The use of high-frequency data in cryptocurrency research: A meta-review of literature with bibliometric analysis
SSRN Electronic Journal
2023
- 1Citations
- 1,276Usage
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
As the crypto-asset ecosystem matures, the use of high-frequency data becomes increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. Based on 189 articles collected from the Scopus database from 2015 to 2022, we highlight the most influential authors, articles, and journals. This approach enables us to identify the emerging trends and research hotspots with the aid of co-citation and cartographic analyses and shows the knowledge expansion through authors’ collaboration in the cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of volatility in cryptocurrencies; (ii) (in)efficiency of cryptocurrencies; (iii) price dynamics and bubbles in cryptocurrencies; and (iv) diversification, safe haven and hedging properties of Bitcoin. We conclude that the investment features and the economic outcomes of highly trading cryptocurrencies would be analysed predominantly on tick-by-tick bases in the future. This paper provides recommendations for future research.
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