{'fraction_non_alphanumeric': 0.018205461638491547, 'fraction_numerical': 0.0, 'mean_word_length': 5.936936936936937, '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}}
Understanding Analysts' Earnings Expectations: Biases, Nonlinearities and Predictability

This paper studies the asymmetric behavior of negative and positive values of analysts' earnings revisions and links it to the conservatism principle of accounting. Using a new three-state mixture of log-normals model that accounts for differences in the magnitude and persistence of positive, negative and zero revisions, we find evidence that revisions to analysts' earnings expectations can be predicted using publicly available information such as lagged interest rates and past revisions. We also find that our forecasts of revisions to analysts' earnings estimates help predict the actual earnings figure beyond the information contained in analysts' earnings expectations.