A reinforcement learning approach to optimal execution
将微小订单的最优执行问题建模为最优停止问题，提出基于监督学习和强化学习的两种数据驱动方法，通过历史市场数据实验证明能显著降低成本，并分析了时序差分学习中的收敛速度、数据效率及偏差-方差权衡。
We consider the problem of execution timing in optimal execution. Specifically, we formulate the optimal execution problem of an infinitesimal order as an optimal stopping problem. By using a novel neural network architecture, we develop two versions of data-driven approaches for this problem, one based on supervised learning, and the other based on reinforcement learning. Temporal difference learning can be applied and extends these two methods to many variants. Through numerical experiments on historical market data, we demonstrate significant cost reduction of these methods. Insights from numerical experiments reveals various tradeoffs in the use of temporal difference learning, including convergence rates, data efficiency, and a tradeoff between bias and variance.