Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018

Mutual-excitation of cryptocurrency market returns and social media topics

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of ‘risk and investment vs trading’ being indicative of price falls, the discussion of ‘substantial price movements’ being indicative of volatility, and the discussion of ‘fundamental cryptocurrency value’ by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals

Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance

The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

The Predictive Power of Social Media within Cryptocurrency Markets

Blockchain technology has generated a great deal of interest in recent years, as has the associated area of cryptocurrency trading, not only on the part of individuals but also from traditional financial institutions and hedge funds. However, there is currently limited knowledge as to how to predict future cryptocurrency price movements. This thesis investigates whether online indicators, especially from social media, can be harnessed to predict cryptocurrency price movements – to achieve this, three experiments are conducted. The first experiment analyses time-evolving relationships between chosen online indicators and associated cryptocurrency prices; relationships are considered over short, medium and long-term durations. The work introduces and evaluates several influential factors from the social media platform Reddit, a platform previously unexplored within cryptocurrency prediction literature. It is found that medium and longer-term relationships strengthen in bubble market regimes (compared to non-bubble regimes). The second experiment utilises these promising new factors as inputs to a predictive model. The model used was originally designed to detect influenza epidemic outbreaks, and is repurposed here to model epidemic-like cryptocurrency price bubbles, demonstrating how social media can be used to track the epidemic spread of an investment idea. The predictive power of the model is validated through the generation of a profitable trading strategy. Having considered quantitative count-based metrics in the previous chapters (e.g. posts per day, submissions per day, new authors per day etc.), the next experiment considers the content of social media submissions. More specifically, the third experiment analyses social media submission content to investigate whether certain topics of discussion precede upcoming shorter term (positive or negative) price movements. Information evidencing time-varying interest in various topics is retrieved from social media submissions, upon which hidden interactions with the associated cryptocurrency price are deciphered. It is found that certain topics precede major positive or negative price movements, and also additional analysis shows that certain discussion topics exhibit longer-term relationships with cryptocurrency market prices

Cryptocurrency price prediction based on multiple market sentiment

With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult. To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

Bullish Sentiment on Price Upward Trend : A Netnographic Study of Cryptocurrency Communities

Cryptocurrency as a digital decentralized currency has attracted many investors and obtained a lot of support from communities. Previous studies have concluded that there were indeed connections between community sentiment and cryptocurrency price movement. However, most of the research were conducted using sophisticated methods that difficult to utilized by cryptocurrency investors. This research objective was to find practical ways to identify bullish sentiment during price upward trend especially during the Covid-19 omicron variant outbreak that started in the last quarter of 2021. Netnography method was used as qualitative approach for this research to get insight from cryptocurrency communities. LunarCrush web application as a more user-friendly tool, was being used to analyze bullish sentiment and price data. During December 2021 until January 2022, 303 price upward trend data from 264 coins had been collected and analyzed. The result of this research revealed 5 categories of bullish sentiment messages from top influencers which are community booster, official information, project updates, achievement, and trading plan. However, it can be concluded that price movements were not always related with bullish sentiment. Thus, bullish sentiment should not be used as the sole factor to identify price upward trends. Furthermore, investors should join the cryptocurrency community to understand the characteristics of bullish sentiment and not just rely on data from monitoring tools. Interestingly, there were no Covid-19 related topics on bullish sentiment collected. Hence, it did not necessarily need to publish good news related to Covid-19 handling to create bullish sentiment among the investors

Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing

Copyright © 2021 The Author(s). We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”1 that took place in January 2021.Funding: No funding was received for this work

A modulated renewal Hawkes process and its application to modelling extreme mid-price drops on cryptocurrencies

The Hawkes process was first proposed by Alan G. Hawkes in which the arrival of events exhibits a self-exciting behaviour. One extension of the classical Hawkes process is the renewal Hawkes process, which allows the underlying process for background events to be a renewal process, rather than the homogeneous Poisson process in the classical Hawkes process. The renewal Hawkes process is stationary in nature, so it is not suitable in situations where there are systematic trends in event occurrence rate. Therefore, in this thesis, we propose a renewal Hawkes process in which a trend function is employed to account for the systematic patterns in the event occurrence rate. We term the process the modulated renewal Hawkes process. Due to the lack of an explicit expression for the intensity process, likelihood evaluation for the modulated renewal Hawkes process model is not trivial. However, by modifying the likelihood evaluation algorithm for renewal Hawkes process in Chen & Stindl (2018), we are able to propose an algorithm to evaluate the exact likelihood of the modulated renewal Hawkes process model. The evaluated likelihood can then be maximised to obtain the maximum likelihood estimator (MLE) of the model parameters. We also propose a method to obtain fast and accurate approximations to the likelihood. In the case where a suitable parametric form of the trend function is not available, we approximate the trend function using B-spline functions. We also derive the Rosenblatt residuals of the modulated renewal Hawkes process, which can serve as a basis for assessing the goodness-of-fit of the model. Simulation experiments were conducted to assess the performance of the MLE of the modulated renewal Hawkes process with either exact or approximate likelihood evaluation, both in the parametric model and in the semiparametric model with an unspecified trend function. We also present an application of the modulated renewal Hawkes process model to the analysis of cryptocurrency data. The modulated renewal Hawkes process model with a B-spline trend function is applied to model extreme intraday negative returns on several cryptocurrencies. The estimated trend function suggests an inverse U-shaped trend in the intraday occurrence times of extreme negative returns on cryptocurrencies. We also compared the model fitting results with several simpler models, such as the nonstationary Hawkes process and the renewal Hawkes process. On most of the cryptocurrency data sets considered in this work, the modulated renewal Hawkes process was found to provide the best fit both by the Rosenblatt residuals based goodness-of-fit check and by the Akaike Information Criterion