A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Implied volatility estimation of bitcoin options and the stylized facts of option pricing

The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option insurance.These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively.The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options markets.These stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial markets.These are expected to be true for Bitcoin options as well.The data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative exchange.The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis.This study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection methods.The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data.Moreover,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options.However,the Newton Raphson forecasting technique converges faster than does the Bisection method.

A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen

As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert communication.However,its combination with covert communication thus far has been mostly at the theoretical level.The BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high costs.The present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information.This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used.Under the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency.Thus,a more efficient and feasible method for the application of covert communication on the blockchain is proposed.In addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.

Bitcoin市场的长记忆性的实证分析

Bitcoin是一种新型的虚拟电子货币。运用初步的统计方法和R/S分析法分析其市场交易数据,对其长记忆性进行实证分析。分析结果表明,现阶段,Bitcoin的交易市场具有聚集性和持续性,当前的价格对于未来很长一段时间都有影响。

Analysis on the influence factors of Bitcoin’s price based on VEC model

Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,achieved decentralization,anonymous and transparent.As the most representative digital currency,people curious to study how Bitcoin’price changes in the past.Methods:In this paper,we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index,US dollar index,Dow jones industry average,Federal Funds Rate and gold price influence Bitcoin price.Result:From empirical analysis we find that all these variables do have a long-term influence.US dollar index is the biggest influence on Bitcoin price while gold price influence the least.Conclusion:From our result,we conclude that for now Bitcoin can be treated as a speculative asset,however,it is far from being a proper credit currency.

The effect of propagation delay on the dynamic evolution of the Bitcoin blockchain

This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I.Eyal and E.G.Sirer.This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable.It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network.Considering that the propagation delay of information between any two miners in the network,which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners,and a constant variance independent of others'delays,we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.

Dynamic spillovers between the term structure of interest rates,bitcoin,and safe‑haven currencies

This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.

Predicting changes in Bitcoin price using grey system theory

Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.

Portfolio diversification benefits of alternative currency investment in Bitcoin and foreign exchange markets

This study examines the portfolio diversification benefits of alternative currency trading in Bitcoin and foreign exchange markets.The following methods are applied for the analysis:the spillover index method of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012.https://doi.org/10.1016/j.ijfor ecast.2011.02.006),the spillover asymmetry measures of Barunik et al.(J Int Money Finance 77:39–56,2017.https://doi.org/10.1016/j.jimon fin.2017.06.003),and the frequency connectedness method of Barunik and Křehlik(J Financ Econom 16(2):271–296,2018.https://doi.org/10.1093/jjfin ec/nby001).The findings identify the presence of low-level integration and asymmetric volatility spillover as well as a dominant role of short horizon spillover among Bitcoin markets and foreign exchange pairs for six major trading currencies(US dollar,euro,Japanese yen,British pound sterling,Australian dollar,and Canadian dollar).Bitcoin is found to provide significant portfolio diversification benefits for alternative currency foreign exchange portfolios.Alternative currency Bitcoin trading in euro is found to provide the most significant portfolio diversification benefits for foreign exchange portfolios consisting of major trading currencies.The findings of the study regarding spillover dynamics and portfolio diversification capabilities of the Bitcoin market for foreign exchange markets of major trading currencies have significant implications for portfolio diversification and risk minimization.

Analyzing and De-Anonymizing Bitcoin Networks:An IP Matching Method with Clustering and Heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research.Since Bitcoin was created by Nakamoto in 2009,it has,to some extent,deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations.In this paper,the power-law distribution that the Bitcoin network obeys is given with mathematical proof,while traditional deanonymous methods such as clustering fail to satisfy it.Therefore,considering the profit-oriented characteristics of Bitcoin traders in such occasion,we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT),with realtime exchange rate of Bitcoin involved.With heuristic approach used for de-anonymity,algorithm that deals with the adjacency matrix and transition probability matrix are also put forward,which then makes it possible to apply clustering to the IP matching method.Basing on the heuristic approach and additional algorithm for clustering,finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain.Experiments on IP matching method are applied to the actual data.It turns out that similar behavioral pattern between IP and transaction records are shown,which indicates the superiority of IP matching method.

Bitcoin pricing: impact of attractiveness variables

The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency.Using the error correction model,the relationship between the price of the virtual currency,Bitcoin,and the number of Google searches that used the terms bitcoin,bitcoin crash and crisis between December 2012 and February 2018 is analyzed.The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period.The Johansen(J Econ Dyn Control 12:231-254,1988)test demonstrates that the price and number of searches on Google for the first two terms are cointegrated.This research indicates that there are strong short-term and long-term dynamics among attractiveness factors,suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase.In contrast,an increase in market mistrust over a collapse of the currency,as measured by the term bitcoin crash,is followed by a fall in price.Intense world economic crisis events appear to have a strong impact on interest in the virtual currency.This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment,increasing its price.Based on it,bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.

Bitcoin Network Measurement and a New Approach to Infer the Topology

Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on the transaction layer,but less on the network layer.In this paper,we developed BNS(Bitcoin Network Sniffer),which could find and connect nodes in the Bitcoin network,and made a measurement in detail.We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes.We counted the reachable nodes'properties such as:service type,port number,client version and geographic distribution.In addition,we analyzed the stability of the reachable nodes in depth and found nearly 60%kept stable during 15 days.Finally,we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps,which had an accuracy over 80%.

Bitcoin price change and trend prediction through twitter sentiment and data volume

Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease.Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease.In this paper,we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease.We utilise not only sentiment extracted from tweets,but also the volume of tweets.We present results from experiments exploring the relation between sentiment and future price at different temporal granularities,with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change.Two different neural network models are explored and evaluated,one based on recurrent nets and one based on convolutional networks.An additional model is presented to predict the magnitude of change,which is framed as a multi-class classification problem.It is shown that this model yields more reliable predictions when used alongside a price trend prediction model.The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy(63%).

A new proof-of-work mechanism for bitcoin

Background:Bitcoin system,when more than 51%computing power is controlled by a single node,the block chain can be distorted maliciously.This is called 51%attack which is a well-known potential risk that could destroy the Bitcoin system.Method:The paper proves that under the current proof-of-work mechanism,computing power eventually will be centralized at a single node if miners are rational enough.Result:The paper propose a new proof-of-work mechanism that improves decentralization and reduces the risk of 51%attack without increasing the risk of Sybil attack.Concusions:This new mechanism introduces a series of principles such as Career open to all talents,without distinction of birth,Distribution according to labor and All Men are created equal.

Manipulation of the Bitcoin market:an agent-based study

Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.

The linkage between Bitcoin and foreign exchanges in developed and emerging markets

This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.

Take Bitcoin into your portfolio:a novel ensemble portfolio optimization framework for broad commodity assets

The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.

The time‑varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

基于云监管的P2P网络Bitcoin虚拟货币模型

Bitcoin是目前越来越流行的网络虚拟货币,该货币的理论基础是由特定的p2p算法大量计算产生,并使用整个p2p网络中节点构成的分布式数据库来确认并记录所有的交易过程,bitcoin优点很多但随着应用的广泛确定也逐渐显示出。该文试想了将云技术应用于bitcoin的交易过程,使得bitcoin的使用更加安全和快捷。

Whether and when did bitcoin sentiment matter for investors?Before and during the COVID‑19 pandemic

Using a wavelet coherence approach,this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1,2016 to June 30,2021,covering the COVID-19 pandemic period.The results reveal that before the pandemic,sentiment positively drove prices,especially for relatively higher frequencies(2–18 weeks).During the pandemic,the relationship was still positive,but interestingly,the lead-lag relationship disappeared.Employing partial wavelet tools,we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return.Our results robustly reveal that,before the pandemic,sentiment had a positive effect on return.Although positive coherence still existed during the pandemic,the lead-lag relationship disappeared again.Thus,the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies(up to six weeks frequency).