Paper: arXiv 2604.21672
Authors: Irene Aldridge, Jolie An, Riley Burke, Michael Cao, Chia-Yi Chien, Kexin Deng, Ruipeng Deng, Yichen Gao, Olivia Guo, Shunran He, Zheng Li, George Lin, Weihang Lin, Percy Lyu, Alex Ng, Qi Wang, Hanxi Xiao, Dora Xu, Yuanyuan Xue, Sheng Zhang, Sirui Zhang, Yun Zhang, Sirui Zhao, Xiaolong Zhao, Yihan Zhao, Waner Zheng
Abstract
The emergence of agentic artificial intelligence (AI) represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention. This comprehensive survey synthesizes recent advances in agentic AI across multiple dimensions of financial operations, including system architecture, market applications, regulatory frameworks, and systemic implications. We examine how agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination. Our analysis shows that while agentic AI offers substantial potential for enhanced market efficiency, liquidity provision, and risk management, it also introduces novel challenges related to market stability, regulatory compliance, interpretability, and systemic risk. Through a systematic review of foundational research, technical architectures, market applications, and governance frameworks, this survey provides scholars and practitioners with a structured understanding of how agentic AI is reshaping financial markets and identifies critical research directions for ensuring that these systems enhance both operational efficiency and market resilience.
Complexity vs Empirical Score
- Math Complexity: 2.5/10
- Empirical Rigor: 1.5/10
- Quadrant: Philosophers — conceptual discussion, limited math and data
Why this score: This paper is a broad literature survey, not a quantitative research or empirical discovery paper, and therefore lacks mathematical derivations or direct backtesting results. It focuses on synthesizing conceptual frameworks and defining taxonomies for agentic artificial intelligence in financial contexts.
Research Flowchart
flowchart TD
A[<b>Goal:</b> Analyze Agentic AI's Impact on Financial Markets] --> B[<b>Methodology:</b> Systematic Review of Research & Frameworks]
B --> C[<b>Inputs:</b> Foundational Research, Tech Architectures, Market Data]
C --> D[<b>Process:</b> Comparative Analysis vs. Trad. Algo/GenAI]
D --> E[<b>Themes:</b> Market Efficiency, Risk, & Governance]
E --> F[<b>Outcomes:</b> Identified Risks & Future Research Directions]