A Machine Learning Based Regulatory Risk Index for Cryptocurrencies

TL;DR

This paper introduces CRRIX, a machine learning-based index that quantifies regulatory risks in cryptocurrencies by analyzing policy news, effectively capturing major policy shifts and correlating with market volatility.

Contribution

The paper develops a novel regulatory risk index for cryptocurrencies using news classification and machine learning, providing a new tool for market risk assessment.

Findings

CRRIX captures major policy-changing moments effectively.

CRRIX movements are synchronous with market volatility index VCRIX.

Algorithms and code are publicly available for research.

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.