Predicting financial distress in publicly-traded companies

Felipe Fontaine Rezende,Roberto Marcos da Silva Montezano,Valdir de Jesus Lameira,Fernando Nascimento de Oliveira

doi:10.1590/1808-057x201704460

Felipe Fontaine Rezende, Roberto Marcos da Silva Montezano + Show 2 more

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https://doi.org/10.1590/1808-057x201704460

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Abstract

ABSTRACT Several models for forecasting bankruptcy have been developed over the years, one of the reasons for which is the important part it plays in decision-making. However, forecasting a company’s bankruptcy leaves a very short time for stakeholders to change the situation. It is in this context that this paper arises in order to develop a model for predicting financial distress, which is identified as a step prior to bankruptcy. The predictive model uses the logistic regression technique with panel data and a sample of Brazilian publicly-traded companies with shares listed on the São Paulo Stock, Commodities, and Futures Exchange between 2001 and 2014. As well as financial variables, the final model includes market expectations (macroeconomic) and sector variables. These variables are statistically tested and the hypothesis is confirmed that they improve the accuracy of the model. The research identified the existence of financial distress in 96% of the companies that went bankrupt. In addition, the relationship between the phenomena of bankruptcy and financial distress is verified, using financial and macroeconomic explanatory variables. The results demonstrate that most (83%) of the explanatory variables in the model for predicting bankruptcy are also present in the model for predicting the phenomenon of financial distress. The expected gross domestic product variables and the quick ratio, asset turnover, and net equity over total liabilities financial variables are statistically significant in predicting both phenomena. With this evidence, the study suggests the use of the concept of financial distress as a stage prior to bankruptcy and provides a model for predicting financial distress with 89% accuracy when applied to publicly-traded companies in Brazil in the period examined.

Highlights

IntroductionModels seeking to predict company bankruptcy have been studied with enthusiasm in recent decades in the academic fields (Allen & Saunders, 2004). Horta, Borges, Carvalho, and Alves (2011) and Horta, Alves, and Carvalho (2013) report that bankruptcy forecasting models offer an advanced tool for analysts and credit managers that is free from subjective influences and that makes it possible to obtain a reliable classification regarding a company’s future ability to continue honoring its financial commitments.In their review concerning bankruptcy forecasting models since 1930, Bellovary, Giacomino, and Akers (2007) reach the conclusion that, despite the differences that exist between the forecasting models, the empirical tests for most show a high predictive ability, suggesting that they are useful for many groups, including auditors, managers, creditors, and analysts.Pinheiro, Santos, Colauto, and Pinheiro (2009) stress the importance of updating these models, due to the loss in validity of the coefficients associated with the variables over time
In their review concerning bankruptcy forecasting models since 1930, Bellovary, Giacomino, and Akers (2007) reach the conclusion that, despite the differences that exist between the forecasting models, the empirical tests for most show a high predictive ability, suggesting that they are useful for many groups, including auditors, managers, creditors, and analysts
Before developing of the model for predicting financial distress, the hypotheses raised need to be verified to identify if the event of financial distress precedes the event of bankruptcy

Summary

Introduction

Models seeking to predict company bankruptcy have been studied with enthusiasm in recent decades in the academic fields (Allen & Saunders, 2004). Horta, Borges, Carvalho, and Alves (2011) and Horta, Alves, and Carvalho (2013) report that bankruptcy forecasting models offer an advanced tool for analysts and credit managers that is free from subjective influences and that makes it possible to obtain a reliable classification regarding a company’s future ability to continue honoring its financial commitments.In their review concerning bankruptcy forecasting models since 1930, Bellovary, Giacomino, and Akers (2007) reach the conclusion that, despite the differences that exist between the forecasting models, the empirical tests for most show a high predictive ability, suggesting that they are useful for many groups, including auditors, managers, creditors, and analysts.Pinheiro, Santos, Colauto, and Pinheiro (2009) stress the importance of updating these models, due to the loss in validity of the coefficients associated with the variables over time. Models seeking to predict company bankruptcy have been studied with enthusiasm in recent decades in the academic fields (Allen & Saunders, 2004). Horta, Borges, Carvalho, and Alves (2011) and Horta, Alves, and Carvalho (2013) report that bankruptcy forecasting models offer an advanced tool for analysts and credit managers that is free from subjective influences and that makes it possible to obtain a reliable classification regarding a company’s future ability to continue honoring its financial commitments. Pinheiro, Santos, Colauto, and Pinheiro (2009) stress the importance of updating these models, due to the loss in validity of the coefficients associated with the variables over time. Balcaen and Ooghe (2004) highlight that these losses mainly occur in models that only contemplate financial variables as they do not consider macroeconomic conditions. These models implicitly assume that the relationship between the variables is stable over time

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