Follow blockchain research across journals, conferences, and preprint repositories.
3,636 results · page 108 of 152
Soon Hyeok Choi, Robert A. Jarrow
No abstract is available for this record.
Man-Ching Yuen, Ka-Ming Lau, Ka-Fai Ng
Nowadays, cryptocurrency's transmission volume increases continuously and rapidly. Bitcoin is the first and the most popular cryptocurrency. One of the ways to obtain Bitcoins is coin mining. Bitcoin mining is a mechanism, in which miner expend resources in a computation process to synchronize Bitcoin transactions for collecting Bitcoins as rewards. Currently, there are a number of crypto-mining marketplaces, in which allows selling or buying computing power on demand. In this paper, we focus on NiceHash platform, which is the largest crypto-mining marketplace having a large number of active miners. In NiceHash platform, sellers (also called, miners) mine coins to fulfill the selected buyers' orders. NiceHash offers a software which can automatically select the most profitable algorithm for miners, so miners do not need to monitor the market and their multiple wallets. However, the existing software usually select an algorithm for a miner to mine without any further update for a long time even the profit gained by using a mining algorithm is highly fluctuated. Moreover, many miners experienced lots of disconnects on the NiceHash pool. To address the above problems, we proposed a system which can maximize the Bitcoin profit of miners by automatically selecting the most profitable mining algorithms and keeping track on the connection between miners and the mining pool. Experiences show that our automated system can effectively maximize the mining profits by increasing 21% when compared with the original algorithm used in NiceHash.
Hamed Ghoddusi, Mohammad Morovati, Nima Rafizadeh
No abstract is available for this record.
Q. K. N. Chan, Wenzhi Ding, Chen Lin, Alberto G. Rossi
No abstract is available for this record.
Vladimir Soloviev, Oleksandr SERDIUK
The possibility of constructing dynamic measures of complexity as quantum econophysical behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicatorsprecursors of these falls has been identified. From positions, attained by modern theoretical physics the concept of economic Plank's constant has been proposed.
Anantha Divakaruni, Peter Zimmerman
We show that recent technological innovations have significantly improved the efficiency of Bitcoin as a means of payment. We study three particular innovations: the Lightning Network, a means of netting payments off the blockchain; SegWit, an improvement to the way data are stored on the blockchain; and Bitcoin Cash, a new cryptocurrency forked from Bitcoin. We find a robust and significant association between adoption of the Lightning Network and reduced blockchain congestion. This improvement cannot be explained by other factors, such as changes in speculative demand for Bitcoin. We show that the Lightning Network has become increasingly centralised, with payments channelled through relatively few intermediaries. Finally, we argue that improved functioning of Bitcoin is positive for welfare, and may reduce the environmental footprint of Bitcoin mining.
Barbara Będowska-Sójka, Tomasz Hinc, Agata Kliber
No abstract is available for this record.
Ana Fernández Vilas, Rebeca P. Dı́az Redondo, Anton Lorenzo Garcia
There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manipulation. Although Twitter hashtags allow to aggregate topic-related content, a specific mechanism for financial information also exists: Cashtag (consisting of the company ticker preceded by $) is a supporting mechanism to track financial tweets referring to a company listed in a stock market. However, according to our experiments and due to the lack of conventions in cashtags usage, the irruption of cryptocurrencies has resulted in a significant degradation on the cashtag-based aggregation of posts. Unfortunately, Twitter' users may use homonym tickers to refer to cryptocurrencies and to companies in stock markets, which means that filtering by cashtag may result on both posts referring to stock companies and cryptocurrencies. This research proposes automated classifiers to distinguish conflicting cashtags and, so, their container tweets by analyzing the distinctive features of tweets referring to stock companies and cryptocurrencies. As experiment, this paper analyses the interference between cryptocurrencies and company tickers in the London Stock Exchange (LSE), specifically, companies in the main and alternative market indices FTSE-100 and AIM-100. Heuristic-based as well as supervised classifiers are proposed and their advantages and drawbacks, including their ability to self-adapt to Twitter usage changes, are discussed. The experiment confirms a significant distortion in collected data when colliding or homonym cashtags exist, i.e., the same $ acronym to refer to company tickers and cryptocurrencies. According to our results, the distinctive features of posts including cryptocurrencies or company tickers support accurate classification of colliding tweets (homonym cashtags) and Independent Models, as the most detached classifiers from training data, have the potential to be trans-applicability (in different stock markets) while retaining performance.
Toshiko Matsui, Lewis Gudgeon
No abstract is available for this record.
Tobias A. Huber, Didier Sornette
Bitcoin represents one of the most interesting technological breakthroughs and socio-economic experiments of the last decades. In this paper, we examine the role of speculative bubbles in the process of Bitcoin's technological adoption by analyzing its social dynamics. We trace Bitcoin's genesis and dissect the nature of its techno-economic innovation. In particular, we present an analysis of the techno-economic feedback loops that drive Bitcoin's price and network effects. Based on our analysis of Bitcoin, we test and further refine the Social Bubble Hypothesis, which holds that bubbles constitute an essential component in the process of technological innovation. We argue that a hierarchy of repeating and exponentially increasing series of bubbles and hype cycles, which has occurred over the past decade since its inception, has bootstrapped Bitcoin into existence.
Aurelio F. Bariviera, Ignasi Merediz‐Solà
This survey develops a dual analysis, consisting, first, in a bibliometric examination and, second, in a close literature review of all the scientific production around cryptocurrencies conducted in economics so far. The aim of this paper is twofold. On the one hand, proposes a methodological hybrid approach to perform comprehensive literature reviews. On the other hand, we provide an updated state of the art in cryptocurrency economic literature. Our methodology emerges as relevant when the topic comprises a large number of papers, that make unrealistic to perform a detailed reading of all the papers. This dual perspective offers a full landscape of cryptocurrency economic research. Firstly, by means of the distant reading provided by machine learning bibliometric techniques, we are able to identify main topics, journals, key authors, and other macro aggregates. Secondly, based on the information provided by the previous stage, the traditional literature review provides a closer look at methodologies, data sources and other details of the papers. In this way, we offer a classification and analysis of the mounting research produced in a relative short time span.
Pierre Venter, Eben Maré, Edson Pindza
In this paper, two univariate generalised autoregressive conditional heteroskedasticity (GARCH) option pricing models are applied to Bitcoin and the Cryptocurrency Index (CRIX). The first model is symmetric and the other takes asymmetric effects into account. Furthermore, the accuracy of the GARCH option pricing model applied to Bitcoin is tested. Empirical results indicate that asymmetry is not an important factor to consider when pricing options on Bitcoin or CRIX, this is consistent with findings in the literature. In addition, the GARCH option pricing model provides realistic price discovery within the bid-ask spreads suggested by the market.
David Kuo Chuen Lee, Ernie G. S. Teo
No abstract is available for this record.
Rebecca Abraham
Bitcoin is the currency of the blockchain, which promises cost reductions for businesses. This paper develops models to value bitcoin, bitcoin futures, and bitcoin options. It provides the theoretical basis for bitcoin pricing. Optimal bitcoin prices are derived at the intersection of an aberrancy utility function, a hyperbolic cosine utility function, and a Bessel utility function with price distributions. Rational investors value bitcoin on the basis of blockchain applications, while irrational investors' value bitcoin based on personal recommendations.
Thomas Dimpfl, Kai Mäckle
No abstract is available for this record.
Yoshi Fujiwara, Rubaiyat Islam
No abstract is available for this record.
Romina Torres, Miguel A. Solís, Rodrigo Salas, Aurelio F. Bariviera
Cryptocurrencies have been receiving the sustained attention of investors since 2009. These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms. Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by developing algorithm-based trading strategies. Such strategies aim to beat the market. It has been previously reported that daily returns do not exhibit long range dependence. However, daily volatility in major cryptocurrencies is highly persistent. Therefore, buy/hold/sell decision support systems could be able to capture such market inefficiency. This is especially important for investors interested in periodically trading a set of cryptocurrencies, in order to maximize their wealth. This paper presents a dynamic linguistic decision making approach for building decision models to support cryptocurrency investors in buy/hold/sell decisions. This approach exhibits a good computational performance for obtaining recommendations based on quantitative data. Moreover, this procedure is able to identify some inefficient cryptocurrency behaviors which are not captured by traditional econometric techniques. Our results uncover arbitrage opportunities that outperform buy-and-hold or random strategies.
Xinwen Ni, Taojun Xie, Wolfgang Karl Härdle, Xiaorui Zuo
Abstract Cryptocurrency markets are highly sensitive to regulatory changes, often experiencing sharp price fluctuations in response to new policies and government interventions. Despite this, existing market indices fail to adequately capture the risks associated with regulatory uncertainty. In this paper, we introduce the Cryptocurrency Regulatory Risk Index (CRRIX), a machine learning-based index designed to quantify the impact of regulatory developments on cryptocurrency markets. Our methodology employs Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms, providing an objective measure of regulatory risk. We find that the CRRIX exhibits strong synchronicity with VCRIX, a cryptocurrency volatility index, suggesting that regulatory uncertainty plays a significant role in driving market fluctuations. Our results indicate that regulatory risk is a leading factor in market volatility, with major policy shifts triggering significant market movements. The proposed regulatory risk index provides a novel approach to quantifying policy uncertainty in the cryptocurrency sector, offering valuable insights for market participants navigating this rapidly changing environment.
Roman Matkovskyy, Akanksha Jalan
No abstract is available for this record.
Vladimir Soloviev, Сергій Олексійович Семеріков, Victoria Solovieva
The informational (Kolmogorov) measure of complexity in accordance with the Lempel-Ziv algorithm (LZC) is calculated for the logarithmic returns of daily Bitcoin/$ values. The calculations were carried out for a moving window with a variation in its size (50–250 days) in increments of one day in the framework of the implemented coarse graining procedure. It is shown that in both mono-and multi-scaling versions, LZC is sensitive to noticeable fluctuations in the Bitcoin price that occur as a result of critical events in the cryptocurrency market. In equilibrium, stable state, having a relatively low value, LZC rapidly increases immediately before the crisis, which proves the dominance of the chaotic component of the time series. The classification and periodization of crisis phenomena in the cryptocurrency market for the period 2010–2020 has been carried out. The results demonstrate the possibility of using the LZC measure as an indicator-precursor of crisis phenomena in the cryptocurrency market.
Yoontae Jeon, Laleh Samarbakhsh, Kenji Hewitt
No abstract is available for this record.
Daniele Bianchi, Massimo Guidolin, Manuela Pedio
In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coeficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.
Devesh Chandra, Pranav Tyagi, Radhe Shyam Gupta, Aayush Mohan Saxena · 5 authors
The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.
Junhuan Zhang, Yuqian Xu, Daniel Houser
Cryptocurrencies including Bitcoin are known to be vulnerable to so-called ‘double-spending’ attacks, where the same digital currency is used to execute multiple different transactions simultaneously. Little is known, however, about the underlying reasons for this vulnerability. Here we develop an agent-based model to study how features of cryptocurrency networks contribute to their vulnerability to double-spending attacks. Perhaps surprisingly, we find neither the number of network nodes nor its path length seem to influence the probability of successful attacks. We find robust evidence that the network's clustering coefficient has substantial influence. In particular, scale-free networks, with their small clustering coefficients, are more than twice as likely to succumb to double-spending attacks than are networks with larger coefficients, such as regular networks. The implication is that cryptocurrency networks, which are scale-free, may be uniquely susceptible to double-spending attacks.