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Trading algorithms with learning in latent alpha models

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  • Philippe Casgrain
  • Sebastian Jaimungal

Abstract

Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyzes how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem. We provide a verification theorem, and a methodology for calibrating the model by deriving a variation of the expectation–maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies that ignore learning in the latent factors. We also provide calibration results for a particular model using Intel Corporation stock as an example.

Suggested Citation

  • Philippe Casgrain & Sebastian Jaimungal, 2019. "Trading algorithms with learning in latent alpha models," Mathematical Finance, Wiley Blackwell, vol. 29(3), pages 735-772, July.
    • Handle: RePEc:bla:mathfi:v:29:y:2019:i:3:p:735-772
    DOI: 10.1111/mafi.12194
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    Cited by:

    1. Campi, Luciano & Zabaljauregui, Diego, 2020. "Optimal market making under partial information with general intensities," LSE Research Online Documents on Economics 104612, London School of Economics and Political Science, LSE Library.
    2. David Evangelista & Yuri Thamsten, 2020. "On finite population games of optimal trading," Papers 2004.00790, arXiv.org, revised Feb 2021.
    3. Fayc{c}al Drissi, 2022. "Solvability of Differential Riccati Equations and Applications to Algorithmic Trading with Signals," Papers 2202.07478, arXiv.org, revised Aug 2023.
    4. Yuchen Fang & Kan Ren & Weiqing Liu & Dong Zhou & Weinan Zhang & Jiang Bian & Yong Yu & Tie-Yan Liu, 2021. "Universal Trading for Order Execution with Oracle Policy Distillation," Papers 2103.10860, arXiv.org.
    5. Sebastian Jaimungal & Xiaofei Shi, 2024. "The Price of Information," Papers 2402.11864, arXiv.org, revised Mar 2024.
    6. Peter Bank & 'Alvaro Cartea & Laura Korber, 2023. "Optimal execution and speculation with trade signals," Papers 2306.00621, arXiv.org, revised Jul 2023.
    7. Yongli Li & Tianchen Wang & Baiqing Sun & Chao Liu, 2022. "Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.
    8. Colaneri, Katia & Eksi, Zehra & Frey, Rüdiger & Szölgyenyi, Michaela, 2020. "Optimal liquidation under partial information with price impact," Stochastic Processes and their Applications, Elsevier, vol. 130(4), pages 1913-1946.
    9. Sebastian Jaimungal, 2022. "Reinforcement learning and stochastic optimisation," Finance and Stochastics, Springer, vol. 26(1), pages 103-129, January.

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