A hybrid demand-value query procedure for portfolio preference elicitation in crossing markets recovers 88% of full-information welfare in calibrated experiments and identifies disclosure-sensitive package representation as a key design choice.
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A simulation-grounded neural detection framework identifies transient mechanical liquidity erosion in limit order books with 36% AUC gain over rule-based baselines.
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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Portfolio Preference Elicitation in Institutional Crossing Markets
A hybrid demand-value query procedure for portfolio preference elicitation in crossing markets recovers 88% of full-information welfare in calibrated experiments and identifies disclosure-sensitive package representation as a key design choice.
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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.