Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

Georgoula, Ifigeneia; Pournarakis, Demitrios E.; Bilanakos, Christos; Sotiropoulos, Dionisios N.; Giaglis, George M.

doi:10.2139/ssrn.2607167

Cited by 131 publications

(92 citation statements)

References 11 publications

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“…The third group of research comprises of researchers attempting to use every factor to predict Bitcoin price. Georgoula et al [12] and Garcia et al [13] contribute their work in this way. As they provide many conclusions, we are not summarizing here.…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrency Investing Examined

Liew¹,

Li²,

Budavàri³

et al. 2019

The JBBA

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In this work we examine the largest 100 cryptocurrency returns ranging from 2015 to early 2018. We concentrate our analysis on daily returns and find several interesting stylized facts. First, principal components analysis reveals a complex daily return generating process. As we examine data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation. Second, similar to hedge fund returns, cryptocurrency returns suffer from the "beta-in-the-tails" hidden risk. Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per crypto-currency. Fourth, lower volatile cryptocurrencies are slightly more predictable than more volatile ones. Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms. Finally, short-term predictability is very tenuous, which suggests that near-term cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging.

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Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrency Investing Examined

Liew¹,

Li²,

Budavàri³

et al. 2019

The JBBA

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“…Using SVM algorithms, binomial logistic regression classifiers, and random forests, they predicted the Bitcoin price with an accuracy of 55%. Georgoula et al (2015) investigated the determinants of the Bitcoin rate along with an emotional analysis using SVM. The result showed that the amount of Wikipedia hits and hash rates in the network had a positive relationship with the Bitcoin price.…”

Section: Bitcoinmentioning

confidence: 99%

Predicting changes in Bitcoin price using grey system theory

Jalali

Heidari

2020

Financ Innov

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Bitcoin is currently the leading global provider of cryptocurrency. Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage. In recent years, the Bitcoin network has attracted investors, businesses, and corporations while facilitating services and product deals. Moreover, Bitcoin has made itself the dominant source of decentralized cryptocurrency. While considerable research has been done concerning Bitcoin network analysis, limited research has been conducted on predicting the Bitcoin price. The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory. The first order grey model (GM (1,1)) is used for this purpose. It uses a firstorder differential equation to model the trend of time series. The results show that the GM (1,1) model predicts Bitcoin's price accurately and that one can earn a maximum profit confidence level of approximately 98% by choosing the appropriate time frame and by managing investment assets.

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“…They find their sentiment measure to correlate and co-integrate with an official consumer index. Georgoula et al (2015) use time-series analysis to study the relationship between Bitcoin prices, fundamental economic variables, and measurements of collective mood derived from Twitter. Using an SVM classifier trained on tweets mentioning Bitcoin, they obtain a sentiment measure which is used as a variable in an OLS and a VECM models.…”

Section: Sentiment Analysismentioning

confidence: 99%

Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

Pekar

Binner

2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Consumer spending is a vital macroeconomic indicator. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three-and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.

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Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

Cited by 131 publications

References 11 publications

Cryptocurrency Investing Examined

Cryptocurrency Investing Examined

Predicting changes in Bitcoin price using grey system theory

Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

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