No. 256 Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios

by Paolo Giordani, Tor Jacobson, Erik von Schedvin and Mattias Villani

NOVEMBER 2011

Abstract

We demonstrate improvements in predictive power when introducing spline functions to take account of highly non-linear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive non-linearities yields substantially improved bankruptcy predictions, on the order of 70 to 90 percent, compared with a standard logistic model. The spline model provides several important and surprising insights into non-monotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.

Keywords

bankruptcy risk model, micro-data, logistic spline regression, nancial ratios

JEL

C41, G21, G33, G38