Alessio, AzzuttiAlmutairi, Nasser2025-12-092025https://hdl.handle.net/20.500.14154/77407Algorithmic trading has shifted from rule-based speed to adaptive autonomy, with deep learning and reinforcement learning agents that learn, re-parameterize, and redeploy in near real time, amplifying opacity, correlated behaviours, and flash-crash dynamics. Against this backdrop, the dissertation asks whether existing EU and US legal frameworks can keep pace with new generations of AI trading systems. It adopts a doctrinal and comparative method, reading MiFID II and MAR, the EU AI Act, SEC and CFTC regimes, and global soft law (IOSCO, NIST) through an engineering lens of AI lifecycles and value chains to test functional adequacy. Chapter 1 maps the evolution from deterministic code to self-optimizing agents and locates the shrinking space for real-time human oversight. Chapter 2 reframes technical attributes as risk vectors, such as herding, feedback loops, and brittle liquidity, and illustrates enforcement and stability implications. Chapter 3 exposes human-centric assumptions (intent, explainability, “kill switches”) embedded in current rules and the gaps they create for attribution, auditing, and cross-border supervision. Chapter 4 proposes a hybrid, lifecycle-based model of oversight that combines value-chain accountability, tiered AI-agent licensing, mandatory pre-deployment verification, explainability XAI requirements, cryptographically sealed audit trails, human-in-the-loop controls, continuous monitoring, and sandboxed co-regulation. The contribution is threefold: (1) a technology-aware risk typology linking engineering realities to market integrity outcomes; (2) a comparative map of EU and US regimes that surfaces avenues for regulatory arbitrage; and (3) a practicable governance toolkit that restores traceable accountability without stifling beneficial innovation. Overall, the thesis argues for moving from incremental, disclosure-centric tweaks to proactive, lifecycle governance that embeds accountability at design, deployment, and post-trade, aligning next-generation trading technology with the enduring goals of fair, orderly, and resilient markets.51enArtificial IntelligenceDeep LearningMachine LearningReinforcement LearningHigh-Frequency TradingExplainable Artificial IntelligenceMarkets in Financial Instruments Directive IIThesis