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Estimating permanent price impact via machine learning

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  • Philip, R.

Abstract

In this paper, we show that vector auto-regression (VAR) models, which are commonly used to estimate permanent price impact, are misspecified and can produce conflicting and incorrect inferences when the price impact function is nonlinear. We propose an alternative method to estimate permanent price impact by modifying a reinforcement learning (RL) framework. Our approach assumes the data is stationary and Markov, but is otherwise unrestrictive. We obtain empirical estimates for our model using an iterative learning rule and demonstrate that our model captures nonlinearities and makes correct inferences.

Suggested Citation

  • Philip, R., 2020. "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, vol. 215(2), pages 414-449.
    • Handle: RePEc:eee:econom:v:215:y:2020:i:2:p:414-449
    DOI: 10.1016/j.jeconom.2019.10.002
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    Cited by:

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    2. Upson, James & McInish, Thomas & IV, B. Hardy Johnson, 2021. "Order based versus level book trade reporting: An empirical analysis," Journal of Banking & Finance, Elsevier, vol. 125(C).
    3. 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.
    4. Sadoghi, Amirhossein & Vecer, Jan, 2022. "Optimal liquidation problem in illiquid markets," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1050-1066.
    5. Doan, Bao & Vo, Duc Hong, 2021. "Is there any information content of traded stocks in an emerging market? Evidence from Vietnam," International Economics, Elsevier, vol. 167(C), pages 78-87.
    6. Amirhossein Sadoghi & Jan Vecer, 2022. "Optimal liquidation problem in illiquid markets," Post-Print hal-03696768, HAL.
    7. Chaeshick Chung & Sukjin Park, 2021. "Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks," Working Papers 2108, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
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    9. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    10. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.

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    More about this item

    Keywords

    Price impact; Information content of a trade; Machine learning; Reinforcement learning;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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