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A Machine Learning Based Regulatory Risk Index for Cryptocurrencies

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  • Xinwen Ni
  • Wolfgang Karl Hardle
  • Taojun Xie

Abstract

Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on www.quantlet.de.

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  • Xinwen Ni & Wolfgang Karl Hardle & Taojun Xie, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," Papers 2009.12121, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2009.12121
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    3. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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