Banking failure predictiona boosting classification tree approach

  1. Alexandre Momparler 3
  2. Pedro Carmona 1
  3. Francisco Climent 2
  1. 1 Universitat de València. Departament de Comptabilitat
  2. 2 Universitat de València. Departament d'Economia Financera i Actuarial
  3. 3 Universitat de València. Departament de Finances Empresarials
Mostrar afiliaciones +
Revista:
Revista española de financiación y contabilidad

ISSN: 0210-2412

Año de publicación: 2016

Volumen: 45

Número: 1

Páginas: 63-91

Tipo: Artículo

DOI: 10.1080/02102412.2015.1118903 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: Revista española de financiación y contabilidad

Resumen

La reciente crisis financiera muestra que el fracaso de algunas instituciones financieras puede producir la quiebra en cadena de otras entidades financieras y, en última instancia, originar graves problemas al sistema financiero mundial. Los bancos de la zona euro que experimentaron problemas de liquidez o solvencia durante las turbulencias de los mercados financieros fueron rescatados por sus gobiernos nacionales con el apoyo y la supervisión financiera de la Unión Europea. En este trabajo se aplica la metodología Boosting Classification Tree con el objeto de predecir el fracaso en el sector bancario identificando cuatro indicadores clave, cuyo seguimiento es primordial para anticipar y prevenir problemas financieros en dicho sector. La muestra utilizada en este estudio se compone de series anuales de 25 ratios financieras de 155 bancos de la Zona Euro para el período 2006–2012. Los resultados indican que a mayor tamaño, mayores ingresos extraordinarios y mayor ratio préstamos/depósitos, más probable es el fracaso de un banco. En cambio, cuanto mayor sea la ratio préstamos al interbancario/prestamos del interbancario, la probabilidad de que la entidad tenga problemas financieros es menor. Por lo tanto, con el objetivo de mejorar su estabilidad financiera, la banca debería financiar su actividad crediticia principalmente a través de los depósitos de los clientes, evitando una dependencia excesiva de fuentes de ingresos extraordinarios no recurrentes.

€ Ver financiación

Información de financiación

This work was supported by Ministerio de Econom?a y Competitividad [grant number ECO2013-40816-P].

Financiadores

Referencias bibliográficas

  • E.Alfaro,, N.García,, M.Gámez,, & D.Elizondo, (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45, 110–122. doi:10.1016/j.dss.2007.12.002
  • A.Assadian,, & J.M.Ford, (1997). Determinants of business failure: The role of firm size. Journal of Economics and Finance, 21, 15–23. doi:10.1007/BF02929018
  • F.Betz,, S.Oprică,, T.A.Peltonen,, & P.Sarlin, (2014). Predicting distress in European banks. Journal of Banking & Finance, 45, 225–241. doi:10.1016/j.jbankfin.2013.11.041
  • V.Calcagno,, & C.Mazancourt, (2010). Glmulti: An R package for easy automated modelselection with (generalized) linear models. Journal of Statistical Software, 34(12), 1–29. doi:10.18637/jss.v034.i12
  • M.Chambers,, & T.W.Dinsmore, (2015). Advanced analytics methodologies: Driving business value with analytics. Upper Saddle River, NJ: Pearson Education.
  • E.A.Cortés,, M.G.Martínez,, & N.G.Rubio, (2008). Linear discriminant analysis versus adaboost for failure forecasting. Revista Española De Financiación Y Contabilidad, XXXVII(137, Enero–Marzo), 13–32. doi:10.1080/02102412.2008.10779637
  • Y.Demyanyk,, & I.Hasan, (2010). Financial crises and bank failures: A review of prediction methods. Omega, 38, 315–324. doi:10.1016/j.omega.2009.09.007
  • R.DeYoung,, & G.Torna, (2013). Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation, 22, 397–421. doi:10.1016/j.jfi.2013.01.001
  • J.Elith,, J.R.Leathwick,, & T.Hastie, (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813. doi:10.1111/j.1365-2656.2008.01390.x
  • A.Ellul,, & V.Yerramilli, (2013). Stronger risk controls, lower risk: Evidence from U.S. Bank Holding Companies. The Journal of Finance, LXVIII(5), 1757–1803. doi:10.1111/jofi.12057
  • European Commission. (2012). Commission staff working document. Brussels: Summary of the Impact Assessment. 6.6.2012; SWD (2012) 167.
  • European Commission Competition. (2011, October). The effects of temporary state aid rules adopted in the context of the financial and economic crisis (Commission Staff Working Paper). Brussels: Author.
  • J.Friedman, (2001). Greedy function approximation: A gradient boosting machine. TheAnnals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451
  • J.H.Friedman, (2002a). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. doi:10.1016/S0167-9473(01)00065-2
  • J.H.Friedman, (2002b). Tutorial: Getting Started with MART in R Retrieved from http://statweb.stanford.edu/~jhf/r-mart/tutorial/tutorial.pdf
  • N.Gambetta,, A.Zorio-Grima,, & M.A.García-Benau, (2015). Complaints management and bank risk profile. Journal of Business Research, 68, 1599–1601. doi:10.1016/j.jbusres.2015.02.002
  • M.García-Posada,, & J.S.Mora-Sanguinetti, (2014). Are there alternatives to bankruptcy? A study of small business distress in Spain. Journal of the Spanish Economic Association, 5, 287–332.
  • T.Hastie,, R.Tibshirani,, & J.Friedman, (2009). The elements of statistical learning. New York, NY: Sprinter.
  • R.J.Hijmans,, S.Phillips,, J.Leathwick,, & J.Elith, (2015). Dismo: Species distribution modeling. R package version 1.0-12. Retrieved from http://CRAN.R-project.org/package=dismo
  • G.James,, D.Witten,, T.Hastie,, & R.Tibshirani, (2013). An introduction to statistical learning (2nd ed.). New York, NY: Springer.
  • J.Y.Jin,, K.Kanagaretnam,, & G.J.Lobo, (2011). Ability of accounting and audit quality variables to predict bank failure during the financial crisis. Journal of Banking & Finance, 35, 2811–2819. doi:10.1016/j.jbankfin.2011.03.005
  • M.J.Kim,, D.K.Kang,, & H.B.Kim, (2015). Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction. Expert Systems with Applications, 42(3), 1074–1082.
  • M.Kuhn, (2015). Caret: Classification and Regression Training. R package version 6.0-41. Retrieved from http://CRAN.R-project.org/package=caret
  • M.Kuhn,, & K.Johnson, (2013). Applied predictive modeling. New York, NY: Springer.
  • J.R.Leathwick,, J.Elith,, M.P.Francis,, T.Hastie,, & P.Taylor, (2006). Variation in demersal fish species richness in the oceans surrounding New Zealand: An analysis using boosted regression trees. Marine Ecology Progress Series, 321, 267–281. doi:10.3354/meps321267
  • A.I.McLeod,, & C.Xu, (2014). Bestglm: Best Subset GLM. R package version 0.34. Retrieved from http://CRAN.R-project.org/package=bestglm
  • A.Natekin,, & A.Knoll, (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7(21). doi:10.3389/fnbot.2013.00021
  • R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/
  • G.Ridgeway, (2009). Generalized Boosted Models: A Guide to the gbm Package. Retrieved from https://r-forge.r-project.org/scm/viewvc.php/*checkout*/pkg/inst/doc/gbm.pdf?revision=18&root=gbm&pathrev=22
  • G.Ridgeway, (2015). Gbm: Generalized Boosted Regression Models. R package version 2.1.1. Retrieved from http://CRAN.R-project.org/package=gbm
  • P.Savona,, R.De Lisa,, E.Dorodnykh,, & F.Vallascas, (2013, June). Explaining state support to European banks during the crisis: Which implications for regulation? Fondo interbancario di tutela dei depositi. Interbank deposit protection fund (Working Paper No. 12). Interbank deposit protection fund [Fondo interbancario di tutela dei depositi].
  • R.Schapire, (2003). The boosting approach to machine learning - an overview. In D.D.Denison, M.H.Hansen, C.Holmes, B.Mallick, & B.Yu (eds), MSRI workshop on nonlinear estimation and classification, 2002. New York, NY: Springer.
  • J.Sun,, H.Li,, Q.-H.Huang,, & K.-Y.He, (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. doi:10.1016/j.knosys.2013.12.006
  • G.Wang,, J.Ma,, & S.Yang, (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353–2361. doi:10.1016/j.eswa.2013.09.033
Fuente de los datos: Dialnet