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Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency

Author

Listed:
  • Feng Han

    (Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
    Current address: Clear Water Bay, Sai Kung, New Territories, Hong Kong, China.)

  • Xiaojuan Ma

    (Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
    Current address: Clear Water Bay, Sai Kung, New Territories, Hong Kong, China.)

  • Jiheng Zhang

    (Department of Industrial Engineering and Decision Analytics and Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
    Current address: Clear Water Bay, Sai Kung, New Territories, Hong Kong, China.)

Abstract

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

Suggested Citation

  • Feng Han & Xiaojuan Ma & Jiheng Zhang, 2022. "Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency," JRFM, MDPI, vol. 15(1), pages 1-21, January.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:1:p:26-:d:721117
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    References listed on IDEAS

    as
    1. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
    2. David R. Meyer & George Guernsey, 2017. "Hong Kong and Singapore exchanges confront high frequency trading," Asia Pacific Business Review, Taylor & Francis Journals, vol. 23(1), pages 63-89, January.
    3. Mensi, Walid & Hernandez, Jose Arroeola & Yoon, Seong-Min & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Spillovers and connectedness between major precious metals and major currency markets: The role of frequency factor," International Review of Financial Analysis, Elsevier, vol. 74(C).
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