Nasir, MA orcid.org/0000-0003-2779-5854, Huynh, TLD, Nguyen, SP et al. (1 more author) (2019) Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5 (1). 2. ISSN 2199-4730
Abstract
In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s). 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Financial innovation; Forecasting; Blockchain; Google search values; Bitcoin; Cryptocurrencies |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Feb 2022 13:05 |
Last Modified: | 25 Feb 2022 13:05 |
Status: | Published |
Publisher: | SpringerOpen |
Identification Number: | 10.1186/s40854-018-0119-8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184076 |