{'fraction_non_alphanumeric': 0.013112491373360938, 'fraction_numerical': 0.0, 'mean_word_length': 5.712962962962963, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Detection of Earnings Manipulation

and Université du Québec à Montréal. I am indebted to David Hsieh for his generous econometric advice and the use of his estimation subroutines. I thank Julie Azoulay, Pablo Cisilino and Melissa McFadden for expert assistance. Abstract The Detection of Earnings Manipulation The paper profiles a sample of earnings manipulators, identifies their distinguishing characteristics, and estimates a model for detecting manipulation. The model's variables are designed to capture either the effects of manipulation or preconditions that may prompt firms to engage in such activity. The results suggest a systematic relation between the probability of manipulation and financial statement variables. The evidence is consistent with accounting data being useful in detecting manipulation and assessing the reliability of accounting earnings. In holdout sample tests, the model identifies approximately half of the companies involved in earnings manipulation prior to public discovery. Because companies discovered manipulating earnings see their stocks plummet in value, the model can be a useful screening device for investing professionals. While the model is easily implemented-the data can be extracted from an annual report-, the screening results require further investigation to determine whether the distortions in financial statement numbers result from earnings manipulation or have another structural root.