Work

An Adversarial Autonomous Risk Governance System for High-friction Emerging Equity Markets Under Multi-day Settlement and Price Corridors

Year: 2026

Type: preprint

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21766530

Author: Nguyen, Thi Nhu Y

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7172282967

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