A simulation-grounded neural detection framework identifies transient mechanical liquidity erosion in limit order books with 36% AUC gain over rule-based baselines.
Applied Mathematical Finance , volume=
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In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.
citing papers explorer
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When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books
A simulation-grounded neural detection framework identifies transient mechanical liquidity erosion in limit order books with 36% AUC gain over rule-based baselines.
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Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution
In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.