Contrastive Learning Framework for Bitcoin Crash Prediction

Zhaoyan Liu (), Min Shu and Wei Zhu
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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

Stats, 2024, vol. 7, issue 2, 1-32

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.

Keywords: cryptocurrency; deep learning; machine learning; representation learning; time series classification (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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