Investigation of financial distress with a dynamic logit based on the linkage between liquidity and profitability status of listed firms

Abstract The scope of this paper is to investigate the predictability of financial distress, adopting a survival model based on dynamic logit for a sample of NYSE listed firms. The main assumption of this study is that liquidity and profitability constitute the key criteria for the configuration of financial distress status of a firm. Specifically, two independent models are applied for the period after the financial crisis of 2007–2008. The first model is constructed on the pillar of liquidity, and the classification into the subgroup of distressed firms is based on specific criteria such as current ratio, current liabilities / total liabilities, Equity / Liabilities and Total Debt / Total Asset. The second model is based on the pillar of profitability where the specific criteria for the classification from the primary group into the subgroup of distressed firms are ROE < ROA and Net Profit Margin ≤ 0. Finally, a third model is established as a result of the combination of the two previous models. A further purpose of this work is to ascertain whether during the period of crisis there has been a differentiation in the policy of listed companies, namely whether their efforts have been shifted to addressing liquidity problems at the expense of profitability.

[1]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[2]  W. Beaver,et al.  Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy , 2004 .

[3]  Koen Vanhoof,et al.  Bankruptcy prediction using a data envelopment analysis , 2004, Eur. J. Oper. Res..

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

[5]  Martin Niemann,et al.  Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity. , 2008 .

[6]  J. Campbell,et al.  In Search of Distress Risk , 2006, SSRN Electronic Journal.

[7]  Deron Liang,et al.  Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study , 2016, Eur. J. Oper. Res..

[8]  Ming-Chang Lee,et al.  Business Bankruptcy Prediction Based on Survival Analysis Approach , 2014 .

[9]  Tyler Shumway Forecasting Bankruptcy More Accurately: A Simple Hazard Model , 1999 .

[10]  Zulridah Mohd Noor,et al.  Corporate Governance and Corporate Failure: a Survival Analysis , 2022 .

[11]  Vineet Agarwal,et al.  Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test , 2014 .

[12]  Chunchi Wu,et al.  Default prediction with dynamic sectoral and macroeconomic frailties. , 2014 .

[13]  Bing Xu,et al.  Performance Evaluation of Bankruptcy Prediction Models: An Orientation Super-efficiency DEA-based Framework , 2015 .

[14]  K. Keasey,et al.  Financial Distress Prediction Models: A Review of Their Usefulness1 , 1991 .

[15]  W. R. Lane,et al.  An application of the cox proportional hazards model to bank failure , 1986 .

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

[17]  Solomon Tesfamariam,et al.  Predicting water main failures using Bayesian model averaging and survival modelling approach , 2015, Reliab. Eng. Syst. Saf..

[18]  Iván Pastor Sanz,et al.  Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks , 2015, Expert Syst. Appl..

[19]  Edward I. Altman,et al.  Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt , 2005 .

[20]  Marjorie B. Platt,et al.  Predicting corporate financial distress: Reflections on choice-based sample bias , 2002 .

[21]  N. Wilson,et al.  Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables , 2013 .

[22]  C. Zopounidis,et al.  A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece , 1999 .

[23]  J. Sinkey A MULTIVARIATE STATISTICAL ANALYSIS OF THE CHARACTERISTICS OF PROBLEM BANKS , 1975 .

[24]  S. Rahman,et al.  Identifying Financial Distress Indicators of Selected Banks in Asia , 2004 .

[25]  Kevin M. Small,et al.  Estimation and Inference , 2013 .

[26]  Yasuo Amemiya,et al.  Instrumental variable estimator for the nonlinear errors-in-variables model , 1985 .

[27]  José Manuel Pereira,et al.  Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal , 2014 .

[28]  Enrico Zio,et al.  Ensemble-approaches for clustering health status of oil sand pumps , 2012, Expert Syst. Appl..

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

[30]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[31]  A. Sule Predicting Financial Distress and the Performance of Distressed Stocks , 2011 .

[32]  Indranil Bose,et al.  Deciding the financial health of dot-coms using rough sets , 2006, Inf. Manag..

[33]  Teija Laitinen,et al.  Comparative analysis of failure prediction methods: the Finnish case , 1999 .

[34]  C. Cardone-Riportella,et al.  EXAMINING WHAT BEST EXPLAINS CORPORATE CREDIT RISK: ACCOUNTING-BASED VERSUS MARKET-BASED MODELS , 2013 .

[35]  David A. Elizondo,et al.  Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks , 2008, Decis. Support Syst..

[36]  J. Barraux,et al.  Revue française de gestion , 1978 .

[37]  Qinghua Huang,et al.  Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches , 2014, Knowl. Based Syst..

[38]  Tzong-Huei Lin,et al.  A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models , 2009, Neurocomputing.

[39]  Abuzar M. A. Eljelly Liquidity ‐ profitability tradeoff: An empirical investigation in an emerging market , 2004 .

[40]  Alicia Mateos-Ronco,et al.  Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain , 2011 .

[41]  Constantin Zopounidis,et al.  Prediction of Greek company takeovers via multivariate analysis of financial ratios , 1997 .

[42]  Erkki K. Laitinen,et al.  Survival analysis as a tool for company failure prediction , 1991 .

[43]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[44]  Jincheng Wang,et al.  Financial Statements Analysis , 2010 .

[45]  Ahsan Habib,et al.  Financial distress, earnings management and market pricing of accruals during the global financial crisis , 2013 .

[46]  R. Jarrow,et al.  Bankruptcy Prediction With Industry Effects , 2004 .

[47]  Suzan Hol,et al.  The influence of the business cycle on bankruptcy probability , 2007, Int. Trans. Oper. Res..

[48]  Van Horne,et al.  Financial Management and Policy , 1968 .

[49]  M. Beynon,et al.  Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .

[50]  Raktim Pal,et al.  Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach , 2006, Eur. J. Oper. Res..

[51]  A. Gregory,et al.  Some New Models for Financial Distress Prediction in the UK , 2010 .

[52]  C. Charalambous,et al.  Predicting Corporate Failure: Empirical Evidence for the UK by , 2001 .

[53]  Thomas S. Shively,et al.  A semiparametric stochastic spline model as a managerial tool for potential insolvency , 2000 .

[54]  Yu Wang,et al.  Financial failure prediction using efficiency as a predictor , 2009, Expert Syst. Appl..

[55]  Sally I. McClean,et al.  A data mining approach to the prediction of corporate failure , 2001, Knowl. Based Syst..

[56]  J. Courtis MODELLING A FINANCIAL RATIOS CATEGORIC FRAMEWORK , 1978 .

[57]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[58]  David C. Mauer,et al.  The Determinants of Corporate Liquidity: Theory and Evidence , 1998, Journal of Financial and Quantitative Analysis.

[59]  P. W. Wilson,et al.  Estimation and inference in two-stage, semi-parametric models of production processes , 2007 .

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

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

[62]  Roger M. Stein Benchmarking default prediction models: pitfalls and remedies in model validation , 2007 .

[63]  Ming-Fu Hsu,et al.  A hybrid approach of DEA, rough set and support vector machines for business failure prediction , 2010, Expert systems with applications.