Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios
在逻辑破产模型中引入样条函数处理杠杆、盈利和流动性与企业失败之间的高度非线性关系，相比标准模型预测能力提升70%-90%，并发现低杠杆和高盈利企业风险更高。
Abstract We demonstrate improvements in predictive power when introducing spline functions to take account of highly nonlinear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive nonlinearities yields substantially improved bankruptcy predictions, on the order of 70%–90%, compared with a standard logistic model. The spline model provides several important and surprising insights into nonmonotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than those given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.