Predicting Bankruptcy with Robust Logistic Regression

Richard P Hauser,David Booth

doi:10.6339/jds.201110_09(4).0006

Abstract

Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator versus maximum likelihood (ML) logistic regression. With both the 2006 and 2007 data, BY robust logistic regression improves both the classification of bankrupt firms in the training set and the prediction of bankrupt firms in the testing set. In an out of sample test, the BY robust logistic regression correctly predicts bankruptcy for Lehman Brothers; however, the ML logistic regression never predicts bankruptcy for Lehman Brothers with either the 2006 or 2007 data. Our analysis indicates that if the BY robust logistic regression significantly changes the estimated regression coefficients from ML logistic regression, then the BY robust logistic regression method can significantly improve the classification and prediction of bankrupt firms. At worst, the BY robust logistic regression makes no changes in the estimated regression coefficients and has the same classification and prediction results as ML logistic regression. This is strong evidence that BY robust logistic regression should be used as a robustness check on ML logistic regression, and if a difference exists, then BY robust logistic regression should be used as the primary classifier.

Highlights

The prediction of corporate bankruptcy is an important and widely studied topic (Wilson and Sharda, 1994)
The main purpose of this study was to investigate the accuracy of predicting bankruptcy using Bianco and Yohai (BY) robust logistic regression versus maximum likelihood (ML) logistic regression
In the 2006 data (Table 2), the BY robust logistic regression improved the prediction of bankrupt firms over ML logistic regression from 16.7% to 29.2% correct

Summary

Introduction

The prediction of corporate bankruptcy is an important and widely studied topic (Wilson and Sharda, 1994). Creditors and investors in corporations need to be able to predict the probability of default for profitable business decisions. Accurate assessment of the probability of bankruptcy can lead to sounder lending practices as well as better fair value estimates of interest rates that reflect credit risks. The need to predict corporate bankruptcy goes beyond banks. Accounting firms may risk lawsuits if the auditors. More ubiquitous in business are derivative contracts where firms must often assess their counterparty risk. Much of the credit or counterparty risk assessment was to use ratings issued by the standard credit rating agencies. There is a great need to develop accurate quantitative models for prediction of corporate bankruptcy

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Conclusion