Adaptive Execution: Exploration and Learning of Price Impact

Beomsoo Park () and Benjamin Van Roy ()
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Beomsoo Park: Stanford University, Stanford, California 94305
Benjamin Van Roy: Stanford University, Stanford, California 94305

Operations Research, 2015, vol. 63, issue 5, 1058-1076

Abstract: 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.

Keywords: adaptive execution; price impact; reinforcement learning; regret bound (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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