Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 013

A Machine Learning Based Regulatory Risk Index for Cryptocurrencies

Xinwen Ni
Wolfgang Karl Härdle
Taojun Xie

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

Cryptocurrency, Regulatory Risk, Index, LDA, News Classification

JEL Classification:
C45, G11, G18