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Contrastive Learning Framework for Bitcoin Crash Prediction

Author

Listed:
  • Zhaoyan Liu

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA)

  • Min Shu

    (Department of Statistics, Actuarial and Data Sciences, Central Michigan University, Mt Pleasant, MI 48859, USA)

  • Wei Zhu

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA)

Abstract

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model’s ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes.

Suggested Citation

  • Zhaoyan Liu & Min Shu & Wei Zhu, 2024. "Contrastive Learning Framework for Bitcoin Crash Prediction," Stats, MDPI, vol. 7(2), pages 1-32, May.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:2:p:25-433:d:1390045
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