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Taxable Stock Trading with Deep Reinforcement Learning

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  • Shan Huang

Abstract

In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance could induce more than 62% loss on the average portfolio returns, implying that taxes should be embedded in the environment of continuous stock trading on AI platforms.

Suggested Citation

  • Shan Huang, 2019. "Taxable Stock Trading with Deep Reinforcement Learning," Papers 1907.12093, arXiv.org, revised Jul 2019.
    • Handle: RePEc:arx:papers:1907.12093
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    References listed on IDEAS

    as
    1. Min Dai & Hong Liu & Chen Yang & Yifei Zhong, 2015. "Optimal Tax Timing with Asymmetric Long-Term/Short-Term Capital Gains Tax," The Review of Financial Studies, Society for Financial Studies, vol. 28(9), pages 2687-2721.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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