The Logistic Lasso and Ridge Regression in Predicting Corporate Failure

Abstract The prediction of corporate bankruptcy is a phenomenon of interest to investors, creditors, borrowing firms, and governments alike. Many quantitative methods and distinct variable selection techniques have been employed to develop empirical models for predicting corporate bankruptcy. For the present study the lasso and ridge approaches were undertaken, since they deal well with multicolinearity and display the ideal properties to minimize the numerical instability that may occur due to overfitting. The models were employed to a dataset of 2032 non-bankrupt firms and 401 bankrupt firms belonging to the hospitality industry, over the period 2010-2012. The results showed that the lasso and ridge models tend to favor the category of the dependent variable that appears with heavier weight in the training set, when compared to the stepwise methods implemented in SPSS.

[1]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[2]  Mee Young Park,et al.  L1‐regularization path algorithm for generalized linear models , 2007 .

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

[4]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[5]  K. Keasey,et al.  Non‐Financial Symptoms and the Prediction of Small Company Failure: A Test of Argenti's Hypotheses , 1987 .

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

[7]  R. Schaefer,et al.  A ridge logistic estimator , 1984 .

[8]  Robert Tibshirani,et al.  The Entire Regularization Path for the Support Vector Machine , 2004, J. Mach. Learn. Res..

[9]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[10]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[11]  Dae-Ki Kang,et al.  Ensemble with neural networks for bankruptcy prediction , 2010, Expert Syst. Appl..

[12]  M. Iorio,et al.  A semi-automatic method to guide the choice of ridge parameter in ridge regression , 2012, 1205.0686.

[13]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[14]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[15]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

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

[17]  Yan Yu,et al.  Variable selection and corporate bankruptcy forecasts , 2015 .

[18]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[19]  Clive S. Lennox,et al.  Identifying failing companies: a re-evaluation of the logit, probit and DA approaches , 1999 .

[20]  T. Santner,et al.  On the small sample properties of norm-restricted maximum likelihood estimators for logistic regression models , 1989 .

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

[22]  Kyoung-jae Kim,et al.  Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach , 2009, Appl. Soft Comput..

[23]  Jianqing Fan,et al.  Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .

[24]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[25]  Hian Chye Koh,et al.  A neural network approach to the prediction of going concern status , 1999 .

[26]  Bonifacio Martín del Brío,et al.  Predicción de la quiebra bancaria mediante el empleo de redes neuronales artificiales , 1993 .

[27]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[28]  Pamela K. Coats,et al.  A neural network for classifying the financial health of a firm , 1995 .

[29]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[30]  R. Moyer.  Forecasting Financial Failure: A Re-Examination , 1977 .

[31]  Marc Blum.  FAILING COMPANY DISCRIMINANT-ANALYSIS , 1974 .

[32]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

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

[34]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[35]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

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

[37]  Daniel Martin,et al.  Early warning of bank failure: A logit regression approach , 1977 .