Predicting corporate failurethe GRASP-LOGIT Model

  1. Silvia Casado Yusta 1
  2. Laura Nuñez Letamendía 2
  3. Joaquín Antonio Pacheco Bonrostro 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 IE Business School, IE University
Revista:
Revista de métodos cuantitativos para la economía y la empresa

ISSN: 1886-516X

Año de publicación: 2018

Volumen: 26

Páginas: 294-314

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de métodos cuantitativos para la economía y la empresa

Resumen

La predicción de la quiebra empresarial es un problema que goza de una gran relevancia en las ciencias empresariales. En este trabajo se propone un nuevo método para predecir la quiebra empresarial en una muestra de empresas españolas. Concretamente se trata de un algoritmo de selección de variables basado en la estrategia metaheurística GRASP (procedimiento de búsqueda adaptativa aleatoria y voraz) para seleccionar un subconjunto de ratios financieros, como un paso preliminar para estimar un modelo de regresión logística que prediga la quiebra empresarial. La selección de un subconjunto de ratios financieros, de entre todos los disponibles, reduce los costes de adquisición de datos, aumenta la precisión de la predicción al excluir las variables irrelevantes y proporciona información sobre la naturaleza del problema de predicción. Todo lo anterior permite una mejor comprensión del modelo de clasificación final. Nuestro nuevo modelo, al que llamamos modelo GRASP-LOGIT, funciona mejor que una simple regresión logística en el sentido de que alcanza el mismo nivel de capacidad de predicción con menos ratios contables, lo que lleva a una mejor interpretación del modelo y, por lo tanto, a una mejor comprensión del proceso de quiebra empresarial.

€ Ver financiación

Información de financiación

This work has been partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2013-47129-C4-3-R and ECO2016-76567-C4-2-R), the Regional Government of “Castilla y León”, Spain (Project BU329U14) and the Regional Government of “Castilla y León” and FEDER funds (Project BU062U16); all of whom are gratefully acknowledged.

Financiadores

Referencias bibliográficas

  • Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
  • Altman, E.; Marco, G. and Varetto F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks. Journal of Banking and Finance, 18, 505-529.
  • Arslan, O. (2012). Weighted LAD–LASSO method for robust parameter estimation and variable selection in regression. Computational Statistics & Data Analysis, 56 (6), 1952-1965.
  • Baesens, B.; Setiono, R.; Mues, C. and Vanthienen, J. (2003a). Using neural networks rule extaction and decision tables for credit-risk evaluation. Management Science, 49(3), 312-329.
  • Baesens, B.; Van Gestel, T.; Viaene, S.; Stepanova, M.; Suykens, J. and Vanthienen J. (2003b). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627-635.
  • Bala, J.; Dejong, K., Huang, J.; Vafaie, H. and Wechsler, H. (1996). Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation, 4 (3), 297-311.
  • Balcaen, S. and Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38 (1), 63-93.
  • Beaver, W. (1966). Financial ratios as predictors of failures. In: S. Davidson (ed.), Empirical Research in Accounting: Selected Studies (pp. 71-111), Chicago: Institute of Professional Accounting.
  • Cortes, C. and Vapnik, V. N. (1995). Support-vector networks. Machine Learning, 20 (3). 273-297.
  • Cotta C.; Sloper, C. and Moscato, P. (2004). Evolutionary search of thresholds for robust feature set selection: Application to the analysis of microarray data. Lecture Notes in Computer Science, 3005, 21-30.
  • Crone, S. F. and Finlay, S. (2012). Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting, 28(1), 224-238.
  • Curran, S. and Mingers J. (1994). Neural networks, decision tree induction and discriminant analysis: An empirical comparison. Journal of the Operational Research Society, 45 (4), 440-450.
  • Dambolena, I. G. and Khoury, S. J. (1980). Ratio stability and corporate failure. Journal of Finance, 35, 1017-1026.
  • Etheridge, H. L. and Sriram, R. S. (1996). A Neural Network Approach to Financial Distress Analysis. In: S. G. Sutton (ed.), Advances in Accounting Information Systems, Volume 4 (pp.201-222), Bingley: Emerald Group Publishing.
  • Fayyad, U. M. and Irani, K. B. (1992). On the handling of continuous-valued attributes in decision tree generation. Machine Learning, 8, 87-102.
  • Feo, T. A. and Resende M. G. C. (1989). A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters, 8 (2), 67-71.
  • Feo, T. A. and Resende M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 2, 1-27.
  • Frydman, H.; Altman, E. I. and Kao, D. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance, 40(1), 269-291.
  • Ganster, H.; Pinz, A.; Rohrer, R.; Wildling, E.; Binder, M. and Kittler, H. (2001). Automated melanoma recognition. IEEE Transactions on Medical Imaging, 20 (3), 233-239.
  • Hua, Z.; Wang, Y.; Xu, X.; Zhang, B. and Liang, L. (2007). Predicting corporate financial distress on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440.
  • Iturriaga, F. J. L. and Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with Applications, 42(6), 2857-2869.
  • Jain, A. and Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153-158.
  • Jeong C.; Min, J. N. and Kim, M. S. (2012). A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction. Expert Systems with Applications, 39(3), 3650-3658.
  • Jones, F. L. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131-164.
  • Jones, S. and Hensher, D. A. (2004). Predicting firm financial distress: a mixed logit model. The Accounting Review, 79 (4), 1011-1038.
  • Jourdan, L.; Dhaenens, C. and Talbi, E. (2001). A genetic algorithm for feature subset selection in data-mining for genetics. In: J. P. de Sousa (ed.), Proceedings of the 4th Metaheuristics International Conference (pp. 29-34), Porto: MIC.
  • Kohavi, R. (1995). Wrappers for performance enhancement and oblivious decision graphs. Ph. D. Thesis, Computer Science Department, Stanford University.
  • Laitinen, E. K. and Laitinen, T. (2000). Bankruptcy prediction. Application of the Taylor’s expansion in logistic regression. International Review of Financial Analysis, 9, 327-349.
  • Lee, S. H. and Urrutia, J. L. (1996). Analysis and prediction of insolvency in the property-liability insurance industry: A comparison of logit and hazard models. The Journal of Risk and Insurance, 63(1), 121-130.
  • Lee, S.; Yang, J. and Oh, K. W. (2003). Prediction of molecular bioactivity for drug design using a decision tree algorithm. Lecture Notes in Artificial Intelligence, 2843, 344-351.
  • Lennox, C. (1999). Identifying failing companies: A reevaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.
  • Lewis, P. M. (1962). The characteristic selection problem in recognition systems. IEEE Transactions on Information Theory, 8, 171-178.
  • Li, H.; Lee, Y. C.; Zhou, Y. C. and Sun, J. (2011). The random subspace binary logit (RSBL) model for bankruptcy prediction. Knowledge-Based Systems, 24 (8), 1380-1388.
  • Liang, D.; Lu, C. C.; Tsai, C. F. and Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572.
  • Liu, H. and Motoda, H. (1998). Feature selection for knowledge discovery and data mining. Boston: Kluwer Academic.
  • Lu, Y.; Zeng, N.; Liu, X. & Yi, S. (2015). A new hybrid algorithm for bankruptcy prediction using switching particle swarm optimization and support vector machines. Discrete Dynamics in Nature and Society, 2015, Article ID 294930, 7 pp.
  • Mangalova, E. and Agafonov, E. (2014). Wind power forecasting using the k-nearest neighbors algorithm. International Journal of Forecasting, 30(2), 402-406.
  • Matsui, H. (2014). Variable and boundary selection for functional data via multiclass logistic regression modeling. Computational Statistics & Data Analysis, 78, 176-185.
  • Meiri, R. and Zahavi, J. (2006). Using simulated annealing to optimize the feature selection problem in marketing applications. European Journal of Operational Research, 171, 842-858.
  • Messier Jr., W. F. and Hansen, J. V. (1988). Inducing rules for expert system development: An example using default and bankruptcy data. Management Science, 34 (12), 1403-1415.
  • Murphy, P. M. and Aha, D. W. (1994). UCI Repository of Machine Learning. Department of Information and Computer Science, University of California.
  • Narendra, P. M. and Fukunaga, K. (1977). A Branch and Bound algorithm for feature subset selection. IEEE Transactions on Computers, 26(9), 917-922.
  • Neophytou, E. and Molinero, C. M. (2004). Predicting corporate failure in the UK: a multidimensional scaling approach. Journal of Business Finance and Accounting, 31(5-6), 677-710.
  • Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-111.
  • Oliveira, L. S.; Sabourin, R.; Bortolozzi, F. and Suen, C. Y. (2003). A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence, 17(6), 903-929.
  • Ooghe, H. and Balcaen, S. (2002). Are failure prediction models transferable from one country to another? An empirical study using Belgian financial statements. Vlerick Leuven Gent Management School Working Paper, 2002-3, 42 pp.
  • Pacheco, J.; Alfaro, E.; Casado, S.; Gámez, M. and García, N. (2012). A GRASP Method for Building Classification Trees. Expert Systems with Applications, 39(3), 3241-3248.
  • Pacheco, J.; Casado, S. and Núñez, L. (2009). A variable selection method based on tabu search for logistic regression models. European Journal of Operational Research, 199, 506-511.
  • Pacheco, J.; Casado, S.; Núñez, L. and Gómez, O. (2006). Analysis of new variable selection methods for discriminant analysis. Computational Statistics & Data Analysis, 51, 1463-1478.
  • Pitsoulis, L. S. and Resende, M. G. C. (2002). Greedy randomized adaptive search procedures. In: P. M. Pardalos and M. G. C. Resende (eds.), Handbook of Applied Optimization, Oxford: Oxford University Press.
  • Pompe, P. P. M. and Bilderbeek, J. (2005). The prediction of small- and medium-sized industrial firms. Journal of Business Venturing, 20(6), 847-868.
  • Reunanen, J. (2003). Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research, 3, 1371-1382.
  • Sarkar, S. and Sriram, R. S. (2001). Bayesian models for early warning of bank failures. Management Science, 47(11), 1457-1475.
  • Scott, J. (1977). Bankruptcy, secured debt and optimal capital structure. Journal of Finance, 33, 1-19.
  • Scott, J. (1981). The probability of bankruptcy. A comparison of empirical predictions and theoretical models. Journal of Banking and Finance, 5, 317-344.
  • Sebestyen, G. (1962). Decision-making processes in pattern recognition. New York: Macmillan.
  • Sexton, R. S.; Sriram, R.S. and Etheridge, H. (2003). Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach. Decision Sciences, 34(3) 421-442.
  • Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), 101-124.
  • Varetto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance, 22, 1421-1439.
  • Wang, H.; Li, G. and Jiang, G. (2007). Robust regression shrinkage and consistent variable selection through the LAD-Lasso. Journal of Business and Economic Statistics, 25(3), 347-355.
  • Wu, C. H.; Tzeng, G. H.; Goo, Y. J. and Fang, W. C. ( 2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications, 32(2), 397-408.
  • Yang, Z.; You, W. and Ji, G. (2011). Using partial least squares and support vector machines for bankruptcy prediction. Expert Systems with Applications, 38(7), 8336-8342.
  • Zhang, C. X.; Wang, G. W. and Liu, J. M. (2015). RandGA: Injecting randomness into parallel genetic algorithm for variable selection. Journal of Applied Statistics, 42(3), 630-647.
  • Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(Supplement), 59-82.
Fuente de los datos: Dialnet