This randomized trial introduces an autonomous risk governance system to improve market stability in emerging equity markets with multi-day settlement delays and price corridors.
Conventional deep reinforcement learning (DRL) algorithms and high‑frequency trading models in quantitative finance are built on idealized assumptions of market clearing — namely continuous settlement (T+0) and unlimited counterparty liquidity. Yet, when these systems are deployed in emerging equity markets, where settlement delays of multiple days (T+2.5) and strict daily floor/ceiling price bands are unavoidable, they tend to collapse in a systematic way. This paper introduces Agent‑2.5 JueShen a deterministic Autonomous Risk Governance Operating System (RGOS) designed to counter both human behavioral biases and the execution traps inherent in algorithmic trading.
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Thi Nhu Y Nguyen (2026) studied this question.
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