Distinguishing between recurring and nonrecurring components of earnings is a critical task in financial analysis and valuation. Academics and quantitative investors often rely on measures of recurring and nonrecurring components derived from standardized financial databases. We use unobserved components modeling and the Kalman smoother to obtain efficient ex-post estimates of the recurring and nonrecurring components of annual earnings. We then show that popular measures are significantly misspecified and that investors appear to anticipate a significant portion of the misspecification. Finally, we identify certain misclassified items that drive misspecification and provide algorithms to improve their ex-ante classification.

中文翻译：

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使用未观察到的成分建模区分收益的经常性和非经常性成分

区分收益的经常性和非经常性组成部分是财务分析和估值中的一项关键任务。学术界和量化投资者经常依赖于标准化金融数据库得出的经常性和非经常性成分的衡量标准。我们使用未观察到的成分建模和卡尔曼平滑器来获得年收益的经常性和非经常性成分的有效事后估计。然后我们表明，流行的衡量标准存在严重错误指定，并且投资者似乎预期了很大一部分错误指定。最后，我们识别出某些导致错误指定的错误分类项目，并提供算法来改进其事前分类。