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Deep Learning Option Price Movement

Author

Listed:
  • Weiguan Wang

    (School of Economics, Shanghai University, 333 Nanchen Road, Baoshan District, Shanghai 200444, China
    These authors contributed equally to this work.)

  • Jia Xu

    (School of Economics, Shanghai University, 333 Nanchen Road, Baoshan District, Shanghai 200444, China
    These authors contributed equally to this work.)

Abstract

Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features.

Suggested Citation

  • Weiguan Wang & Jia Xu, 2024. "Deep Learning Option Price Movement," Risks, MDPI, vol. 12(6), pages 1-17, June.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:6:p:93-:d:1408678
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    References listed on IDEAS

    as
    1. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
    2. Álvaro Arroyo & Álvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2024. "Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers," Quantitative Finance, Taylor & Francis Journals, vol. 24(1), pages 35-57, January.
    Full references (including those not matched with items on IDEAS)

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