Performance of Cryptocurrencies Under a Sentiment Analysis Approach in the Time of COVID-19

Evangelos Vasileiou () and Petros Koutrakos
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Evangelos Vasileiou: University of the Aegean, Department of Financial and Management Engineering
Petros Koutrakos: University of the Aegean, Department of Financial and Management Engineering

A chapter in Data Analytics for Management, Banking and Finance, 2023, pp 255-265 from Springer

Abstract: Abstract This chapter presents a sentiment analysis model that examines the performance of the most popular and highly capitalized cryptocurrencies during the COVID-19 era. Specifically, we look at the performance of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Cardano (ADA) during the period 1/1/2020–30/9/2021. We do not use unique and expensive data and we do not apply complex models for our analysis. Instead, we construct behavioral indices based on Google trends data and we test the impact of these indices on the performance of BTC, ETH, BNB, and ADA. We used open source software, such as Python, so similar analysis can be carried out by anyone who is interested in financial markets. The results of this study support the following conclusions: (i) when the health risk increases, the BTC, BNB, and ADA prices fall (and vice versa), (ii) the Google trends indices, which reveal the users’ intention to buy a cryptocurrency, could be useful indicators of crypto performance, but (iii) the sell sentiment, at least in our case, does not have any statistically significant effect. Thus, sentiment analysis based on Google searches could be useful for practitioners, investors, analysts, and scholars, and further study on this direction should be done.

Keywords: Health risk; COVID-19; Sentiment analysis; Google trends; Behavioral finance (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-36570-6_11

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