997 .

[16]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[17]  Ron Kohavi,et al.  Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.

[18]  Zoran Bosnic,et al.  ROC analysis of classifiers in machine learning: A survey , 2013, Intell. Data Anal..

[19]  Loris Nanni.  A novel ensemble of classifiers for protein fold recognition , 2006, Neurocomputing.

[20]  Huimin Zhao,et al.  Incorporating domain knowledge into data mining classifiers: An application in indirect lending , 2008, Decis. Support Syst..

[21]  Kin Keung Lai,et al.  An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction , 2007, ICANNGA.

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

[23]  Chae Woo Nam,et al.  Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies , 2008 .

[24]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[25]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[26]  Sheng-Tun Li,et al.  Predicting financial activity with evolutionary fuzzy case-based reasoning , 2009, Expert Syst. Appl..

[27]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[28]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[29]  E. Laitinen,et al.  Bankruptcy prediction: Application of the Taylor's expansion in logistic regression , 2000 .

[30]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[31]  Kin Keung Lai,et al.  Benchmarking binary classification models on data sets with different degrees of imbalance , 2009, Frontiers of Computer Science in China.

[32]  James H. Scott.  The probability of bankruptcy: A comparison of empirical predictions and theoretical models , 1981 .

[33]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[34]  J. Dietrich.  Discussion of Methodological Issues Related to the Estimation of Financial Distress Prediction Models , 1984 .

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

[36]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[37]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[38]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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