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

• 成果类型: Article
• 署名作者: Bae, Joon Woo; Belo, Frederico; Li, Jun; Lin, Xiaoji; Zhao, Xiaofei
• 署名单位: University System of Ohio; Case Western Reserve University; INSEAD Business School; Centre for Economic Policy Research - UK; University of Texas System; University of Texas Dallas; University of Minnesota System; University of Minnesota Twin Cities; Georgetown University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4615
• 发表日期: 2023
• 页码: 6313-6332
• 关键词: Learning; complexity; INFORMATION; Volatility dynamics; Textual analysis

摘要

We evaluate the impact of complexity and information content of 10-K filings on uncertainty dynamics following the filings. We have three main findings. First, the option-implied volatility on average increases in the first four weeks after the filings, followed by a net decrease in the subsequent six weeks. Second, this hump-shaped volatility dynamic is more pronounced for firms with larger 10-K file sizes. Third, we provide a novel decomposition of 10-K file size based on the individual sections' disclosure amount and topic analysis and find that the discussions on topics in the risk factors section mainly capture the complexity aspect, whereas the discussions on topics in the managerial discussion and analysis section mainly capture the information content aspect of the 10-K filings. Our findings highlight the importance of timing for understanding the opposing effects of complexity and information content on asset prices.