We use machine learning methods to create a comprehensive measure of credit risk based on qualitative information disclosed in conference calls and in management’s discussion and analysis section of the 10-K. In out-of-sample tests, we find that our measure improves the ability to predict credit events (bankruptcies, interest spreads, and credit rating downgrades), relative to credit risk measures developed by prior research (e.g., z-score). We also find our measure based on conference calls explains within-firm variation in future credit events; however, we find little evidence that the measures of credit risk developed by prior research explain within-firm variation in credit risk. Our measure has utility for both academics and practitioners, as the majority of firms do not have readily available measures of credit risk, such as actively-traded CDS or credit ratings. Our study also adds to the growing body of research using machine-learning methods to gather information from conference calls and MD&A to explain key outcomes.

我们使用机器学习方法，根据电话会议以及管理层在10-K讨论和分析部分中披露的定性信息，创建一种全面的信用风险度量。在样本外测试中，我们发现，相对于先前研究（例如z评分）开发的信用风险度量，我们的度量提高了预测信用事件（破产，利息利差和信用评级降级）的能力。我们还发现，基于电话会议的措施可以解释未来信用事件中公司内部的变化；但是，我们发现几乎没有证据表明先前研究开发的信用风险度量可以解释企业内部信用风险的变化。我们的措施对学者和从业者都有用，因为大多数公司没有现成的信用风险度量，例如活跃交易的CDS或信用评级。我们的研究还增加了使用机器学习方法从电话会议和MD＆A收集信息以解释关键结果的研究范围，这一研究正在不断发展。