Diagnosing corporate stability using grammatical evolution

Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios to be incorporated into the classification rule are evolved from the raw financial data. This allows the creation and subsequent evolution of alternative ratio-based representations of the financial data. A sample of 178 publicly quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model correctly classified 86 (77)% of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.

[1]  Anthony Brabazon,et al.  Grammatical Swarm , 2004, GECCO.

[2]  Anthony Brabazon,et al.  Grammatical Constant Creation , 2004, GECCO.

[3]  Frank H. Easterbrook,et al.  Is corporate bankruptcy efficient , 1990 .

[4]  Daniel A. Levinthal.  Random Walks and Organizational Mortality , 1991 .

[5]  Michael O'Neill,et al.  Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.

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

[7]  J. Schumpeter.  The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle , 1934 .

[8]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[9]  Constantin Zopounidis,et al.  BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES , 1999 .

[10]  J. Argenti.  Corporate Collapse: The Causes and Symptoms , 1976 .

[11]  M. Peel,et al.  Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings , 1995 .

[12]  Paul Newbold,et al.  CLASSIFYING BANKRUPT FIRMS WITH FUNDS FLOW COMPONENTS , 1985 .

[13]  Kaisa Sere,et al.  Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms , 1996 .

[14]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[15]  Balaji Rajagopalan,et al.  Financial decision support with hybrid genetic and neural based modeling tools , 1997 .

[16]  James O. Horrigan.  Some empirical bases of financial ratio analysis , 1965 .

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

[18]  Michael O'Neill,et al.  Grammatical evolution - evolutionary automatic programming in an arbitrary language , 2003, Genetic programming.

[19]  Richard Morris,et al.  Early Warning Indicators of Corporate Failure , 1998 .

[20]  E. Altman,et al.  ZETATM analysis A new model to identify bankruptcy risk of corporations , 1977 .

[21]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

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

[23]  Howard Thomas,et al.  BANKRUPTCY AS A DELIBERATE STRATEGY: THEORETICAL CONSIDERATIONS AND EMPIRICAL EVIDENCE , 1993 .

[24]  Anthony Brabazon,et al.  Grammatical Evolution And Corporate Failure Prediction , 2002, GECCO.

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

[26]  Conor Ryan,et al.  An Investigation into the Use of Different Search Strategies with Grammatical Evolution , 2002, EuroGP.

[27]  J. Hair.  Multivariate data analysis , 1972 .

[28]  John R. Koza,et al.  Genetic Programming II , 1992 .

[29]  Edward I. Altman,et al.  Corporate Financial Distress and Bankruptcy , 1993 .

[30]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[31]  Donald C. Hambrick,et al.  Large Corporate Failures as Downward Spirals , 1988 .

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

[33]  Constantin Zopounidis,et al.  Multicriteria Decision Aid Methods for the Prediction of Business Failure , 1998 .

[34]  Raimar Richers.  The theory of economic development , 1961 .

[35]  Rolph E. Anderson,et al.  Nederlandse samenvatting en bewerking van 'Multivariate data analysis, 4th Edition, 1995' , 1998 .

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

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

[38]  Anil K. Makhija,et al.  The Impact of Chapter 11 Filings on the Risk and Return of Securityholders, 1979-1989 , 1996 .