Lead-lag relationship between Bitcoin and Ethereum: evidence from hourly and daily data

This paper investigates lead-lag relationship between heavyweight cryptocurrencies Bitcoin and Ethereum. Traditional studies of information flow between markets preponderate on cash vs. futures, whereby researchers are interested in the stabilizing impact of futures on spot markets. While interest in the same relationship in the nascent cryptocurrency sphere is emerging, little is known regarding price leadership between these assets. In this paper, we employ a battery of statistical tests—VECM, Granger Causality, ARMA, ARDL and Wavelet Coherence—to identify price leadership between the two crypto heavyweights Bitcoin and Ethereum. Based on one year hourly and daily data from August 2017 through to September 2018, our tests yield varied results but largely suggest bi-directional causality between the two assets. Moreover, the results indicate that intraday crypto traders can barely exploit Bitcoin-Ethereum hourly or daily price discovery process to their advantage

What can Google Tell us about Bitcoin Trading Volume in Croatia? Evidence from the Online Marketplace Localbitcoins

Timely economic statistics is crucial for effective decision making. However, most of them are released with a lag. Thus, "nowcasting" has become widely popular in economics, and web search volume histories are already used to make predictions in various fields including IT, communications, medicine, health, business and economics. This article seeks to explore the potential of incorporating internet search data, in particular Google Trends data, in autoregressive models used to predict the volume of Bitcoin trading. Toda and Yamamoto procedure was applied in order to examine causality between Google search data and Bitcoin trading volume on the online marketplace LocalBitcoins, for the area of the Republic of Croatia. The results showed that internet search data can be useful for forecasting Bitcoin trading volume, since Google searches for the term “bitcoin” Granger causes Bitcoin trading volume in the online marketplace LocalBitcoins

The predictor impact of Web Search and Social Media

In recent years, web search and social media have emerged online. Search engine technology has had to speed up to keep up with the growth of the World Wide Web, that has turned the Internet into a wide information space with different and badly managed content. Millions of people all over the world search online several information each day, which makes Web search queries a valuable source of information. Due to the huge amount of available information, searching has become dominant in the use of Internet. Users that daily interact with search engines, produce valuable sources of interesting data regarding several aspects of the world. Social media increasingly pervades life in several fields of the world, enabling communication among users and collecting massive amount of information for social media companies that want to refine their products. Popular services like Twitter and Facebook attract a lot of users who share facts of their daily life. This kind of content has become more present on the web and, due to its public nature, even appears in search results from search engines, like Google and Bing. With the explosion of user generated content, came the need by politicians, analysts, researcher to monitor the content of different users. During my PhD, I decided to investigate whether social media activity or information collected by web search media could be profitable and used for predictive purposes. I studied whether some relationship exists between particular phenomena and volume of search data, considering the examined topic on web engines. Then, I analyzed the related social volume in order to discover whether the chatter of the community can be used to make qualitative predictions about the considered phenomena, attempting to establish whether there is any correlation. Simultaneously, I decided to apply automated Sentiment Analysis on shared short messages of users on Twitter in order to automatically analyze people opinions, sentiments, evaluations and attitude

Economic Simulation of Cryptocurrencies and Their Control Mechanisms

A cryptocurrency needs a relatively stable value if it is to fulfill the traditional functions of money and be useful as a currency. To achieve this, controls are needed within the ecosystem of the cryptocurrency. Although a simulation cannot predict future currency rates or other variables exactly, it is argued that a model that simulates a range of challenging behavior can be a useful testbed for control schemes. To illustrate and explore this idea, an agent-based economic model was used to simulate the early period of a hypothetical cryptocurrency and test two control mechanisms. The results suggest that this approach may be fruitful and that it may be important to include more than just coin minting within the control scheme. An economic simulation model is likely to be a valuable tool in developing and regulating effective cryptocurrency systems

Forecasting bitcoin prices: ARIMA vs LSTM

Bitcoin has recently received special attention in economics and finance as the most popular blockchain technology. This dissertation aims to discuss whether newly machine-leaning models perform better than traditional models in forecasting. Particularly, this study compares the accuracy of the prediction of bitcoin prices using two different models: Long-Short Term Memory (LSTM) versus Auto Regressive Integrated Moving Average (ARIMA), in terms of forecasting errors, and Python routines were used for such purpose. Bitcoin price time series ranges from 2017-06-18 to 2019-08-07, in a daily basis, sourced from the Federal Reserve Economic Data. To compare the results of both models, data was divided into two subsets: training (83.5%) and testing (16.5%). The literature usually indicates that LSTM outperforms ARIMA. In this dissertation, the results do confirm that LSTM forecasts of bitcoin prices improve on average ARIMA predictions by 92% and 94%, according to RMSE and MAE.A Bitcoin tem recebido recentemente especial atenção em áreas como a economia e finanças por ser a mais popular tecnologia de blockchain. Esta dissertação tem como objetivo verificar se os novos modelos de machine-learning apresentam melhores resultados que os modelos tradicionais em previsões. Este estudo compara, em particular, a precisão da previsão do preço da Bitcoin usando dois modelos diferentes: Long-Short Term Memory (LSTM) versus Auto Regressive Integrated Moving Average (ARIMA), em termos de erros de previsão e aplicando rotinas do Python. A análise teve como base os preços diários da Bitcoin entre 18 de junho de 2016 e 7 de agosto de 2019, retirados da base de dados da Reserva Federal. Para comparar os resultados dos dois modelos, os dados foram divididos em duas secções: o treino (83.5%) e o teste (16.5%). A literatura indica que o modelo LSTM tem uma melhor precisão que o ARIMA e nesta dissertação os resultados confirmam que o modelo LSTM melhora em média 92% e 94% a previsão do ARIMA, de acordo com o RMSE e o MAE

HOW DO LARGE STAKES INFLUENCE BITCOIN PERFORMANCE? EVIDENCE FROM THE MT.GOX LIQUIDATION CASE

Bitcoin as the first and still most important decentralized cryptocurrency has gained wide popu-larity due to the steep rise of its price during the second half of 2017. Because of its digital na-ture, Bitcoin cannot be valuated exclusively with fundamental approaches, which is why factors such as investor sentiment have become a common alternative to capture its performance. In this work, we studied whether and how the sale of Bitcoins from the insolvency assets of Mt.Gox, which represent about 1.1% of the current global total, relates to Bitcoin price movements. We used social media sentiment analysis of Twitter data to examine how investors are influenced in their decision to buy or sell Bitcoin when confronted with the trade actions of Nobuaki Koba-yashi, the trustee in charge of the Mt.Gox case. We built a vector error correction model to ana-lyze the long-run relationship between cointegrated variables. Our analysis confirms the posi-tive association of Bitcoin performance with positive Twitter sentiment and tweet volume and the negative association with negative sentiment. We further found empirical evidence that Mt.Gox selloff events have a lasting negative impact on the Bitcoin price and that we can measure this effect by Twitter sentiment and tweet volume

Bitcoin and Volatility: Does the Media Play a Role?

The worlds first successful crypto currency (Bitcoin) has gained a lot of attention both positive and negative. The main issue keeping Bitcoin from being fully accepted by the public is its high volatility and unpredictability. This research provides an empirical analysis that offers insights into the factors that cause Bitcoin to maintain a high price volatility. The primary goal of the research is to determine whether or not the media plays a role on Bitcoin volatility. Our model uses ordinary least squares regression analysis to support the findings of previous research that generally uses GARCH models. The results show that Bitcoin volatility is primarily correlated with Google trends search data. Furthermore we find that negative news announcements have a significant positive correlation with Bitcoin volatility; whereas, economic health indicator variables yield insignificant results. Although our analysis suggest Bitcoin is an unsafe investment tool, we propose a number of future research possibilities that should enhance our understanding of crypto currencies so that they can eventually be utilized to their fullest potential

The Impact of News Media on Cryptocurrency Prices: Modelling Data Driven Discourses in the Crypto-Economy

Natural Language Processing was adopted in this study to model data driven discourses in the crypto economy. Utilising topic modelling, specifically Latent Dirichlet Allocation (LDA), a text analysis of cryptocurrency articles (N=4218) published from over 60 countries in international news media, identified key topics associated with cryptocurrency in the international news media from 2018 to 2020. This study provides empirical evidence that 18 key topics were framed around the following key macro discourses: crypto related crime, financial speculation and investment, financial governance and regulation, political economy (with reference to specific geographical areas), cryptocurrency actors and communities and specific crypto projects and their respective markets. Analysis showed that the identified cryptocurrency macro discourses may have had a ‘social signal’ effect on movements in the crypto financial markets, including potential effects of crypto price volatility. Further in some cases, that the source of the news may have amplified the effect, particularly in terms of geographical region, relative to broader market conditions