Influencers, are they responsible for Bitcoin's volatility? Transfer entropy and Granger causality in prol of an answer

Bitcoin, like any other cryptocurrency, is subject to fluctuations in price. The volatility of this market can be a reflection of several reasons, such as public opinion, social networks and news. Social networks, in particular Twitter, are increasingly used as an important source of value extraction because through this network, it is possible to find out about news in real-time, follow the repercussions, know what experts in the financial world are commenting or thinking and even decide based on influencer's opinion whether to invest or not. This study investigates the influence that a specific group of people exert on Bitcoin volatility. A selection of influencers from the “crypto world” was made, and through the Twitter API, it was possible to select the tweets of the object of study. To choose the classification model for sentiment analysis, two techniques were compared, one being very popular with a focus on the domain of social networks and the other recently created and focused on finance. From the selected technique, only positive and negative sentiments were considered, and then the daily series of the Sentiment Score was calculated. Next, the causal relationship between Bitcoin and sentiment was investigated using Granger causality and Transfer Entropy tests. Transfer Entropy showed encouraging results, suggesting that there is a transfer of information from Sentiment to Returns and that it is possible for an influencer to contribute to Bitcoin’s volatilityO Bitcoin, assim como qualquer outra criptomoeda, está sujeito a flutuações no preço. A volatilidade desse mercado pode ser reflexo de vários motivos, tais como, opinião pública, redes sociais e notícias. As redes sociais, em particular o Twitter, cada vez mais é utilizado como uma fonte importante de extração de valor, isto porque através desta rede é possível saber das novidades em tempo real, acompanhar as repercussões, saber o que entendedores do mundo financeiro estão a comentar e decidir até mesmo com base na opinião de um influenciador se irá investir ou não. Este estudo investiga a influência que determinadas pessoas exercem sobre a volatilidade do Bitcoin. Foi feita uma seleção de influenciadores do “mundo crypto” e através da API do Twitter foi possível selecionar os tweets de objeto de estudo. Para a escolha do modelo de classificação para análise de sentimento foram comparadas duas técnicas, sendo uma muito popular com foco no domínio de redes sociais e a outra recém-criada e focada em finanças. A partir da técnica selecionada, apenas os sentimentos positivos e negativos foram considerados e então calculada a série diária do Sentiment Score. A seguir foi investigada a relação causal entre o Bitcoin e o sentiment utilizando os testes de causalidade de Granger e Entropia de Transferência. A Entropia de Transferência mostrou resultados animadores que sugerem existir transferência de informação de Sentiment para Returns e que, portanto, é possível que um influencer contribua para a volatilidade do Bitcoin

Forecasting bitcoin's volatility: Exploring the potential of deep-learning

The importance of using the right statistical, mathematical and computational tools can highly influence the decision-making process. With the recent computational progress, Deep Learning methodologies based on Artificial Intelligence seem to be pointed out as a promising tool to study financial time series, characterised by out-of-the-ordinary patterns. Cryptocurrencies are a new asset class with several specially interesting characteristics that still lack deep study and differ from the traditional time series. Bitcoin in particular is characterised by extraordinary high volatility, high number of structural breaks and other identified characteristics that might further difficult the study and forecasting of the time series using classical models. The goal of this study is to critically compare the forecasting properties of classic methodologies (ARCH and GARCH) with Deep Learning Techniques (with MLP, RNN and LSTM architectures) when forecasting Bitcoin’s Volatility. The empirical study focuses on the forecasting of Bitcoin’s Volatility using such models and comparing its forecasting quality using MAE and MAPE for one, three- and seven-day’s forecasting horizons. The Deep learning methodologies show advantages in terms of forecasting quality (when we take in consideration the MAPE) but also require huge computational costs. Diebold-Mariano tests were also performed to compare the forecasts concluding the superiority of Deep Learning Methodologies.A importância de usar as ferramentas estatísticas, matemáticas e computacionais certas pode certamente influenciar o processo de decisão. Com os recentes avanços computacionais, as metodologias Deep-Learning, baseadas em Inteligência Artificial apontam para uma ferramenta promissora para o estudo de séries temporais de dados financeiros, caracterizadas por padrões que são fora do normal. As criptomoedas são uma nova classe de ativos que são caracterizados por alta volatilidade, elevado número de quebras de estrutura e outras características que podem dificultar o estudo e previsão por parte de modelos clássicos. O objetivo deste trabalho é analisar de forma crítica as capacidades de previsão das metodologias clássicas (ARCH e GARCH) comparativamente a metodologias de Deep-Learning (nomeadamente arquiteturas de redes neuronais: MLP, RNN e LSTM) para a previsão da volatilidade da bitcoin. O estudo empírico deste trabalho foca-se na previsão da volatilidade da bitcoin com os modelos supramencionados e comparar a sua qualidade preditiva usando as medidas de erro MAE e MAPE para horizontes de previsão de um, três e sete dias. As metodologias de Deep-Learning apresentam algumas vantagens no que respeita à qualidade de previsão (pela análise da métrica de erro MAPE) mas apresentam um custo computacional superior. Também foram realizados Testes de Diebold-Mariano para comparar as previsões, concluindo-se a superioridade das metodologias de Deep-Learning

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

Cryptocurrency and trading strategies

The aim of this dissertation is to provide a review on the current cryptocurrency economics which is still vague to a vast number of investors. Regression results suggest some but limited similarities to stocks with regards to the price movements in the market. The goal of the dissertation is to examine the profitability of moving average trading strategies with 3, 9 and 30-days moving averages which have only been tested on a longer lag moving average and the feasibility of volatility timing strategy which has not yet been implemented on Bitcoin markets. Results show that moving average strategies significantly outperform the Buy-and-Hold Bitcoin benchmark, but increase the higherorder risk. The volatility timing strategy did not produce the desired decrease in higher-order risk. However, this result does not rule-out the possibility that an application of a more sophisticated asset-pricing model could further decrease excess kurtosis, which seems problematic for a broader scope of investors since there is a continuous risk of crash present in the cryptocurrency markets.O objetivo desta dissertação é fornecer uma análise da atual economia do cripto moeda, que ainda é vaga para um grande número de investidores. Os resultados das regressões sugerem algumas semelhanças, mas limitadas, com a existência de momentum no mercado actionista. O objetivo da dissertação é examinar a rentabilidade das estratégias de investimento usando médias móveis de 3, 9 e 30 dias que só foram testadas numa média móvel de longo prazo e a viabilidade da estratégia ajustar a alavancagem para atingir uma volatilidade alvo que ainda não foi implementada nos mercados de Bitcoin. Os resultados mostram que as estratégias usando médias móveis médias superam significativamente o benchmark Buy-and-Hold Bitcoin, mas aumentam o risco de curtose excessiva mais elevado. A estratégia de volatilidade alvo não produziu a diminuição desejada do risco de ordem superior. No entanto, esse resultado não descarta a possibilidade de que a aplicação de um modelo de preços de ativos mais sofisticado possa diminuir ainda mais a curtose excessiva, o que parece problemático para um âmbito mais alargado de investidores, uma vez que existe um risco contínuo de perdas extremas nos mercados de cripto moeda

Internet in the European Union: past, present and future of digitalization

openThe Internet is a complex tool that completely changed the world in recent years, enabling fast connections all over the world, and at the same time giving birth to a new digital economy. To better understand this proces, and particularly to understand what lies ahead for the European Union in terms of policy decisions, it is necessary to analyse the history, the economy and the implications of the internet in the international context. These considerations will be used to reflect on the upcoming challenges, in terms of security, privacy and power that the European Union will have to deal with in the upcoming years

Експериментальна економіка та машинне навчання для прогнозування динаміки емерджентної економіки: матеріали вибраних робіт 8-ї Міжнародної конференції з моніторингу, моделювання та управління емерджентною економікою (M3E2 2019)

This volume represents the proceedings of the selected papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), held in Odessa, Ukraine, on May 22-24, 2019. It comprises 38 papers dedicated to the experimental economics and machine learning that were carefully peer-reviewed and selected from 71 submissions.Цей том представляє вибрані матеріали 8-ої Міжнародної конференції "Моніторинг, моделювання та менеджмент емерджентної економіки" (M3E2 2019), що відбулася в Одесі, Україна, 22-24 травня 2019 року. Він містить 38 робіт, присвячених експериментальній економіці та машинному навчанню, які були ретельно прорецензовані та відібрані з 71 подання

8 Міжнародна конференція "Моніторинг, моделювання та менеджмент емерджентної економіки" (M3E2 2019)

Матеріали конференції.Proceedings

Coordinating crowdfunded innovation projects in the cryptocurrency sector through narratives

Initial Coin Offerings (ICOs) are innovation projects that serve as a crowdfunding mechanism. This study discusses the need to examine how cryptocurrencies are created, particularly through ICOs. ICO projects are described as radical, multistakeholder innovation projects that involve communicating potential value and use of cryptocurrency between project teams and crowdfunders through whitepapers and YouTube videos. The study aims to identify useful dictionaries containing active signals and cues used in successfully crowdfunded ICO narratives from project teams (Marketer Generated Content) and crowdfunders’ comments (User Generated Content). The literature review highlights three gaps: a lack of understanding the effectiveness of specific signals/cues and crowdfunders' trust, a lack of diversification in signal/cue specific constructs, and a lack of understanding of positive tone signals/cues in the ICO context. The study tests the use of specific signals and cues. It does so with dictionaries that have been validated for academic studies in Marketer Generated Content (MGC) and then correlates with their use in User Generated Content (UGC) in ICOs. The study collected and analysed textual data on 20 ICOs through YouTube video transcripts, comments, and whitepapers, and used Computer Aided Text Analysis (CATA) software and customised digital dictionaries. The analysis aimed to identify patterns of cues/signals in project and crowdfunder narratives. The selection of the dictionaries was based on their relevance to the signals/cues being measured and bundled into constructs for analysis using CATA software. Overall, the study shed light on the coordination practices of ICOs, identifying the dictionaries that suit ICOs, and test and confirm hypotheses using signalling theory to address the gaps in the literature. The finding was that positive signals and cues from the project instigator’s whitepapers and video-transcripts have been used in crowd conversations in YouTube comment-sections, but much less so negative ones. Eight useful academic dictionaries have been identified. This study can be extended to other studies to explore further the issue of narrative coordination of innovation projects, but it also contributes to practitioners with practical application in an effort to coordinate ICO crowdfunding