A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database

In this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data) and a considerable number of companies have missing data. For comparison purposes we have solved the same problem using a support vector machine. Genetic programming has achieved very satisfactory results, improving those obtained with the support vector machine.

[1]  Sancho Salcedo-Sanz,et al.  Genetic programming for the prediction of insolvency in non-life insurance companies , 2005, Comput. Oper. Res..

[2]  Thomas E. McKee,et al.  Bankruptcy theory development and classification via genetic programming , 2006, Eur. J. Oper. Res..

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

[4]  Thomas E. McKee,et al.  Genetic programming and rough sets: A hybrid approach to bankruptcy classification , 2002, Eur. J. Oper. Res..

[5]  Jano I. van Hemert,et al.  A Comparison of Genetic Programming Variants for Data Classification , 1999, IDA.

[6]  Ingoo Han,et al.  The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms , 2003, Expert Syst. Appl..

[7]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[8]  Georgios Dounias,et al.  Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming , 2006, Expert Syst. Appl..

[9]  Brad G. Kyer.  Review of 5 of biologically inspired algorithms for financial modelling by Anthony Brabazon, Michael O'Neill Springer-Verlag Berlin Heidelberg, 2006 , 2010, SIGA.

[10]  Anthony Brabazon,et al.  A hybrid genetic model for the prediction of corporate failure , 2004, Comput. Manag. Sci..

[11]  Ben Paechter,et al.  A Framework for Distributed Evolutionary Algorithms , 2002, PPSN.

[12]  Kyung-shik Shin,et al.  A genetic algorithm application in bankruptcy prediction modeling , 2002, Expert Syst. Appl..

[13]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[14]  A.H. Sung,et al.  On the performance of learning machines for bankruptcy detection , 2004, Second IEEE International Conference on Computational Cybernetics, 2004. ICCC 2004..

[15]  Juan Julián Merelo Guervós,et al.  JEO: Java Evolving Objects , 2002, GECCO.

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

[17]  Michael O'Neill,et al.  Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series) , 2005 .

[18]  Lalit M. Patnaik,et al.  Genetic programming based pattern classification with feature space partitioning , 2001, Inf. Sci..

[19]  Franco Varetto.  Genetic algorithms applications in the analysis of insolvency risk , 1998 .

[20]  Moshe Leshno,et al.  Neural network prediction analysis: The bankruptcy case , 1996, Neurocomputing.

[21]  Mak Kaboudan,et al.  Biologically Inspired Algorithms for Financial Modelling , 2006, Genetic Programming and Evolvable Machines.