JOURNAL ARTICLE
Fragmented Securities Regulation, Information-Processing Costs, and Insider Trading.
Published In: Management Science (INFORMS), 2024, v. 70, n. 7. P. 4407 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Kim, Sehwa; Kim, Seil 3 of 3
Abstract
This article investigates the effects of fragmented securities regulation on information-processing costs and insider trading by comparing stand-alone banks, which file insider-trading disclosures with federal bank regulators via FDICconnect, to bank holding companies, which file with the U.S. Securities and Exchange Commission (SEC) through the EDGAR system. The study finds that market reactions to insider-trading filings on FDICconnect are significantly less timely and smaller in the short run than reactions to filings on SEC EDGAR, indicating higher information-processing costs associated with FDICconnect. Large institutional investors, but not retail investors, appear to benefit from this delayed market reaction by trading more on FDICconnect filings, suggesting a distortion of the level playing field. Additionally, insiders at stand-alone banks engage in more opportunistic selling prior to negative earnings announcements, implying that fragmented regulation may facilitate the exploitation of private information. Overall, the findings highlight that regulatory fragmentation undermines market efficiency and fairness by increasing information-processing costs and enabling opportunistic insider behavior.
Additional Information
- Source:Management Science (INFORMS). 2024/07, Vol. 70, Issue 7, p4407
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4903
- Accession Number:178319267
- 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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