The proliferation of algorithmic trading, artificial intelligence (AI)-enabled credit assessment and automated investment consultancy has facilitated the digital transformation of securities markets. Such technological progress has spawned a new regulatory concern: algorithmic collusion. Unlike traditional cartels formed via human negotiation and explicit coordination, algorithmic collusion relies on automated price signaling, shared data channels and reinforcement learning technologies, allowing market participants to realize tacit behavioral coordination without human interaction. Focusing on digital securities trading scenarios, this paper divides algorithmic collusion into four primary categories, namely indirect messenger collusion, hub-and-spoke collusion, predictive agent collusion and autonomous learning collusion, and analyzes the unique regulatory dilemmas corresponding to each category. Based on over ten domestic and foreign legal research achievements, alongside the regulatory practical experience of the European Union and the United States, this study verifies notable defects in the existing antitrust system, which fails to clarify relevant legal liabilities, identify coordinated behaviors or implement targeted regulatory enforcement. This research develops a full-spectrum governance solution that incorporates pre-supervision transparency enhancement, substantive liability standard refinement, regulatory institutional capacity building and market trust restoration. The findings indicate that regulators should update traditional supervisory logic, phase out scattered, penalty-oriented supervision modes, and build a flexible regulatory system that adapts to technological updates. The optimized governance framework can effectively balance market technological innovation, protect investor legitimate rights and interests, and sustain long-term financial stability.
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Share and Cite
Wang, S. (2026) Algorithmic Collusion in Financial Markets: Regulatory Dilemmas and Legal Responses in Digital‑age Securities Markets. Hong Kong Financial Bulletin, 2(2), 25-36. https://doi.org/10.71052/hkfb2025/RVBB5483