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AI agents in trading systems exhibit phase transitions and altered market impact

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the behavior of Limit Order Books (LOBs) when populated solely by autonomous reinforcement learning agents. The study identifies distinct phase transitions in order flow, separating stable price discovery from volatile cascade states based on agent numbers and market depth. It also reveals that market impact dynamics under agentic liquidity provision differ from traditional models, showing unique regimes influenced by non-linear feedback loops. AI

IMPACT This research could inform the development of more stable and predictable algorithmic trading systems by understanding agent-driven market dynamics.

RANK_REASON The cluster contains a research paper detailing novel findings in quantitative finance using AI agents. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents in trading systems exhibit phase transitions and altered market impact

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The cluster contains a research paper detailing novel findings in quantitative finance using AI agents. [lever_c_demoted from research: ic=1 ai=0.7]
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46 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jan Rosenzweig ·

    Agentic Limit Order Books: Phase Transitions and Market Impact

    arXiv:2609.31260v1 Announce Type: cross Abstract: We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning agentic traders. By formalizing agent interactions within a microscopic order-match…

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