Fundamental and speculative components of the cryptocurrency pricing dynamics

The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.

Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

Store of value or speculative investment?Market reaction to corporate announcements of cryptocurrency acquisition

In this study,we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions,selling,or acceptance as a means of payment.Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency.We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows.However,when we perform heterogeneity analyses,we observe significant differences in market reaction between events with high(larger CARs)and low cryptocurrency exposure(lower CARs).Multivariate regressions show that the level of exposure to cryptocurrency("skin in the game")is a critical factor underlying abnormal returns around the event.Further analyses reveal that economically meaningful acquisitions of BTC or ETH(relative to firm’s total assets)drive the observed effect.Our findings have important implications for managers,investors,and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

Cryptocurrency相关新词及其翻译

cryptocurrency(加密货币)的流行催生了一大批相关的新词。文章以新词研究实践为基础,不仅探讨了几个关键词的缘起,而且从构词法角度分析了这些新词的具体构成方式,同时又讨论了翻译这些新词应采用的手段。

Cryptocurrency trading:a comprehensive survey

In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has accelerated.Cryptocurrencies are the first pure digital assets to be included by asset managers.Although they have some commonalities with more traditional assets,they have their own separate nature and their behaviour as an asset is still in the process of being understood.It is therefore important to summarise existing research papers and results on cryptocurrency trading,including available trading platforms,trading signals,trading strategy research and risk management.This paper provides a comprehensive survey of cryptocurrency trading research,by covering 146 research papers on various aspects of cryptocurrency trading(e.g.,cryptocurrency trading systems,bubble and extreme condition,prediction of volatility and return,crypto-assets portfolio construction and crypto-assets,technical trading and others).This paper also analyses datasets,research trends and distribution among research objects(contents/properties)and technologies,concluding with some promising opportunities that remain open in cryptocurrency trading.

Forecasting cryptocurrency returns and volume using search engines

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting,this brief study analyzes the predictability of Bitcoin volume and returns using Google search values.We employed a rich set of established empirical approaches,including a VAR framework,a copulas approach,and non-parametric drawings,to capture a dependence structure.Using a weekly dataset from 2013 to 2017,our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.Shocks to search values have a positive effect,which persisted for at least a week.Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics,which are of special interest to investors and economic policymakers.

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Since the emergence of Bitcoin,cryptocurrencies have grown significantly,not only in terms of capitalization but also in number.Consequently,the cryptocurrency market can be a conducive arena for investors,as it offers many opportunities.However,it is difficult to understand.This study aims to describe,summarize,and segment the main trends of the entire cryptocurrency market in 2018,using data analysis tools.Accord-ingly,we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies,and one that looks for associations between the clustering results,and other factors that are not involved in clustering.Particularly,the methodology involves applying three different partitional clustering algorithms,where each of them use a different representation for cryptocurrencies,namely,yearly mean,and standard deviation of the returns,distribution of returns that have not been applied to financial markets previously,and the time series of returns.Because each representation provides a different outlook of the market,we also examine the integration of the three clustering results,to obtain a fine-grained analysis of the main trends of the market.In conclusion,we analyze the association of the clustering results with other descriptive features of cryptocurrencies,including the age,technological attributes,and financial ratios derived from them.This will help to enhance the profiling of the clusters with additional descriptive insights,and to find associations with other variables.Consequently,this study describes the whole market based on graphical information,and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly,and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period,we found that the market can be typically segmented in few clusters(five or less),and even considering the intersections,the 6 more populations account for 75%of the market.Regarding the associations between the clusters and descriptive features,we find associations between some clusters with volume,market capitalization,and some financial ratios,which could be explored in future research.

Impact of COVID-19 effective reproductive rate on cryptocurrency

The importance of cryptocurrency to the global economy is increasing steadily,which is evidenced by a total market capitalization of over$2.18T as of December 17,2021,according to coinmarketcap.com(Coin,2021).Cryptocurrencies are too confusing for laymen and require more investigation.In this study,we analyze the impact that the effective reproductive rate,an epidemiological indicator of the spread of COVID-19,has on both the price and trading volume of eight of the largest digital currencies—Bitcoin,Ethereum,Tether,Ripple,Litecoin,Bitcoin Cash,Cardano,and Binance.We hypothesize that as the rate of spread decreases,the trading price of the digital currency increases.Using Generalized Autoregressive Conditional Heteroskedasticity models,we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region.These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.

Are suspicious activity reporting requirements for cryptocurrency exchanges effective?

This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering(ML)increases the ratio of reported transactions to unreported transactions.If a representative money launderer makes an optimal portfolio choice,then this ratio increases further.Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks.We attribute this result to cryptocurrency exchanges’inferior ML detection abilities and their proximity to the underground economy.

Liquidity connectedness in cryptocurrency market

We examine the dynamics of liquidity connectedness in the cryptocurrency market.We use the connectedness models of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012)and Baruník and Křehlík(J Financ Econom 16(2):271–296,2018)on a sample of six major cryptocurrencies,namely,Bitcoin(BTC),Litecoin(LTC),Ethereum(ETH),Ripple(XRP),Monero(XMR),and Dash.Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies,whereas BTC and LTC play a significant role in connectedness magnitude.A distinct liquidity cluster is observed for BTC,LTC,and XRP,and ETH,XMR,and Dash also form another distinct liquidity cluster.The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium-and long-run time horizons.In the short run,BTC,LTC,and XRP are the leading contributor to liquidity shocks,whereas,in the long run,ETH assumes this role.Compared with the medium term,a tight liquidity clustering is found in the short and long terms.The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time,pointing to the possible effect of rising demand and higher acceptability for this unique asset.Furthermore,more pronounced liquidity connectedness patterns are observed over the short and long run,reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

A Novel Cryptocurrency Prediction Method Using Optimum CNN

In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media.Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels.Therefore,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet sentiments.This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and Ethereum.The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

Lottery‑like preferences and the MAX effect in the cryptocurrency market

We investigate the significance of extreme positive returns in the cross-sectional pricing of cryptocurrencies.Through portfolio-level analyses and weekly cross-sectional regressions on all cryptocurrencies in our sample period,we provide evidence for a positive and statistically significant relationship between the maximum daily return within the previous month(MAX)and the expected returns on cryptocurrencies.In particular,the univariate portfolio analysis shows that weekly average raw and riskadjusted return differences between portfolios of cryptocurrencies with the highest and lowest MAX deciles are 3.03%and 1.99%,respectively.The results are robust with respect to the differences in size,price,momentum,short-term reversal,liquidity,volatility,skewness,and investor sentiment.

An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions

There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global operation.The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption.Popular energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and Thailand.The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and Oman.This review compiles estimates made by various studies from 2018 to 2021.We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa.We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints.Finally,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.

An analysis of the acquisition of a monetary function by cryptocurrency using a multi-agent simulation model

Many types of cryptocurrencies,which predominantly utilize blockchain technology,have emerged worldwide.Several issuers plan to circulate their original cryptocurrencies for monetary use.This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function.We use a multi-agent model,referred to as the Yasutomi model,which simulates the emergence of money.We analyze two scenarios that may result from the actions taken by the issuer.These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop.We find that a cryptocurrency can attain a monetary function in two cases.One such case occurs when 20%of all agents accept the cryptocurrency for payment and 50%of the agents are aware of this fact.The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.

Tail spillover effects between cryptocurrencies and uncertainty in the gold,oil,and stock markets

This study investigates tail dependence among five major cryptocurrencies,namely Bitcoin,Ethereum,Litecoin,Ripple,and Bitcoin Cash,and uncertainties in the gold,oil,and equity markets.Using the cross-quantilogram method and quantile connectedness approach,we identify cross-quantile interdependence between the analyzed variables.Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles,implying that diversification benefits for these assets may differ widely across normal and extreme market conditions.Under normal market conditions,the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions.Moreover,we show that under all market conditions,cryptocurrencies have a leadership influence over the volatility indices.Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges,as we show that cryptocurrency and volatility markets are insignificantly(weakly)connected under normal(extreme)market conditions.

Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.

基于样本嵌入的挖矿恶意软件检测方法

加密货币挖矿恶意软件的高盈利性和匿名性,对计算机用户造成了巨大威胁和损失。为了对抗挖矿恶意软件带来的威胁,基于软件静态特征的机器学习检测器通常选取单一类型的静态特征,或者通过集成学习来融合不同种类静态特征的检测结果,忽略了不同种类静态特征之间的内在联系,其检测率有待提升。文章从挖矿恶意软件的内在层级联系出发,自下而上提取样本的基本块、控制流程图和函数调用图作为静态特征,训练三层模型以将这些特征分别嵌入向量化,并逐渐汇集从底层到高层的特征,最终输入分类器实现对挖矿恶意软件的检测。为了模拟真实环境中的检测情形,先在一个小的实验数据集上训练模型,再在另一个更大的数据集上测试模型的性能。实验结果表明,三层嵌入模型在挖矿恶意软件检测上的性能领先于近年提出的机器学习模型,在召回率和准确率上相比其他模型分别提高了7%和3%以上。