We use a novel text classification approach from deep learning to more accurately measure sentiment in a large sample of 10-Ks. In contrast to most prior literature, we find that positive and negative sentiments predict abnormal returns and abnormal trading volume around the 10-K filing date and future firm fundamentals and policies. Our results suggest that the qualitative information contained in corporate annual reports is richer than previously found. Both positive and negative sentiments are informative when measured accurately, but they do not have symmetric implications, suggesting that a net sentiment measure advocated by prior studies would be less informative. (JEL C81, D83, G10, G14, G30, M41) Received February 12, 2020; editorial decision January 5, 2021 by Editor Hui Chen
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Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning
Semantic Scholar · Economics · 2020
Abstract
We use a novel text classification approach from deep learning to more accurately measure sentiment in a large sample of 10-Ks. In contrast to most prior literature, we find that positive and negative sentiments predict abnormal returns and abnormal trading volume around the 10-K filing date and future firm fundamentals and policies. Our results suggest that the qualitative information contained in corporate annual reports is richer than previously found. Both positive and negative sentiments are informative when measured accurately, but they do not have symmetric implications, suggesting that a net sentiment measure advocated by prior studies would be less informative. (JEL C81, D83, G10, G14, G30, M41) Received February 12, 2020; editorial decision January 5, 2021 by Editor Hui Chen