• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Fiona Chu
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HOME > Archive > 2026 > Volume 14 Number 3 (2026) >
JOEBM 2026 Vol.14(3): 240-244
DOI: 10.18178/joebm.2026.14.3.948

Research on Emergent Coordination and the Market Fragility in AI Trading Systems

Xingru Du
Xingru Du
Wuhan Britain China School, Wuhan, China
Email: duxingru2027@163.com

Manuscript received June 2, 2026; accepted August 20, 2026; published September 9, 2026.

Abstract—Autonomous artificial intelligence trading systems have captured an expanding share of global financial market transactions in recent years. Leading quantitative institutions widely deploy self-learning algorithms that process multi-dimensional market information and complete automated order execution with limited manual oversight. Extant literature on AI trading predominantly centers on algorithmic profit performance and transaction efficiency, yet few studies systematically analyze how interactive behaviors between independent trading agents erode overall market stability. This paper explores emergent coordination caused by AI trading systems and examines its potential impact on market fragility, investigating whether autonomous AI agents can unintentionally develop synchronized behaviors through shared data sources, strategy convergence, and feedback loops. The paper finds that such collective behavior may amplify market volatility, reduce liquidity, and increase systemic financial risk, even in the absence of explicit communication among market participants. Therefore, future regulatory frameworks should focus more on system-level AI interactions, as market instability may arise more from collective AI behavior than from isolated algorithmic failures.
 
Keywords—AI trading systems, emergent coordination, market fragility, systemic risk, complex adaptive systems

Cite: Xingru Du, "Research on Emergent Coordination and the Market Fragility in AI Trading Systems," Journal of Economics, Business and Management, vol. 14, no. 3, pp. 240-244, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

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