Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year- and two-year-ahead predictions and interpret groups at greater risk of misstatements.

机器学习提供了经验方法来筛选具有大量变量并且对功能形式具有先验知识的会计数据集。在这项研究中，我们表明，这些方法有助于检测和解释当前会计错误陈述中存在的模式。我们使用会计，资本市场，治理和审计数据集中的各种变量来检测重大错报。我们的分析的主要见解是，尽管会计变量无法很好地自行检测出虚假陈述，但通过与审计和市场变量的适当互动就变得很重要。我们还分析了错报和违规行为之间的差异，比较了算法，检查了提前一年和两年的预测，并解释了错报风险更大的群体。