du Jardin, Philippe
(2012):
*The influence of variable selection methods on the accuracy of bankruptcy prediction models.*
Published in: Bankers, Markets & Investors
No. 116
(January 2012): pp. 20-39.

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## Abstract

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the influence of the variable selection method. The aim of our research is to evaluate the prediction accuracy of models designed with various classification techniques and variables selection methods. As a result, we demonstrate that a search strategy cannot be designed without considering the characteristics of the modeling technique and that the fit between the variable selection method and the technique used to design models is a key factor in performance.

Item Type: | MPRA Paper |
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Original Title: | The influence of variable selection methods on the accuracy of bankruptcy prediction models |

Language: | English |

Keywords: | Bankruptcy prediction; Forecasting model; Variable selection |

Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation |

Item ID: | 44383 |

Depositing User: | Professor Philippe du Jardin |

Date Deposited: | 15 Feb 2013 17:09 |

Last Modified: | 28 Sep 2019 13:37 |

References: | Agarwal A., 1999, Abductive networks for two-group classification: A comparison with neural networks, Journal of Applied Business Research, vol. 15, n° 2, pp. 1-12. Alfaro E., Garcia N., Games M., Ellison D. 2008, Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks, Decision Support Systems, vol. 45, pp. 110-122. Altman E. I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, vol. 23, n° 4, pp. 589-609. Altman E. I., Haldeman R., Narayanan P., 1977, Zeta analysis: A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, vol. 1, n° 1, pp. 29-51. Altman E. I., Marco G., Varetto F., 1994, Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural network – The Italian experience, Journal of Banking and Finance, vol. 18, n° 3, pp. 505-529. Atiya A. F., 2001, Bankruptcy prediction for credit risk using neural networks: A survey and new results, IEEE Transactions on Neural Networks, vol. 12, n° 4, pp. 929-935. Aziz A., Emanuel D. C., Lawson G. C., 1988, Bankruptcy prediction: An investigation of cash flow based models, Journal of Management Studies, vol. 25, n° 5, pp. 419-437. Back B., Oosterom G., Sere K., Van Wezel M., 1994, A comparative study of neural networks in bankruptcy prediction, Proceedings of the 10th Conference on Artificial Intelligence Research in Finland, Finnish Artificial Intelligence Society, pp. 140-148. Back B., Laitinen T., Sere K., 1996, Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, vol. 11, n° 4, pp. 407-413. Back B., Laitinen T., Sere K., Van Wezel M., 1996, Choosing bankruptcy predictors using discriminant analysis, Logit analysis and genetic algorithms, Turku Centre for Computer Science, Technical Report, n° 40, 18 p. Back B., Laitinen T., Hekanaho J., Sere K., 1997, The effect of sample size on different failure prediction methods, Turku Centre for Computer Science, Technical Report, n° 155, 23 p. Baranoff E.G., Sager T. W., Shively T. S., 2000, A semiparametric stochastic spline model as a managerial tool for potential insolvency, Journal of Risk and Insurance, vol. 67, n° 3, pp. 369-396. Bardos M., 1995, Détection précoce des défaillances d’entreprises à partir des documents comptables, Bulletin de la Banque de France, Supplément Études, 3ème trimestre, pp. 57-71. Barniv R., Hershbarger A., 1990, Classifying financial distress in the life insurance industry, Journal of Risk and Insurance, vol. 57, n° 1, pp. 110-136. Barniv R., Raveh A., 1989, Identifying financial distress: A new non-parametric approach, Journal of Business Finance and Accounting, vol. 16, n° 3, pp. 361-383. Beaver W. H., 1966, Financial Ratios as predictors of failure, empirical research in accounting, selected studies, Journal of Accounting Research, Supplement, vol. 4, pp. 71-111. Boritz J. E., Kennedy D. B., 1995, Effectiveness of neural network types for prediction of business failure, Expert Systems with Applications, vol. 9, n° 4, pp. 503-512. Bose I., Pal R., 2006, Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach, European Journal of Operational Research, vol. 174, n° 2, pp. 959-982. Boyacioglu M. A., Kara Y., Baykan O. K., (2009), 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, Expert Systems with Applications, vol. 36, pp. 3355–3366. Brabazon A., Keenan P. B., 2004, A hybrid genetic model for the prediction of corporate failure, Computational Management Science, Special Issue, vol. 1, n° 3-4, pp. 293-310. Breiman L., 1996, Bagging predictors, Machine Learning, vol. 24, n° 2, pp. 123-140. Charalambous C., Charitou A., Kaourou F., 2000, Application of feature extractive algorithm to bankruptcy prediction, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, July 24-27, Como, Italy, vol. 5, pp. 303-308. Chava S., Jarrow R. A., 2004, Bankruptcy prediction with industry effects, Review of Finance, vol. 8, n° 4, pp. 537-569. Chen W. S., Du Y. K., 2009, Using neural networks and data mining techniques for the financial distress prediction model, Expert Systems with Applications, vol. 36, pp. 4075-4086. Cortes E. A., Martinez M. G., Rubio N. G., 2007, Multiclass corporate failure prediction by Adaboost.M1, International Advances in Economic Research, vol. 13, n° 3, pp. 301-312. Deakin E. B., 1972, A discriminant analysis of predictors of business failures, Journal of Accounting Research, vol. 10, n° 1, pp. 167-179. Dimitras A. I., Slowinski R., Susmaga R., Zapounidis C., 1999, Business failure prediction using rough sets, European Journal of Operational Research, vol. 114, n° 2, pp. 263-280. Doumpos M., Zopounidis C., 1999, A multicriteria discrimination method for the prediction of financial distress: The case of Greece, Multinational Finance Journal, vol. 3, n° 2, pp. 71-101. Elmer P. J., Borowski D. M. (1988), An expert system approach to financial analysis: The case of savings and loan bankruptcy, Financial Management, vol. 17, n° 3, pp. 66-76. Etheridge H. L., Sriram R. S., 1997, A comparison of the relative costs of financial distress models: Artificial neural networks, Logit and multivariate discriminant analysis, International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 6, n° 3, pp. 235-248. Frydman H., Altman E. I., Kao D., 1985, Introducing recursive partitioning for financial classification: The case of financial distress, Journal of Finance, vol. 40, n° 1, pp. 269-291. Goss E. P., Ramchandani H., 1995, Comparing classification accuracy of neural networks, binary Logit regression and discriminant analysis for insolvency prediction of life insurer, Journal of Economics and Finance, vol. 19, n° 3, pp. 1-18. Grandvalet Y., 2004, Bagging equalizes influence, Machine Learning, vol. 55, n° 3, pp. 251-270. Grice J. S., Dugan M. T., 2003, Reestimations of the Zmijewski and Ohlson Bankruptcy Prediction Models, Advances in Accounting, vol. 20, pp. 77-93. Gupta M. C., 1969, The effect of size, growth, and industry on the financial structure of manufacturing companies, Journal of Finance, vol. 24, n° 3, pp. 517-529. Gupta Y. P., Rao R. P., Bagchi P. K., 1990, Linear goal programming as an alternative to multivariate discriminant analysis: A Note, Journal of Business Finance and Accounting, vol. 17, n° 4, pp 593-598. Hornik K., Stinchombe M., White H., 1990, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural Networks, vol. 3, pp. 551-560. Hornik K., 1991, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol. 4, pp. 251-257. Hornik K., Stinchombe M., White H., Auer P., 1994, Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives, Neural Computing, vol. 6, pp. 1262-1275. Jo H., Han I., Lee H., 1997, Bankruptcy prediction using case-based reasoning, Neural Networks and Discriminant Analysis, Expert Systems with Applications, vol. 13, n° 2, pp. 97-108. John G. H., Kohavi R, Pfleger K., 1994, Irrelevant features and the subset selection problem, in machine learning: Proceedings of the 11th International Conference, New Brunswick, New Jersey, July 10-15, pp. 121-129. Jones S., Hensher D. A., 2004, Modelling corporate failure: A multinomial nested Logit analysis for unordered outcomes, British Accounting Review, vol. 39, n° 1, pp. 89-107. Kiviluoto K., 1998, Predicting bankruptcies with the Self-Organizing Map, Neurocomputing, vol. 21, n° 1-3, pp. 191-220. Kohavi R., 1995, A study of cross validation and bootstrap for accuracy estimation and model selection, Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, vol. 2, pp. 1137-1143. Kotsiantis S., Tzelepis D., Koumanakos E., Tampakas V., 2005, Efficiency of machine learning techniques in bankruptcy prediction, 2d International Conference on Enterprise Systems and Accounting, Thessaloniki, Greece, July 11-12, pp. 39-49. Kumar N., Krovi R., Rajagopalan B., 1997, Financial decision support with hybrid genetic and neural based modeling tools, European Journal of Operational Research, vol. 103, n° 2, pp. 339-349. Laitinen T., Kankaanpaa M., 1999, Comparative analysis of failure prediction methods: The Finnish case, European Accounting Review, vol. 8, n° 1, pp. 67-92. Laitinen E. K., Laitinen T. 2000, Bankruptcy prediction: Application of the Taylor's expansion in logistic regression, International Review of Financial Analysis, vol. 9, n° 4, pp. 327-349. Lensberg T., Eilifsen A., McKee T. E., 2006, Bankruptcy Theory development and classifi-cation via genetic programming, European Journal of Operational Research, vol. 169, pp. 677-697. Leray P., Gallinari P., 1998, Feature selection with neural networks, Behaviormetrika, vol. 26, n° 1, pp.145-166. Leshno M., Spector Y., 1996, Neural network prediction analysis: The bankruptcy case, Neurocomputing, vol. 10, n° 2, pp. 125-147. Lussier R. N., 1995, A nonfinancial business success versus failure prediction model for young firms, Journal of Small Business Management, vol. 33, n° 1, pp. 8-20. Malecot J. F., 1991, Analyses théoriques des défaillances d’entreprise : une revue de la littérature, Revue d'économie financière, n° 19, pp. 205-227. Mensah Y. M., 1984, An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study, Journal of Accounting Research, vol. 22, n° 1, pp. 380-395. Min S. H., Lee J., Han I., 2006, Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Expert Systems with Applications, vol. 31, n° 3, p 652-660. Mossman C. E., Bell G. G., Swartz L. M.; Turtle H., 1998, An empirical comparison of bankruptcy models, Financial Review, vol. 33, n° 2, pp. 35-53. Neophytou E., Mar-Molinero C., 2004, Predicting corporate failure in the UK: A multidi-mensional scaling approach, Journal of Business Finance and Accounting, vol. 31, n° 5-6, pp. 677-710. Odom M. C., Sharda R., 1990, A neural network model for bankruptcy prediction, Proceedings of the IEEE International Joint Conference on Neural Networks, San Diego, California, vol. 2, pp. 163-168. Ohlson J. A., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, vol. 18, n° 1, pp. 109-131. Ooghe H., Claus H., Sierens N., Camerlynck J., 1999, International comparison of failure prediction models from different countries: An empirical analysis, Ghent University, Working Paper, n° 99-79. Platt H. D., Platt M. B., 1990, Development of a class of stable predictive variables: The Case of bankruptcy prediction, Journal of Business Finance and Accounting, vol. 17, n° 1, pp. 31-51. Platt H. D., Platt M. B., Pedersen J. G., 1994, Bankruptcy discrimination with real variables, Journal of Business Finance and Accounting, vol. 21, n° 4, pp. 491-510. Pompe P. P. M., Bilderbeek J., 2005, Bankruptcy prediction: The influence of the year prior to failure selected for model building and the effects in a period of economic decline, International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 13, n° 6, pp. 95-112. Premachandra I. M., Bhabra G. S., Sueyoshi T., 2009, DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique, European Journal of Operational Research, vol. 193, pp.412-424. Salchenberger L. M., Cinar E. M., Lash N. A., 1992, Neural networks: A new tool for predicting thrift failures, Decision Sciences, vol. 23, n° 4, pp. 899-916. Sexton R. S., Sriram R. S., Etheridge H., 2003, Improving decision effectiveness of artificial neural networks: A modified genetic algorithm approach, Decision Sciences, vol. 34, n° 3, pp. 421-442. Shin K. S., Lee Y. J., 2003, A Genetic algorithm application in bankruptcy prediction modeling, Expert Systems with Applications, vol. 23, n° 3, pp. 321-328. Tam K. Y., Kiang, M. Y., 1992, Managerial applications of neural networks: The case of bank failure predictions, Management Science, vol. 38, n° 7, pp. 926-947. Tung W. L., Quek C., Cheng P., 2004, GenSo-EWS: A Novel neural-fuzzy based early warning system for predicting bank failures, Neural Networks, vol. 17, n° 4, pp. 567-587. Tyree E. W., Long J. A., 1996, Bankruptcy prediction models: Probabilistic neural networks versus discriminant analysis and backpropagation neural networks, City University, School of Informatics, Department of Business Computing, Working Paper. Wallrafen J., Protzel P., Popp H., 1996, Genetically Optimized neural network classifiers for bankruptcy prediction – An Empirical study, Proceedings of the 29th Hawaii International Conference on System Sciences, January 3-6, Maui, Hawaii, vol. 2, pp. 419-426. West D., Dellana S., Qian J., 2005, Neural network ensemble strategies for financial decision application, Computers and Operations Research, vol. 32, n° 10, pp. 2543-2559. Wilson R. L., Sharda R. (1994), Bankruptcy prediction using neural networks, Decision Support System, vol. 11, n° 5, pp. 545-557. Yacoub M., Bennani Y., 1997, HVS: A heuristic for variable selection in multilayer artificial neural network classifier, Proceedings of the International Conference on Artificial Neural Networks and Intelligent Engineering, Saint Louis (Missouri), pp. 527-532. Yang Z. R., Platt M. B., Platt H. D., 1999, Probabilistic neural networks in bankruptcy prediction, Journal of Business Research, vol. 44, n° 2, pp. 67-74. Zmijewski M. E., 1984, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, vol. 22, Supplement, pp. 59-82. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/44383 |