In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional logistic and neural network models, using out-of-sample analysis, in terms of discriminatory power, information content and economic impact while we forecast bankruptcy one year ahead, two years ahead but also financial distress, which is a situation that precedes firm bankruptcy. Using US public firms over the period 1990–2015, in all, we find that training models to maximize AUROC, provides more accurate out-of-sample forecasts relative to training them with traditional methods, such as maximizing the log-likelihood function, highlighting the benefits arising by using models with maximized AUROC. Among all models, however, a neural network trained with our method is the best performing one, even when we compare it with other methods proposed in the literature to maximize AUROC. Finally, our results are more pronounced when we increase the forecasting difficulty, such as forecasting financial distress. The implementation of our method to train bankruptcy models is robust in various settings and therefore well-justified.

在本文中，我们通过最大化接收者操作特征 (AUROC) 曲线下的面积所测量的判别力来估计破产预测模型的系数，例如逻辑和神经网络模型。介绍了一种方法，并与传统的逻辑和神经网络模型进行了比较，使用样本外分析，在我们预测未来一年、两年和财务困境的同时，在判别力、信息含量和经济影响方面，是公司破产之前的一种情况。总的来说，我们使用 1990 年至 2015 年期间的美国上市公司发现，与使用传统方法（例如最大化对数似然函数）相比，最大化 AUROC 的训练模型提供了更准确的样本外预测，强调使用具有最大化 AUROC 的模型所带来的好处。然而，在所有模型中，使用我们的方法训练的神经网络是性能最好的，即使我们将其与文献中提出的其他方法进行比较以最大化 AUROC。最后，当我们增加预测难度时，我们的结果会更加明显，例如预测财务困境。我们训练破产模型的方法的实施在各种环境中都是稳健的，因此是合理的。