Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case

A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers) is conducted, and a Monte Carlo simulation-based approach is used in order to measure the average effects of sample size variations on the predictive performance of each classifier. The paper uses as a benchmark the problem of forecasting the level of profitability of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well a distinctive feature of many financial prediction problems, namely that of being characterized by a high dimension feature space as well as a low degree of separability. Response surfaces are estimated in order to summarize the results. A higher performance of model-free classifiers is generally observed, even for fairly moderate sample sizes. � 2004 Elsevier B.V. All rights reserved.

[1]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[2]  M. Greenstein-Prosch,et al.  Bankruptcy Prediction Using Ex Ante Neural Networks and Realistically Proportioned Testing Sets , 1996 .

[3]  Marjorie B. Platt,et al.  DEVELOPMENT OF A CLASS OF STABLE PREDICTIVE VARIABLES: THE CASE OF BANKRUPTCY PREDICTION , 1990 .

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

[5]  M. Tippett,et al.  Economic and Accounting Rates of Return A Statistical Model , 1991 .

[6]  Xiaotong Shen,et al.  Sieve extremum estimates for weakly dependent data , 1998 .

[7]  C. J. Stone,et al.  Consistent Nonparametric Regression , 1977 .

[8]  Gábor Lugosi,et al.  Nonparametric estimation via empirical risk minimization , 1995, IEEE Trans. Inf. Theory.

[9]  A. Zapranis,et al.  Forecasting Corporate Failure with Neural Network Approach: The Greek Case , 2000 .

[10]  Mo Adam Mahmood,et al.  A New Approach to Evaluating Business Ethics : An Artificial Neural Networks Application , 2018 .

[11]  John S. Chandler,et al.  PREDICTING STOCK MARKET BEHAVIOR THROUGH RULE INDUCTION: AN APPLICATION OF THE LEARNING‐FROM‐EXAMPLE APPROACH* , 1987 .

[12]  F. Girosi,et al.  From regularization to radial, tensor and additive splines , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[13]  Richard G. Mathieu,et al.  Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees , 2000 .

[14]  Constantin Zopounidis,et al.  Multicriteria classification and sorting methods: A literature review , 2002, Eur. J. Oper. Res..

[15]  Ingoo Han,et al.  An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks* , 1992 .

[16]  Chih-Yang Tsai.  An iterative feature reduction algorithm for probabilistic neural networks , 2000 .

[17]  J. Patell,et al.  The Experimental Design of Classification Models: An Application of Recursive Partitioning and Bootstrapping to Commercial Bank Loan Classifications , 1984 .

[18]  Hung T. Nguyen,et al.  Fuzzy systems are universal approximators for a smooth function and its derivatives , 2000 .

[19]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[20]  Yuhong Yang,et al.  Minimax Nonparametric Classification—Part I: Rates of Convergence , 1998 .

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

[22]  Pamela K. Coats,et al.  Recognizing Financial Distress Patterns Using a Neural Network Tool , 1993 .

[23]  Ernest P. Goss,et al.  Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers , 1995 .

[24]  Halbert White,et al.  Artificial neural networks: an econometric perspective ∗ , 1994 .

[25]  Hideo Tanaka,et al.  Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms , 1994, CVPR 1994.

[26]  Parag C. Pendharkar,et al.  A computational study on the performance of artificial neural networks under changing structural design and data distribution , 2002, Eur. J. Oper. Res..

[27]  Javier de Andrés,et al.  Statistical techniques vs. SEES algorithm : an application to a small business environment , 2001 .

[28]  H. White,et al.  Information criteria for selecting possibly misspecified parametric models , 1996 .

[29]  Pierre Hansen,et al.  Partitioning Problems in Cluster Analysis: A Review of Mathematical Programming Approaches , 1994 .

[30]  Toshinori Munakata,et al.  Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 , 2002, Eur. J. Oper. Res..

[31]  Robert J. Pavur.  A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem , 2002, Eur. J. Oper. Res..

[32]  B. K. Wong,et al.  The use of cascade-correlation neural networks in University fund raising , 2000, J. Oper. Res. Soc..

[33]  Koen Bertels,et al.  Qualitative company performance evaluation: Linear discriminant analysis and neural network models , 1999, Eur. J. Oper. Res..

[34]  Xiaotong Shen,et al.  On methods of sieves and penalization , 1997 .

[35]  D. Andrews.  Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models , 1991 .

[36]  Manuel Landajo,et al.  A note on smooth approximation capabilities of fuzzy systems , 2001, IEEE Trans. Fuzzy Syst..

[37]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[38]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[39]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[40]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[41]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

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

[43]  Timothy Paul Cronan,et al.  Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage, Commercial, and Consumer Lending , 1991 .

[44]  Michael Gort,et al.  Analysis of Stability and Change in Market Shares , 1963, Journal of Political Economy.

[45]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

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

[47]  Manuel Landajo,et al.  Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case , 2005, Eur. J. Oper. Res..

[48]  Michael W. Kattan,et al.  A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions , 2000 .

[49]  Nagraj Balakrishnan,et al.  Modeling the relationship between corporate strategy and wealth creation using neural networks , 2000, Comput. Oper. Res..

[50]  M. Landajo,et al.  A note on model-free regression capabilities of fuzzy systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[51]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[52]  Bart Kosko,et al.  Fuzzy Systems as Universal Approximators , 1994, IEEE Trans. Computers.

[53]  Rashmi Malhotra,et al.  Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..