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Adaptive Execution: Exploration and Learning of Price Impact

Author

Listed:
  • Beomsoo Park

    (Stanford University, Stanford, California 94305)

  • Benjamin Van Roy

    (Stanford University, Stanford, California 94305)

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.

Suggested Citation

  • Beomsoo Park & Benjamin Van Roy, 2015. "Adaptive Execution: Exploration and Learning of Price Impact," Operations Research, INFORMS, vol. 63(5), pages 1058-1076, October.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:5:p:1058-1076
    DOI: 10.1287/opre.2015.1415
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    References listed on IDEAS

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    Cited by:

    1. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    2. Yan, Tingjin & Chiu, Mei Choi & Wong, Hoi Ying, 2023. "Portfolio liquidation with delayed information," Economic Modelling, Elsevier, vol. 126(C).
    3. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.

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