SMU Data Science Review
Abstract
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
Recommended Citation
Abraham, Jethin; Higdon, Daniel; Nelson, John; and Ibarra, Juan
(2018)
"Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis,"
SMU Data Science Review: Vol. 1:
No.
3, Article 1.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss3/1
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Applied Statistics Commons, Categorical Data Analysis Commons, Statistical Models Commons
