We use a comprehensive set of performance metrics to analyze the improvement in the classification power and prediction accuracy of various bankruptcy prediction models after adding governance variables and/or varying the estimation method used. In a sample covering bankruptcies of U.S. public firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the performance of all bankruptcy prediction models. We also find that the additional explanatory power provided by governance measures improves the further the firm is from bankruptcy, which suggests that governance variables may provide earlier and more accurate warning of the firm's bankruptcy potential. Our findings show that the performance of any bankruptcy prediction model is significantly affected by the estimation method used. We find that regardless of the bankruptcy model, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Furthermore, non-parametric methods such as neural networks, data envelopment analysis or classification and regression trees appear to provide comparable and sometimes superior classification accuracy to hazard analysis. Lastly, we use the dynamic panel generalized methods of moments model to address concerns raised in prior studies about the susceptibility of similar studies to endogeneity issues and find that our findings continue to hold.

在添加治理变量和/或更改所使用的估计方法之后，我们使用一套全面的绩效指标来分析各种破产预测模型的分类能力和预测准确性的提高。在一个涵盖2000年至2015年美国上市公司破产案例的样本中，我们发现，添加治理变量可以显着提高所有破产预测模型的绩效。我们还发现，治理措施所提供的额外解释力进一步提高了公司远离破产的可能性，这表明治理变量可以为公司的破产潜力提供更早，更准确的警告。我们的发现表明，任何破产预测模型的性能都会受到所用估计方法的显着影响。我们发现，无论采用哪种破产模型，危害分析都能在参数方法中提供最佳的分类和样本外预测准确性。此外，非参数方法，例如神经网络，数据包络分析或分类和回归树，似乎可以提供与危害分析相当甚至有时更高的分类精度。最后，我们使用动态面板广义矩量方法模型来解决先前研究中关于类似研究对内生性问题的敏感性的关注，并发现我们的发现继续存在。数据包络分析或分类和回归树似乎可以提供与危害分析相当甚至有时更高的分类精度。最后，我们使用动态面板广义矩量法模型来解决先前研究中关于类似研究对内生性问题的敏感性的关注，并发现我们的发现继续存在。数据包络分析或分类和回归树似乎可以提供与危害分析相当甚至有时更高的分类精度。最后，我们使用动态面板广义矩量方法模型来解决先前研究中关于类似研究对内生性问题的敏感性的关注，并发现我们的发现继续存在。