Optimal Execution

Optimal execution focuses on minimizing the cost of trading large orders by strategically splitting them into smaller ones. Current research heavily utilizes reinforcement learning, often employing neural networks (like recurrent or actor-critic architectures) within agent-based market models to learn optimal trading strategies, considering factors like price impact and order book dynamics. This field is significant for improving algorithmic trading efficiency and informing the design of more robust and realistic market simulations, ultimately contributing to a deeper understanding of market microstructure and financial risk management.