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Ascertaining price formation in cryptocurrency markets with DeepLearning

Author

Listed:
  • Fan Fang
  • Waichung Chung
  • Carmine Ventre
  • Michail Basios
  • Leslie Kanthan
  • Lingbo Li
  • Fan Wu

Abstract

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.

Suggested Citation

  • Fan Fang & Waichung Chung & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & Fan Wu, 2020. "Ascertaining price formation in cryptocurrency markets with DeepLearning," Papers 2003.00803, arXiv.org.
    • Handle: RePEc:arx:papers:2003.00803
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    References listed on IDEAS

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    6. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
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    Cited by:

    1. Nikolaos A. Kyriazis, 2021. "Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 845-861, December.

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