Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In this study, we analyzed bitcoin blockchain data for the period between 2013 and 2018. We constructed daily networks and analyzed the network properties of the bitcoin users and bitcoin flow attributed as edge flow circulated between users to focus on weekdays and weekends activities. In the real world, businesses take time off, particularly on weekends. This is no different in the crypto-asset world, which, theoretically, is operable 24/7. We also performed a threshold analysis of the flow of bitcoin to identify the big wallets and to compare it with the identity of real-world crypto-exchange companies, in particular, their weekly patterns. Finally, we propose a methodology to identify the financial institution in the bitcoin blockchain on the basis of fulfilling some key criteria. The criteria are having high frequency, appearing persistently on daily big trades and showing a distinct weekly pattern of total average network flow.
Off-chain transaction channels represent one of the leading techniques to scale the transaction throughput in cryptocurrencies such as Bitcoin. They allow multiple agents to route payments through one another. So far, the topology and construction of payment networks has not been explored much. Participants are expected to minimize costs that are due to the allocation of liquidity as well as blockchain record fees. In this paper we study the optimization of maintenance costs of such networks. We present for the first time, a closed model for symmetric off-chain channels, and provide efficient algorithms for constructing minimal cost spanning-tree networks under this model. We prove that for any network demands, a simple hub topology provides a 2-approximation to the minimal maintenance cost showing that spanning trees in general are efficient. We also show an unbounded price of anarchy in a greedy game between the transactors, when each player wishes to minimize his costs by changing the network's structure. Finally, we simulate and compare the costs of payment networks with scale free demand topologies.
Abstract Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
The temporal nature of modeling accounts as nodes and transactions as\ndirected edges in a directed graph -- for a blockchain, enables us to\nunderstand the behavior (malicious or benign) of the accounts. Predictive\nclassification of accounts as malicious or benign could help users of the\npermissionless blockchain platforms to operate in a secure manner. Motivated by\nthis, we introduce temporal features such as burst and attractiveness on top of\nseveral already used graph properties such as the node degree and clustering\ncoefficient. Using identified features, we train various Machine Learning (ML)\nalgorithms and identify the algorithm that performs the best in detecting which\naccounts are malicious. We then study the behavior of the accounts over\ndifferent temporal granularities of the dataset before assigning them malicious\ntags. For Ethereum blockchain, we identify that for the entire dataset - the\nExtraTreesClassifier performs the best among supervised ML algorithms. On the\nother hand, using cosine similarity on top of the results provided by\nunsupervised ML algorithms such as K-Means on the entire dataset, we were able\nto detect 554 more suspicious accounts. Further, using behavior change analysis\nfor accounts, we identify 814 unique suspicious accounts across different\ntemporal granularities.\n
We consider a particular instance of user interactions in the Bitcoin network, that of interactions among wallet addresses belonging to scammers. Aggregation of multiple inputs and change addresses are common heuristics used to establish relationships among addresses and analyze transaction amounts in the Bitcoin network. We propose a flow centric approach that complements such heuristics, by studying the branching, merger and propagation of Bitcoin flows. We study a recent sextortion campaign by exploring the ego network of known offending wallet addresses. We compare and combine different existing and new heuristics, which allows us to identify (1) Bitcoin addresses of interest (including possible recurrent go-to addresses for the scammers) and (2) relevant Bitcoin flows, from scam Bitcoin addresses to a Binance exchange and to other other scam addresses, that suggest connections among prima facie disparate waves of similar scams.
As the pioneer of blockchain technology, Bitcoin is the most popular cryptocurrency to date. Given its dramatic price spikes (and crashes) along with the never-ending news from SEC regulations to security breaches, there seems to be a lack of understanding about the dynamics of cryptocurrencies. These dynamics are believed to be affected by various political, security, financial, and regulatory events. In this paper, we present an efficient framework for holistic analysis of cryptocurrency fluctuations by introducing the Impact-Score metric to distinguish event-induced changes from normal variations. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that a majority of the events are correlated with substantial network changes. We observed roughly generalizable correlations between event types (e.g. financial events) and sub-structures of the Bitcoin blockchain network. Subgroups of these events have strongly consistent temporal impacts on specific facets (e.g. activity or fees) of the Bitcoin ecosystem. Furthermore, we demonstrate the robustness of our process by correlating a majority of spikes in network/subnetwork change with major events.
Cryptocurrencies have become a prominent investment tool recently with increasing interest in them and their relationships with stock and foreign exchange markets. We analyze here the cross-correlations of price changes of different cryptocurrencies using Random Matrix Theory and extract community structures by constructing minimum spanning trees, finding their eigenvalues contrast sharply with universal predictions of Random Matrix Theory. We reveal distinct transient community structures among different groupings of cryptocurrencies. By studying the cross-correlation dynamics of sub-communities we find evidence of collective behaviour. Furthermore, we compare eigenvalue changes and find prominent groupings following a community trend, useful for creating cryptocurrency portfolios.
Blockchain is a public distributed ledger, which has the characteristics of decentralization and anonymization, which leads to the frequent occurrence of money laundering and theft. Taking Bitcoin as an example, traders can have multiple addresses, and these addresses have nothing to do with their identities in real life, their identities are difficult to identify, and it is difficult to track the flow of transaction funds on the blockchain. This paper proposes a transaction tracking system that can effectively and accurately track the source and destination of a certain amount of funds on the blockchain, which is superior to existing Bitcoin transaction tracking methods and has a substantial reference value.
Nicolò Vallarano, Claudio J. Tessone, Tiziano Squartini
Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks , a generic name referring to a set of three different constructs: the Bitcoin Address Network , the Bitcoin User Network , and the Bitcoin Lightning Network . The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.
The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.
Bitcoin’s evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence of the Bitcoin measured by the Hurst exponent, considering prices from the Brazilian market, and comparing with Bitcoin in USD as a benchmark. We applied Detrended Fluctuation Analysis (DFA), for the period from 9 April 2017 to 30 June 2018, using daily closing prices, with a total of 429 observations. We focused on two prices of Bitcoins resulting from negotiations made by two different Brazilian financial institutions: Foxbit and Mercado. The results indicate that Mercado and Foxbit returns tend to follow Bitcoin dynamics and all of them show persistent behavior, although the persistence in slightly higher for the Brazilian Bitcoin. However, this evidence does not necessarily mean opportunities for abnormal profits, as aspects such as liquidity or transaction costs could be impediments to this occurrence.
Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong · 5 authors
Blockchains are increasingly becoming popular due to the prevalence of cryptocurrencies and decentralized applications. Ethereum is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that cannot be manipulated or changed. Ethereum blockchain introduces a novel ecosystem of human users and autonomous agents (smart contracts). In this network, we are interested in all possible interactions: user-to-user, user-to-contract, contract-to-user, and contract-to-contract. This requires us to construct interaction networks from the entire Ethereum blockchain data, where vertices are accounts (users, contracts) and arcs denote interactions. Our analyses on the networks reveal new insights by combining information from the four networks. We perform an in-depth study of these networks based on several graph properties consisting of both local and global properties, discuss their similarities and differences with social networks and the Web, draw interesting conclusions, and highlight important, future research directions.
Shahar Somin, Goren Gordon, Alex Pentland, Erez Shmueli · 5 authors
Following the birth of Bitcoin and the introduction of the Ethereum ERC20 protocol a decade ago, recent years have witnessed a growing number of cryptographic tokens that are being introduced by researchers, private sector companies and NGOs. The ubiquitous of such Blockchain based cryptocurrencies give birth to a new kind of rising economy, which presents great difficulties to modeling its dynamics using conventional semantic properties. Our work presents the analysis of the dynamical properties of the ERC20 protocol compliant crypto-coins' trading data using a network theory prism. We examine the dynamics of ERC20 based networks over time by analyzing a meta-parameter of the network, the power of its degree distribution. Our analysis demonstrates that this parameter can be modeled as an under-damped harmonic oscillator over time, enabling a year forward of network parameters predictions.
While cryptocurrencies like Bitcoin have the potential to break traditional financial barriers, there are growing concerns about such currencies being used to fund illegal activities. Blockchain keeps the complete history of all transactions ever performed and each node replicates it. The humongous data it contains can be analyzed to gain useful insights about user transactions as well as the blockchain as a whole. In this paper, we propose an approach to parse and visualize the data of Bitcoin blockchain in a graph structure and carry out analysis that includes tracking and tracing, address clustering and entity tagging. We also try to find patterns in the data at a macro level to provide insights about the overall system. Thus, these efforts lead to foundation work for an analysis tool for getting insights on the coin flow of any financial system including cryptocurrencies.
Blockchain governance is a subject of ongoing research and an interdisciplinary view of blockchain governance is vital to aid in further research for establishing a formal governance framework for this nascent technology. In this paper, the position of blockchain governance within the hierarchy of Institutional governance is discussed. Blockchain governance is analyzed from the perspective of IT governance using Nash equilibrium to predict the outcome of different governance decisions. A payoff matrix for blockchain governance is created and simulation of different strategy profiles is accomplished for computation of all Nash equilibria. The paper elaborates upon payoff matrices for different kinds of blockchain governance, which are used in the proposition of novel mathematical formulae usable to predict the best governance strategy that minimizes the occurrence of a hard fork as well as predicts the behavior of the majority during protocol updates. The paper also includes validation of the proposed formulae using real Ethereum data.
Cases of introducing token economy in designs of ICT services are increasing. Users in the early stages of the service are expected to participate in and be active in the service by expecting future price increases in that cryptocurrency. However, the volatility of cryptocurrencies is always intense, and the large volatility may cause users to be more interested in price changes than service activities, which diminishes the incentives for the service activities. In this study, in order to dampen the volatility of cryptocurrencies at the initial stage of their service launch, we assume the case where the service providers make bids to suppress the price changes based on the funds obtained from ICO, and conduct analysis using simulations in artificial market. In order to reproduce the actual price movement in the artificial market, we built an agent model that has the same stylized facts as the price movement of newly listed cryptocurrencies. Then, we introduced a price stabilization agent, and obtained a parameter set that reduces price volatility while suppressing the change in the slope of a simple linear regression compared to the original state using an optimization method. As a result, by introducing the price stabilization agent, we found a parameter set that can reduce the standard division of percentage changes by about 14% from the original price movement, and keep the slope of the simple linear regression trend at a 3.5% change.
Shahar Somin, Yaniv Altshuler, Goren Gordon, Alex Pentland · 5 authors
Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets.
The purpose of this work was to perform a network analysis on the rapidly\ngrowing bitcoin transaction network. Using a web-socket API, we collected data\non all transactions occurring during a six hour window. Sender and receiver\naddresses as well as the amount of bitcoin exchanged were record. Graphs were\ngenerated, using R and Gephi, in which nodes represent addresses and edges\nrepresent the exchange of bitcoin. The six hour data set was subsetted into a\none and two hour sampling snapshot of the network. We performed comparisons and\nanalysis on all subsets of the data in an effort to determine the minimum\nsampling length that represented the network as a whole. Our results suggest\nthat the six hour sampling was the minimum limit with respect to sampling time\nneeded to accurately characterize the bitcoin transaction network.Anonymity is\na desired feature of the blockchain and bitcoin network however, it limited us\nin our analysis and conclusions we drew from our results were mostly inferred.\nFuture work is needed and being done to gather more comprehensive data so that\nthe bitcoin transaction network can be better analyzed.\n
Aman Sharma, Ankit Agrawal, Ashutosh Bhatia, Kamlesh Tiwari
Bitcoin is the most popular cryptocurrency used worldwide. It provides pseudonymity to its users by establishing identity using public keys as transaction end-points. These transactions are recorded on an immutable public ledger called Blockchain which is an append-only data structure. The popularity of Bitcoin has increased unreasonably. The general trend shows a positive response from the common masses indicating an increase in trust and privacy concerns which makes an interesting use case from the analysis point of view. Moreover, since the blockchain is publicly available and up-to-date, any analysis would provide a live insight into the usage patterns which ultimately would be useful for making a number of inferences by law-enforcement agencies, economists, tech-enthusiasts, etc. In this paper, we study various applications and techniques of performing data analytics over Bitcoin blockchain from a graph theoretic perspective. We also propose a framework for performing such data analytics and explored a couple of use cases using the proposed framework.
This paper investigates the return-volatility asymmetry of Bitcoin. We find that the cross correlations between return and volatility (squared return) are mostly insignificant on a daily level. In the high-frequency region, we find thata power-law appears in negative cross correlation between returns and future volatilities, which suggests that the cross correlation is \revision{long ranged}. We also calculate a cross correlation between returns and the power of absolute returns, and we find that the strength of \revision{the cross correlations} depends on the value of the power.
Vladimir Soloviev, Symon P. Yevtushenko, Viktor Batareyev
This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.
Andrey Pogudin, Anindya S. Chakrabati, Tiziana Di Matteo
Cryptocurrencies represent an asset class featuring two unique properties: they are not backed by sovereigns, and their supply is fixed exogenously. This combination becomes apparent in their volatility, which is driven only by demand-side factors. In particular, cryptocurrencies represent an extreme case of the excess volatility puzzle, with asset prices moving more than the fundamentals. We explore the effects of market capitalization on the dynamics of cryptocurrencies within both returns and volatility networks and show that these cryptocurrencies exhibit scaling properties in volatility with respect to market capitalization. The dependency network suggests that currencies with a larger market share have a larger presence in the dominant eigenspectrum, and they exert more influence in the comovement network. In these regards, we find parallels between the dynamics of cryptocurrencies and those of more traditional asset classes. Our findings have implications for both researchers and practitioners in terms of modeling and analyzing the collective behavior of financial assets.
The blockchain paradigm provides a mechanism for content dissemination and distributed consensus on Peer-to-Peer (P2P) networks. While this paradigm has been widely adopted in industry, it has not been carefully analyzed in terms of its network scaling with respect to the number of peers. Applications for blockchain systems, such as cryptocurrencies and IoT, require this form of network scaling. In this paper, we propose a new stochastic network model for a blockchain system. We identify a structural property called \emph{one-endedness}, which we show to be desirable in any blockchain system as it is directly related to distributed consensus among the peers. We show that the stochastic stability of the network is sufficient for the one-endedness of a blockchain. We further establish that our model belongs to a class of network models, called monotone separable models. This allows us to establish upper and lower bounds on the stability region. The bounds on stability depend on the connectivity of the P2P network through its conductance and allow us to analyze the scalability of blockchain systems on large P2P networks. We verify our theoretical insights using both synthetic data and real data from the Bitcoin network.