The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%
Bitcoin is a cryptocurrency and a decentralized semi-anonymous peer-to-peer payment system in which the transactions are verified by network nodes and recorded in a public massively replicated ledger called the blockchain. Bitcoin is currently considered as one of the most disruptive technologies. Bitcoin represents a paradox of opposing forces. On one hand, it is fundamentally social, allowing people to transact in a peer-to-peer manner to create and exchange value. On the other hand, Bitcoin's core design philosophy and user base contain strong anti-social elements and constraints, emphasizing anonymity, privacy, and subversion of traditional centralized financial systems. We believe that the success of Bitcoin, and the financial ecosystem built around it, will likely rely on achieving an optimal balance between these social and anti-social forces. To elucidate the role of these forces, we analyze the evolution of the entire Bitcoin transaction graph from its inception, and quantify the evolution of its key structural properties. We observe that despite its different nature, the Bitcoin transaction graph exhibits many universal dynamics typical of social networks. However, we also find that Bitcoin deviates in important ways due to anonymity-seeking behavioral patterns of its users. As a result, the network exhibits a two-orders-of-magnitude larger diameter, sparse treelike communities, and an overwhelming majority of transitional or intermediate accounts with incoming and outgoing edges but zero cumulative balances. These results illuminate the evolutionary dynamics of the most popular cryptocurrency, and provide us with initial understanding of social networks rooted in and driven by anti-social constraints.
Cryptocurrency such as Bitcoin is a rapidly developing phenomenon in financial technology with considerable research interest but is understudied. In this research article, we use a Design Science Research paradigm to create a high-frequency trading strategy at the minute level for Bitcoin using six exchanges as our Information Technology artifact. We created financial indicators and utilized a machine learning (ML) algorithm to create our strategy. We provided two sets of evaluation. First, we evaluated this strategy against another popular ML algorithm and found our algorithm performed better on the average. Second, we analyzed the economic benefits using the strategy against out-of-sample trading in foreign exchange currency. We presented both descriptive and prescriptive contributions to Design Science Research via the development and testing of our artifacts.
Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.
Abstract A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.
RODOLFO ANGELO MAGTANGGOL III DE GUZMAN, Mike K. P. So
This paper proposes the use of threshold heteroskedastic models which integrate threshold nonlinearity [Tong, H (1978). On a Threshold Model, pp. 575–586. Netherlands: Sijthoff & Noordhoff; Tong, H and KS Lim (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 3, 245–292.] and GARCH-type conditional variance for modeling Bitcoin returns to provide an understanding on the huge volatility that Bitcoin has been famous for. Specifically, the model attempts to identify different regimes throughout the history of Bitcoin using the different available Bitcoin network characteristics, such as cost per transaction, number of transactions per block, number of active addresses and number of transactions. Estimation and diagnostic checks are performed using Markov chain Monte Carlo methods. In the empirical analysis, we show that our model is able to identify periods of crashes as one of these regimes, which is also a period of declining returns and declining number of active users. We also find that the number of users and the number of transactions determine the magnitude or persistence of a crash period.
We introduce the distributed ledger (blockchain) technology of crypto‐currencies. We examine the ‘monetary’ attributes of crypto‐currencies, and describe some of the reasons they have been adopted. The paper discusses the mechanics of Bitcoin – the original crypto‐currency – to illustrate the fundamental elements of decentralized crypto‐currencies. We then provide a high‐level summary of the implications of crypto‐currencies for consumers, financial systems, and for monetary and regulatory authorities. We argue that crypto‐currencies are unlikely to supplant traditional fiat currencies and we anticipate an enduring role for financial intermediaries in facilitating credit.
The value of various Cryptocurrencies such as Bitcoin, Litecoin, Ethereum are always elusive. Hence, it would be a great value addition to investors if a model is able to predict what would be the nature of the crypto market for the next day. Through this paper, a time-series model using Long Short-Term Memory Networks is built to determine the value of cryptocurrency in the future. As a study, three cryptocurrencies – Bitcoin, Litecoin and Ethereum has been taken into consideration. A comparison of the results by using opinion mining to interpret the mood of the market on the current day for different currencies has been done. The sentiment scores got from natural language processing of textual data are used as features to the model used for predictions. The time-series charts are plotted using Plotly – python library for graphing plots. The Mean Absolute Error calculated between the actual and predicted values is used as the uncertainty quantification method. These uncertainty quantification methods are compared to analyze the present-day scenario of the market using opinion mining.
Analysis of any digital currency is performed for identifying and quantifying uncertainties, estimating their impact on results with real-time market value. Cryptocurrency, as an encrypted form of currencies which is used for shopping, investment, money transfer and in other purpose now days. Most popular use of Bitcoins are investment because its price was unexpectedly high in past few years (data shown in content of paper). In this paper prediction of Bitcoin close price by using the ARIMA model has been performed. The ARIMA model is found suitable for the prediction of bitcoin prices because this model is used for prediction of time series data. The forecast of future values is provided based on seasonality and trend present in the price data. In terms of visualizations, results are manifest by using R programming language. The obtained results are then compared with actual prices and percent mean error is calculated. The present mean error is found here less than 6% for most of the values.
Fatih Ceylan, Ramazan Eki̇nci̇, Osman Tüzün, Hakan Kahyaoğlu
Cryptocurrencies, especially Bitcoin, have been used very often recently. The need for analyzing the price movements of the cryptocurrencies, which are accepted as “a currency” and “a store of value”, has emerged. With the growth and global integration of these markets, whether there are speculative bubbles on the basis of significant changes in prices is important in terms of openness and security in respect of financial stability. In addition, speculative movements in the cryptocurrencies market raise the question of whether market participants act with herd mentality. For this reason, in the study, the presence of speculative bubbles in Bitcoin and Etherium is analyzed by using Philips et al. (2015) method and estimated when they were formed. While the presence of bubbles in the cryptocurrencies market and the existence of these bubbles as a duration of herd mentality, it is also known that there is no balancing speculation in this market. According to the findings, a large number of bubbles were found in the Bitcoin and Etherium cryptocurrencies. The emergence of large bubbles, especially between the years 2017-2018, has shown that these cryptocurrencies are prone to speculative movements.
Abstract This article analyses ongoing changes in the supply of alternative media of payments (MOPs). The comparison between old (cash and deposits) and new (cryptocurrencies and central bank digital currencies) MOPs is based on a novel definition of money where a MOP has three properties: the first two are the standard functions of medium of exchange (liquidity costs) and store of value (opportunity costs) and the third is the novel function of store of information (privacy costs). Given such properties and that the evolution of the different MOPs likely depends on individual preferences, the relevance of experimental economics is highlighted.
Pump-and-dump schemes are fraudulent price manipulations through the spread of misinformation and have been around in economic settings since at least the 1700s. With new technologies around cryptocurrency trading, the problem has intensified to a shorter time scale and broader scope. The scientific literature on cryptocurrency pump-and-dump schemes is scarce, and government regulation has not yet caught up, leaving cryptocurrencies particularly vulnerable to this type of market manipulation. This paper examines existing information on pump-and-dump schemes from classical economic literature, synthesises this with cryptocurrencies, and proposes criteria that can be used to define a cryptocurrency pump-and-dump. These pump-and-dump patterns exhibit anomalous behaviour; thus, techniques from anomaly detection research are utilised to locate points of anomalous trading activity in order to flag potential pump-and-dump activity. The findings suggest that there are some signals in the trading data that might help detect pump-and-dump schemes, and we demonstrate these in our detection system by examining several real-world cases. Moreover, we found that fraudulent activity clusters on specific cryptocurrency exchanges and coins. The approach, data, and findings of this paper might form a basis for further research into this emerging fraud problem and could ultimately inform crime prevention.
While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.
This paper examines the day of the week effect in the cryptocurrency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal–Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.
This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.
Boris Radovanov, Aleksandra Marcikić, Nebojša Gvozdenović
Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.