TY - JOUR
AU1 - Azimi, Mehran
AU2 - Agrawal, Anup
AB - 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) This content is only available as a PDF. © The Author 2020. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning
JF - The Review of Asset Pricing Studies
DO - 10.1093/rapstu/raab005
DA - 2021-03-02
UR - https://www.deepdyve.com/lp/oxford-university-press/is-positive-sentiment-in-corporate-annual-reports-informative-evidence-5Qe0ioAstg
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -