[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125321-en":3,"doc-seo-125321-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125321,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A machine learning based regulatory risk index for cryptocurrencies","Cryptocurrency markets react sharply to regulatory changes, yet existing market indices often fail to quantify risks arising from regulatory uncertainty. This paper proposes the Cryptocurrency Regulatory Risk Index (CRRIX), a machine learning-based measure that estimates how policy developments influence cryptocurrency markets. Using Latent Dirichlet Allocation (LDA), the approach classifies policy-related news articles from major crypto news sources. The resulting CRRIX closely tracks the volatility index VCRIX, indicating that regulatory uncertainty can be a leading driver of market fluctuations and policy shifts can trigger significant moves.","Computational Statistics (2025) 40:3563–3583  \n[https://doi.org/10.1007/s00180-025-01629-y](https://doi.org/10.1007/s00180-025-01629-y)  \nORIGINAL PAPER  \nA machine learning based regulatory risk index for cryptocurrencies  \nXinwen Ni1 · Taojun Xie2 · Wolfgang Karl Härdle1,2,3,4,5 · Xiaorui Zuo3,6  \nReceived: 6 November 2024 / Accepted: 4 April 2025 / Published online: 19 May 2025 © The Author(s) 2025  \nAbstract  \nCryptocurrency markets are highly sensitive to regulatory changes, often experiencing sharp price fluctuations in response to new policies and government interventions. Despite this, existing market indices fail to adequately capture the risks associated with regulatory uncertainty. In this paper, we introduce the Cryptocurrency Regulatory Risk Index (CRRIX), a machine learning-based index designed to quantify the impact of regulatory developments on cryptocurrency markets. Our methodology employs Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms, providing an objective measure of regulatory risk. We find that the CRRIX exhibits strong synchronicity with VCRIX, a cryptocurrency volatility index, suggesting that regulatory uncertainty plays a significant role in driving market fluctuations. Our results indicate that regulatory risk is a leading factor in market volatility, with major policy shifts triggering significant market movements. The proposed regulatory risk index provides a novel approach to quantifying policy uncertainty in the cryptocurrency sector, offering valuable insights for market participants navigating this rapidly changing environment.  \nKeywords Cryptocurrency · Regulatory risk · Index · LDA · News classification  \nThe authors gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft through the International Research Training Group IRTG 1792 \"High Dimensional Non Stationary Time Series\". This paper was supported through \"IDA Institute of Digital Assets\", CF166/15 .11.2022, contract number 760046/23 .05.2023, financed under Romania’s National Recovery and Resilience Plan, Apel nr. PNRR-III-C9-2022-I8 . We gratefully acknowledge the support of the Marie Skłodowska-Curie Actions under the European Union’s Horizon Europe  \nresearch and innovation program for the Industrial Doctoral Network on Digital Finance, acronym: DIGITAL, Project No. 101119635.  \nExtended author information available on the last page of the article  \n1 Introduction  \nToday, there are more than 12,000 cryptocurrencies (CC) in existence, discounting many \"dead\" cryptos leaves us with more than 8,000 more or less active types of cryptocurrencies with around 400 million users across the globe and a market value worth more than $2.5 trillion,(Dolan 2020) . Cryptocurrencies have been created in a largely unregulated environment, although several news channels and media outlets have warned of possible hazards and risk of total loss of investment. Since the birth of Bitcoin (BTC) following the paper of (Nakamoto 2008) new blockchain based tokens like NFTs (non fungible tokens) have been produced and investors paid closer attention to the market. Regulators and international actors, though, remained largely silent, hence prices continued to soar unabated and more new digital tokens were invented. However, the situation changed as with more types of coins and less energy consuming blockchains the financial sector and the regulatory institutions looked more closely to the digital token world.  \nRegulations are designed to protect the investors, to put a stop on money laundering, or to prevent the fiat currency from being crowded out. Despite these intentions, speculation and implementation of regulations have resulted in volatile price movements in the cryptocurrency markets. Recent incidents, including China’s ban on cryptocurrency exchanges and the rumours of South Korea following this decision have caused major sell-offs and losses among investors.","cbCaiujiH2qOmIqK","https://ap.wps.com/l/cbCaiujiH2qOmIqK","pdf",1980900,1,21,"English","en",105,"# Introduction\n## Problem motivation and regulatory risk\n## Related indices and limitations\n# Method and proposed index\n## News classification with LDA\n## Construction of CRRIX\n# Empirical findings\n## Synchrony with VCRIX\n## Regulatory risk as a driver of volatility","[{\"question\":\"What does the Cryptocurrency Regulatory Risk Index (CRRIX) measure?\",\"answer\":\"CRRIX quantifies the impact of regulatory developments and regulatory uncertainty on cryptocurrency markets using a machine learning framework.\"},{\"question\":\"How is regulatory information extracted for CRRIX?\",\"answer\":\"The method applies Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms.\"},{\"question\":\"What is the relationship between CRRIX and market volatility?\",\"answer\":\"The paper reports strong synchronicity between CRRIX and VCRIX, suggesting regulatory uncertainty significantly contributes to cryptocurrency price fluctuations.\"}]","A machine learning based regulatory risk index for cryptocurrencies | 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does the Cryptocurrency Regulatory Risk Index (CRRIX) measure?","Question",{"text":75,"@type":76},"CRRIX quantifies the impact of regulatory developments and regulatory uncertainty on cryptocurrency markets using a machine learning framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is regulatory information extracted for CRRIX?",{"text":80,"@type":76},"The method applies Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the relationship between CRRIX and market volatility?",{"text":84,"@type":76},"The paper reports strong synchronicity between CRRIX and VCRIX, suggesting regulatory uncertainty significantly contributes to cryptocurrency price 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