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q-fin.TR2024
Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning
Fabrizio Lillo, Andrea Macrì
The use of reinforcement learning algorithms in financial trading is becoming increasingly prevalent. However, the autonomous nature of these algorithms can lead to unexpected outc…
q-fin.TR2024★ 5 cited
Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying
Andrea Macrì, Fabrizio Lillo
Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreove…