JOURNAL ARTICLE

The Opposing Effects of Complexity and Information Content on Uncertainty Dynamics: Evidence from 10-K Filings.

  • Published In: Management Science (INFORMS), 2023, v. 69, n. 10. P. 6313 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Bae, Joon Woo; Belo, Frederico; Li, Jun; Lin, Xiaoji; Zhao, Xiaofei 3 of 3

Abstract

This article examines how the complexity and information content of annual reports, specifically U.S. Securities and Exchange Commission (SEC) Form 10-K filings, affect the dynamics of investor uncertainty as measured by option-implied volatility. The study finds a hump-shaped pattern in volatility following 10-K disclosures: an initial increase in uncertainty during the first 2–4 weeks, driven by the complexity of the filings, followed by a larger decrease over the subsequent 6 weeks as investors digest the information content. Larger 10-K file sizes amplify this pattern, reflecting both greater complexity and richer information. Using textual analysis, the authors distinguish the "risk factors" section as primarily capturing complexity (increasing short-run uncertainty) and the "management discussion and analysis" (MD&A) section as reflecting information content (reducing long-run uncertainty). The findings are robust across alternative uncertainty measures and control for confounding factors, and they have implications for investor learning, asset pricing, and disclosure regulation.

Additional Information

  • Source:Management Science (INFORMS). 2023/10, Vol. 69, Issue 10, p6313
  • Document Type:Article
  • Subject Area:Library and Information Science
  • Publication Date:2023
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.4615
  • Accession Number:173037884
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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