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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 127 - 141 (15page)
- DOI
- 10.24886/BLR.2024.12.38.4.127
이용수
초록· 키워드
오류제보하기
This article examines the current challenges and prospects of enforcing Financial disclosure systems under the Securities Regulation, including the liability system in the Japanese Financial Instruments and Exchange Act, by focusing on identifying and measuring “material misstatements in the cases of misstatements.
Traditionally, whether a false or fraudulent disclosure is “material” has been evaluated subjectively, relying on general investor perspectives and qualitative judgments in Japan. This approach, however, often proves insufficient and inappropriate, as it neither guarantees objectivity nor establishes the necessary standards for enforcing liability systems.
Drawing on prominent cases in Japan, including the Seibu Railway, Toshiba, and Nissan cases, I would like to highlight that revelations of misstatements often correlate with significant drops in share prices. While these price changes suggest that material misstatements can be detected by their impact on investor decisions, isolating and measuring their influence remains complex. Corporate governance issues, long-term reputational damage, and broader market conditions can all affect share prices, complicating the objective identification of material misstatements.
The article proposes developing an automated, AI-based data collection system to address these challenges. This approach entails using scripts, natural language processing, and financial data APIs to identify potential “signals” of material misstatements from public filings, such as the U.S. Form 8-K disclosures, and then linking these signals to significant share price fluctuations. Initial testing of such scripts has already produced promising candidates, though much refinement is needed. Improved filtering criteria, broader data sources, and more sophisticated analytical techniques are necessary for achieving statistically meaningful results.
Finally, this article suggests that leveraging AI-driven, data-centric methodologies can lead to a more objective, reliable, and transparent enforcement framework for disclosure regulations. If I could develop these advancements, they could contribute significantly to the administrative enforcement regime and ultimately serve as a valuable tool for regulators, investors, and researchers in ensuring fair and trustworthy financial markets.
Traditionally, whether a false or fraudulent disclosure is “material” has been evaluated subjectively, relying on general investor perspectives and qualitative judgments in Japan. This approach, however, often proves insufficient and inappropriate, as it neither guarantees objectivity nor establishes the necessary standards for enforcing liability systems.
Drawing on prominent cases in Japan, including the Seibu Railway, Toshiba, and Nissan cases, I would like to highlight that revelations of misstatements often correlate with significant drops in share prices. While these price changes suggest that material misstatements can be detected by their impact on investor decisions, isolating and measuring their influence remains complex. Corporate governance issues, long-term reputational damage, and broader market conditions can all affect share prices, complicating the objective identification of material misstatements.
The article proposes developing an automated, AI-based data collection system to address these challenges. This approach entails using scripts, natural language processing, and financial data APIs to identify potential “signals” of material misstatements from public filings, such as the U.S. Form 8-K disclosures, and then linking these signals to significant share price fluctuations. Initial testing of such scripts has already produced promising candidates, though much refinement is needed. Improved filtering criteria, broader data sources, and more sophisticated analytical techniques are necessary for achieving statistically meaningful results.
Finally, this article suggests that leveraging AI-driven, data-centric methodologies can lead to a more objective, reliable, and transparent enforcement framework for disclosure regulations. If I could develop these advancements, they could contribute significantly to the administrative enforcement regime and ultimately serve as a valuable tool for regulators, investors, and researchers in ensuring fair and trustworthy financial markets.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Review of Cases
Ⅲ. Possibility of Using AI-based data collection programs and the collected data for identifying material misstatement
Ⅳ. Conclusion
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