Adaptive Execution: Exploration and Learning of Price Impact

• 成果类型: Article
• 署名作者: Park, Beomsoo; Van Roy, Benjamin
• 署名单位: Stanford University; Stanford University; Stanford University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1415
• 发表日期: 2015
• 页码: 1058-1076
• 关键词: least-squares; identification

摘要

We consider a model in which a trader aims to maximize expected risk-adjusted profit while trading a single security. In our model, each price change is a linear combination of observed factors, impact resulting from the trader's current and prior activity, and unpredictable random effects. The trader must learn coefficients of a price impact model while trading. We propose a new method for simultaneous execution and learning-the confidence-triggered regularized adaptive certainty equivalent (CTRACE) policy-and establish a poly-logarithmic finite-time expected regret bound. In addition, we demonstrate via Monte Carlo simulation that CTRACE outperforms the certainty equivalent policy and a recently proposed reinforcement learning algorithm that is designed to explore efficiently in linear-quadratic control problems.