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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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0111 · Jun 202219922001200920172026
3 results for supra-competitive

Agents learn to outperform in trading by using past and current prices.

problem Optimal trading performance beyond theoretical limits.
method Two-agent Almgren-Chriss liquidation game, schedule-learning, DDQN architectures.
result Agents with access to past and current prices achieve supra-competitive outcomes.

Agents trained with reinforcement learning deviate from Nash equilibrium in optimal execution game.

problem Deviation of reinforcement learning strategies from Nash equilibrium in optimal execution game.
method Two-player optimal execution game with reinforcement learning algorithms (Double Deep Q-Learning).
result Strategies learned by agents deviate significantly from Nash equilibrium, exhibiting supra-competitive solutions.