A Machine Learning Based Regulatory Risk Index for Cryptocurrencies

37 Pages Posted: 11 Nov 2020 Last revised: 24 Aug 2021

See all articles by Xinwen Ni

Xinwen Ni

School of Business and Economics

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Taojun Xie

National University of Singapore (NUS) - Asia Competitiveness Institute

Date Written: April 30, 2021

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 unlabelled 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.

Keywords: Cryptocurrency, Regulatory Risk, Index, LDA, News Classification

JEL Classification: C45, G11, G18

Suggested Citation

Ni, Xinwen and Härdle, Wolfgang K. and Xie, Taojun, A Machine Learning Based Regulatory Risk Index for Cryptocurrencies (April 30, 2021). Available at SSRN: https://ssrn.com/abstract=3699345 or http://dx.doi.org/10.2139/ssrn.3699345

Xinwen Ni (Contact Author)

School of Business and Economics ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Taojun Xie

National University of Singapore (NUS) - Asia Competitiveness Institute ( email )

469C Bukit Timah Road
Level 3, Wing A
259772
Singapore

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