Risk and Legal Regulation of Algorithm Application in Insider Trading Supervision

Laiyao Chen ()
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Laiyao Chen: PhD student at the School of Law, University of International Business and Economics, majoring in Financial Law, China.

Technium Social Sciences Journal, 2023, vol. 43, issue 1, 274-287

Abstract: Insider trading is a kind of information manipulation behavior in the securities market. The insider trading has brought great damage to the securities market in recent years, so securities regulatory authorities have begun to crack down on this kind of illegal behavior. With the application of algorithms in the field of supervision, regulators can accurately identify insider trading behaviors through big data analysis and other technologies, with which the efficiency of supervision was greatly improved. However, the application of algorithms in the supervision of insider trading is prone to cause various legal risks, such as the inaccurate transformation between algorithms and regulatory regulations, the imperfection of algorithms, the infringement of private data rights, etc. The legitimate rights of the regulated objects might be endangered by above risks. Therefore, it’s necessary to establish risk prevention and legal regulation to cope with the algorithms in the filed of insider trading supervision from three aspects of rule transformation, technical supervision and data security. In this way, the property rights, data rights, privacy rights of financial investors, listed companies and other stakeholders are protected.

Keywords: Insider trading; Algorithmic risk; Legal regulation; Data Security (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:tec:journl:v:43:y:2023:i:1:p:274-287

DOI: 10.47577/tssj.v43i1.8767

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