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Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning
[Cash holdings and credit risk]

Author

Listed:
  • Mehran Azimi
  • Anup Agrawal

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)

Suggested Citation

  • Mehran Azimi & Anup Agrawal, 2021. "Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning [Cash holdings and credit risk]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 11(4), pages 762-805.
    • Handle: RePEc:oup:rasset:v:11:y:2021:i:4:p:762-805.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raab005
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    Citations

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    Cited by:

    1. Cui, Xiling & Zhu, Zhongshan & Liu, Libo & Zhou, Qiang & Liu, Qiang, 2024. "Anomaly detection in consumer review analytics for idea generation in product innovation: Comparing machine learning and deep learning techniques," Technovation, Elsevier, vol. 134(C).
    2. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Joel R. Bock, 2024. "Generating long-horizon stock "buy" signals with a neural language model," Papers 2410.18988, arXiv.org.
    4. Mushtaq, Rizwan & Gull, Ammar Ali & Shahab, Yasir & Derouiche, Imen, 2022. "Do financial performance indicators predict 10-K text sentiments? An application of artificial intelligence," Research in International Business and Finance, Elsevier, vol. 61(C).

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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