The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.
Antônio Carlos da Silva Filho, Natália Diniz Maganini, Eduardo Fonseca de Almeida
The recent emergence and use growth of cryptocurrencies based on Blockchain technology increased interest in the study of its economic dynamics and financial characteristics. Bitcoin is up to now the more widely known and disseminated cryptocurrency, with greater volume of transactions, market value and acceptance in exchange services. In order to contribute to the comprehension of the price behavior of the Bitcoin market, this study analyzes whether the historical series of prices of this currency, quoted every 12 h from September 14, 2011 to November 20, 2017 has multifractal behavior. The results of the research identified multifractal characteristics in the series and that both long-range correlations and fat tails distribution contribute to Bitcoin’s multifractal behavior. We compared the non-Gaussian properties and the multifractality degrees of Bitcoin series with the non-Gaussian properties and multifractality degrees of several stock market indices scattered around the world. In addition, we investigated the power of multifractal analysis in the study of volatility and forecast for this series, pointing to a possible use of multifractal parameters in Technical Analysis.
Price stability has often been cited as a key reason that cryptocurrencies\nhave not gained widespread adoption as a medium of exchange and continue to\nprove incapable of powering the economy of decentralized applications (DApps)\nefficiently. Exeum proposes a novel method to provide price stable digital\ntokens whose values are pegged to real world assets, serving as a bridge\nbetween the real world and the decentralized economy.\n Pegged tokens issued by Exeum - for example, USDE refers to a stable token\nissued by the system whose value is pegged to USD - are backed by virtual\nassets in a virtual asset exchange where users can deposit the base token of\nthe system and take long or short positions. Guaranteeing the stability of the\npegged tokens boils down to the problem of maintaining the peg of the virtual\nassets to real world assets, and the main mechanism used by Exeum is\ncontrolling the swap rate of assets. If the swap rate is fully controlled by\nthe system, arbitrageurs can be incentivized enough to restore a broken peg;\nExeum distributes statistical arbitrage trading software to decentralize this\ntype of market making activity. The last major component of the system is a\ncentral bank equivalent that determines the long term interest rate of the base\ntoken, pays interest on the deposit by inflating the supply if necessary, and\nremoves the need for stability fees on pegged tokens, improving their\nusability.\n To the best of our knowledge, Exeum is the first to propose a truly\ndecentralized method for developing a stablecoin that enables 1:1 value\nconversion between the base token and pegged assets, completely removing the\nmismatch between supply and demand. In this paper, we will also discuss its\napplications, such as improving staking based DApp token models, price stable\ngas fees, pegging to an index of DApp tokens, and performing cross-chain asset\ntransfer of legacy crypto assets.\n
We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log–returns using Markov–switching GARCH (MSGARCH) models. We also compare MSGARCH to traditional single–regime GARCH specifications in predicting one–day ahead Value–at–Risk (VaR). The Bayesian approach is used to estimate the model parameters and to compute the VaR forecasts. We find strong evidence of regime changes in the GARCH process and show that MSGARCH models outperform single–regime specifications when predicting the VaR.
Alexandra Piedad Cortez Ordoñez, Ana Belén Tulcanaza-Prieto
The new technological advances have brought a revolution on how economic agents interact with society and markets. Nowadays, the use of virtual currencies is more frequent in the financial transactions and bitcoin has been defined as the most important world cryptocurrency due to its high market capitalization and its technological infrastructure. Several studies have been conducted to discuss bitcoin advantages and disadvantages; however, few papers in literature have examined its connection and influence on the stock market. The objective of this paper is precisely cover this gap. Firstly, by providing tools and concepts to understand bitcoin’s dynamic, and then determining its relationship with stock market indexes. In that context, this manuscript examines the definition and function of bitcoin in the global world and its presence in Ecuador. Besides, exploratory and visual analyses are provided using the evolution of bitcoin and other market indexes. Finally, a linear correlation is computed between bitcoin, other cryptocurrencies, stock exchange indexes and commodities. The results in this study, employing visual and statistical analyses, demonstrated that bitcoin has: a strong relationship with other cryptocurrencies; a lineal correlation, not as strong as the previous one, with the main stock market indexes; and no linear correlation with commodities.
The recent increase in research on financial technology has resulted in transactions receiving considerable research attention. With the increasing importance of bitcoin, many related topics require further clarification. Because bitcoin is a popular financial asset, determining whether the price information is fully circulated among trading platforms has been the focus of many studies in recent years. The method for verifying the efficient market hypothesis relates to whether the price series is a random walk; that is, whether a unit root exists. According to the literature, whether the price of bitcoin satisfies the efficient market hypothesis remains controversial; however, these studies have not considered nonlinear data structures. To deal with the structural change of data, this study employed different unit root tests, namely the Zivot-Andrews unit root test and Kapetanios-Shin unit root test, to investigate bitcoins' relationship to the efficient market hypothesis. If the efficient market hypothesis is validated, the price formed by the bitcoin trading platform is close to a perfectly competitive mechanism, and the information can be quickly and completely reflected in the price, with information of different trading platforms having mutual influence. Therefore, this study applied the threshold vector autoregressive model to explore price information transmission between various trading platforms. The contribution of this research is its exploration of the financial tools derived from the highly developed fields of financial science and technology. This is helpful for providing new methods for completing financial transactions or a platform for speculators to engage in arbitrage.
Pavitra Mohanty, Darshan Patel, Parth Patel, Sudipta Roy
This paper shows the prediction of fluctuation in the future price of cryptocurrencies. Users' comments and tweets from twitter using Apache Flume and Price data was fetched from exchanges. Bitcoin first documented by allies Satoshi Nakamoto, the first decentralized currency payment system has gained a considerable attention in the financial system, economics, social media and computer science due to its combination of peer-to-peer nature, encryption technology, and monetary unit. Predicting the price of Bitcoin and other cryptocurrencies is a great challenge because it is immensely complex and dynamic in nature. In this paper, we have tried to predict the future price of cryptocurrencies like Bitcoin using LSTM (Long Short-Term Memory) and used Twitter data to predict public mood. By combining both market sentiment and social sentiment because bitcoin price shows mixed properties. We also have selected some other important features from the blockchain information which has a major impact on Bitcoin's supply and demand and using them to train model that improves the predictive power of the future Bitcoin price. We have performed a deep study of how data from social media affect the price of Bitcoin and so we have included the twitter data in model training. Our model shows that how well LSTM predict the price of Bitcoin considering the high volatility. The precision given by our model is 60% and accuracy is 50%. More focus is not given to accuracy, in this case, considering the highly volatile market.
Arti Jain, Shashank Tripathi, Harsh Dhar Dwivedi, Pranav Saxena
The problem is to find a method to predict the two-hour price of cryptocurrencies on the basis of the Social Factors, which are increasingly used for online transactions worldwide. The few previous methods proposed to predict price of cryptocurrency are inefficient because they fail to take into consideration the differences in the attributes between real currencies and cryptocurrencies. In this paper, we focus on two cryptocurrencies, namely Bitcoin and Litecoin, each with a large market size and user base, and attempt to predict their future prices using multi-linear regression model.
Francesco Bortolussi, Zeger Hoogeboom, Frank W. Takes
Cryptocurrencies such as Bitcoin and Ethereum have recently gained a lot of popularity, not only as a digital form of currency but also as an investment vehicle. Online marketplaces and exchanges allow users across the world to convert between dozens of different cryptocurrencies and regular currencies such as euros or dollars. Due to the novelty of this concept, the volatility of these markets and the differences in maturity and usage of particular marketplaces, currency pairs may appear at multiple marketplaces but at different trading prices. This paper proposes a novel algorithmic approach to take advantage of these mispricings and capitalize upon the pricing differences that exist between exchanges and currency pairs. To do so, we model each combination of a currency and a market as one node in a graph. A directed link between two nodes indicates that a conversion between these two currency/market pairs is possible. The weight of the link relates to the exchange rate of executing this particular currency exchange. To leverage the mispricings, we seek for cycles in the graph such that upon multiplying the weights of the links in the cycle, a value greater than 1 is found and thus a profit can be made. Our goal is to do this efficiently, without exhaustively enumerating all possible cycles in the graph. Therefore, we convert our data and address the problem in terms of finding minimum weight triangles in graphs with integer weights, for which efficient algorithms can be utilized. We experiment with parameter settings (heuristics) related to the conversion of exchange rate data into integer weight values. We show that our approach improves upon a reasonable baseline algorithm in terms of computation time. Furthermore, using a real-world dataset, we demonstrate how the obtained minimal weight cycles indeed unveil a number of currency exchange cycles that result in a net profit.
This paper deals with the design of the secure blockchain network framework to prevent damages from an attacker. The decentralized network design called the Blockchain Governance Game is a new hybrid theoretical model and it provides the stochastic game framework to find best strategies towards preparation for preventing a network malfunction by an attacker. Analytically tractable results are obtained by using the fluctuation theory and the mixed strategy game theory. These results enable to predict the moment for operations and deliver the optimal portion of backup nodes to protect the blockchain network. This research helps for whom considers the initial coin offering or launching new blockchain based services with enhancing the security features.
The recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, making investors and financial regulators aware of the importance of modeling them correctly. The Lvy's stable distribution is one of the attractive distributions that well describes the fat tails and scaling phenomena in economic systems. In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized by a non-Gaussian Lvy's stable distribution with ' 1:4 under certain conditions on time intervals ranging roughly from 30 min to 4 h. Our arguments are developed under quantitative valuation defined as a distance function using the Parseval's relation in addition to the theoretical background of the General Central Limit Theorem (GCLT). We also discuss the model-fitting for returns by employing the method based on likelihood ratios. Even though the cubic power-law model is a better fitting model than the Lvy's stable model in the tail part of returns, the Lvy's stable model outperforms the fit for the entire and wider range of returns. Our approach can be extended for further analysis of statistical properties and contribute to developing proper applications for financial modeling.
Following Urquhart (2017) who finds evidence of price clustering in Bitcoin, we answer the question of whether the documented price clustering in Bitcoin is driven by any given day-of-the-week. We find evidence that Bitcoin prices cluster around whole numbers more on Fridays and least on Mondays. We also show that Bitcoin price clustering around the top three most frequent two-digit decimals is primarily a Friday phenomenon.
This letter questions the true nature (true versus spurious) of the Long Range Dependence (LRD) behavior observed in the returns and volatility series of four Cryptocurrencies (CC). Using a robust approach, this letter shows that the LRD behavior exhibited by the returns and volatility series of Bitcoin, Litecoin, and Ripple is a true behavior, and not a statistical artifact. As for Ethereum, the results show that the true LRD is only supported for the volatility series. Our results confirm the inefficiency of all the considered markets, with the exception of Ethereum.
The study will be the first to offer empirical justification for time-varying stochastic volatility in Bitcoin returns. Specifically, it tests for time variation in both the trend and transitory components of the stochastic volatility using the unobserved components model that accounts for same. Thereafter, it calculates the Bayes factor using the approach of Chan (2018) which involves the Savage-Dickey density ratio in order to avoid the computation of the marginal likelihood. The results overwhelmingly support at least one time-varying stochastic volatility component in Bitcoin returns and the transitory component is favoured in this regard. These results are robust to different data frequencies.
This paper explores Bitcoin intraday technical trading based on artificial neural networks for the return prediction. In particular, our deep learning method successfully discovers trading signals through a seven layered neural network structure for given input data of technical indicators, which are calculated by the past time-series data over every 15 minutes. Under feasible settings of execution costs, the numerical experiments demonstrate that our approach significantly improves the performance of a buy-and-hold strategy. Especially, our model performs well for a challenging period from December 2017 to January 2018, during which Bitcoin suffers from substantial minus returns. Furthermore, various sensitivity analysis is implemented for the change of the number of layers, activation functions, input data and output classification to confirm the robustness of our approach.