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Estimating price impact via deep reinforcement learning

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  • Yi Cao
  • Jia Zhai

Abstract

Price impact is the adverse change of the asset price against trader's action. As a crucial part of the indirect trading cost, price impact has attracted increasing attention in both econometric and data science literature. In this paper, we draw upon both strands of the literature and develop a deep neural network enhanced recursive (DeRecv) model to estimate temporary and permanent price impact of an order or trade. The temporary price impact is calculated as the sum of the expected immediate impact at each time point after taking action in an ad hoc market condition. The permanent price impact is defined as a new permanent level at which the information of the incoming order is entirely absorbed by the market. Through the experimental evaluation based on data from 10 stocks at NASDAQ and Shanghai Stock Exchange, we show that the proposed DeRecv model is better than the reinforcement learning model and the traditional vector autoregressive model.

Suggested Citation

  • Yi Cao & Jia Zhai, 2022. "Estimating price impact via deep reinforcement learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3954-3970, October.
    • Handle: RePEc:wly:ijfiec:v:27:y:2022:i:4:p:3954-3970
    DOI: 10.1002/ijfe.2353
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    References listed on IDEAS

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    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
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    3. Ying Jiang & Yi Cao & Xiaoquan Liu & Jia Zhai, 2019. "Volatility modeling and prediction: the role of price impact," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2015-2031, December.
    4. Jones, Charles M & Kaul, Gautam & Lipson, Marc L, 1994. "Transactions, Volume, and Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 7(4), pages 631-651.
    5. Hautsch, Nikolaus & Huang, Ruihong, 2012. "The market impact of a limit order," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 501-522.
    6. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    7. Hasbrouck, Joel, 1988. "Trades, quotes, inventories, and information," Journal of Financial Economics, Elsevier, vol. 22(2), pages 229-252, December.
    8. Philip, R., 2020. "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, vol. 215(2), pages 414-449.
    9. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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    Cited by:

    1. Adamantios Ntakaris & Gbenga Ibikunle, 2024. "Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading," Papers 2412.19372, arXiv.org, revised Dec 2024.

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