Jan A. Fischer, Andres Palechor, Daniele DellâAglio, Abraham Bernstein · 5 authors
Bitcoin is built on a blockchain, an immutable decentralised ledger that allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins are associated with alpha-numeric addresses and are transferred via transactions. Each transaction is composed of a set of input addresses (associated with unspent outputs received from previous transactions) and a set of output addresses (to which Bitcoins are transferred). Despite Bitcoin was designed with anonymity in mind, different heuristic approaches exist to detect which addresses in a specific transaction belong to the same entity. By applying these heuristics, we build an Address Correspondence Network: in this representation, addresses are nodes are connected with edges if at least one heuristic detects them as belonging to the same entity. %addresses are nodes and edges are drawn between addresses detected as belonging to the same entity by at least one heuristic. %nodes represent addresses and edges model the likelihood that two nodes belong to the same entity %In this network, connected components represent sets of addresses controlled by the same entity. In this paper, we analyse for the first time the Address Correspondence Network and show it is characterised by a complex topology, signalled by a broad, skewed degree distribution and a power-law component size distribution. Using a large-scale dataset of addresses for which the controlling entities are known, we show that a combination of external data coupled with standard community detection algorithms can reliably identify entities. The complex nature of the Address Correspondence Network reveals that usage patterns of individual entities create statistical regularities; and that these regularities can be leveraged to more accurately identify entities and gain a deeper understanding of the Bitcoin economy as a whole.
May 19, 2021·2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
Cryptocurrencies are in great demand in society. The number of beginners on the cryptocurrency market increases every day. Most of them only focus on a high return on the cryptocurrency investment. However, they do not aware of the high risk from its volatility. Trading is the most popular way to invest with digital currencies due to its potential returns. The cryptocurrency exchange requires a high learning curve for beginners. There exists a strategy to minimize risk in the cryptocurrency investment called "arbitrage". It exploits the market inefficiency to discover a profitable method. This strategy has been investigated in traditional markets like stock markets. Furthermore, some researchers studied arbitrage on general exchange platforms that are operated by companies. However, to the best of our knowledge, there is no research works on arbitrage in the Decentralized Exchange (DEX). The DEX recently emerged with a huge amount of trading volume and profits. The high profit is along with the high risk. Thus, risk-reducing in the cryptocurrency investment is a crucial topic. We demonstrate arbitrage strategy on DEX. The strategy used in this work is to invest an amount of Ether and receive a higher return in each row. The market inefficiency on the current DEX platforms is searched by using an automatic method. We further investigate important factors, which should be considered for profit-maximizing in DEX arbitrage.
Blockchain is a related FinTech asset but it is not the same technology. Basically, Blockchain is a decentralized and distributed digital ledger used to record Bitcoin transactions. The goal of this work is to employ multi-scale analysis to examine self-similarity in EDC Blockchain digital asset. Specifically, market technical data are examined; namely, open, high, low, and close. The resulting generalized Hurst exponent (GHE) estimates revealed that all Blockchain technical indicators exhibit multi-scale dynamics. In addition, short and long dynamics are different. It is concluded that market technical indicators associated with Blockchain provide valuable information for traders.
This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.
In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
Rowel GĂŒndlach, Martijn Gijsbers, David Koops, Jacques Resing
We study the distribution of confirmation times of Bitcoin transactions, conditional on the size of the current memory pool. We argue that the time until a Bitcoin transaction is confirmed resembles the time to ruin in a corresponding Cramer-Lundberg process. This well-studied model gives mathematical insights in the mempool behaviour over time. Specifically, for situations where one chooses a fee, such that the total size of incoming transactions with higher fee is close to the total size of transactions leaving the mempool (heavy traffic), a diffusion approximation leads to an inverse Gaussian distribution for the confirmation times. The results of this paper are particularly interesting for users that want to make a Bitcoin transaction during heavy-traffic situations, as evaluation of the well-known inverse Gaussian distribution is computationally straightforward.
AI and data driven solutions have been applied to different fields and achieved outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers for detecting the trend problem of three cryptocurrency markets. We use these classifiers to design a strategy to trade in those markets. Our input data in the experiments include price data with and without technical indicators in separate tests to see the effect of using them. Our test results on unseen data are very promising and show a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest profit factor for an unseen 66 day span is 1.60. We also discuss limitations of these approaches and their potential impact on Efficient Market Hypothesis.
This paper sets out to explore the nexus between economic policy uncertainty (EPU) and digital currencies. An integrated survey takes place based on eleven primary studies. Furthermore, an econometric analysis is conducted by the threshold ARCH, simple asymmetric ARCH and non-linear ARCH specifications covering the bull and the bear markets as well as the highly volatile period up to the present. Threshold ARCH is found to provide the best fit for estimations. Outcomes reveal that Bitcoin is strongly connected with EPU while Ethereum and Litecoin are not but are strongly linked with Bitcoin performance. Moreover, weak negative effects of the VIX on both cryptocurrencies are detected while oil exerts weak positive impacts on Ethereum. Overall, Ethereum and Litecoin could serve for diversifiers against Bitcoin or hedgers against traditional assets during highly stressed periods with the advantage of not being affected by economic policy uncertainty news.
This study tested the efficient-market hypothesis (EMH) to examine information efficacy in the cryptocurrency market. We conducted three random walk tests to verify the weak-form EMH and used the event study method to test the semi-strong-form EMH. The analysis results demonstrated that 54 (6.04%) of the total of 893 cryptocurrency units satisfied the weak-form EMH, and 24 (2.695%) met the semi-strong market hypothesis. Furthermore, we found that, among the cryptocurrency exchanges that were established before November 2017, large size exchanges were more likely to satisfy the weak- and semi-strong-form EMHs.
In recent years blockchain consensus mechanisms based on Proof of Stake gained increasing attention as an alternative to Proof of Work, which requires high energy consumption. In its original version Proof of Stake hinges on the idea that, for a user, the likelihood to confirm the next block is positively related to the amount of currency units held in the wallet, and possibly also on the time length which the money has been unspent for. In a simple framework with risk neutral users we provide some early insights on the monetary equilibrium of Proof of Stake based platforms. In particular, we find that the aggregate demand and supply of currency may not coincide, which implies that users could hold suboptimal quantities of the currency. Furthermore, we also discuss how symmetric stationary states of the system could be implausible. As a consequence, a long run uniform distribution of money would seem unlikely unless appropriate measures are introduced.
After Nakamoto introduced Bitcoin in 2008 as an alternative online payment system, it became an appealing investment vehicle and an attractive area of study for many researchers. Although much research has been done on the valuation of Bitcoin, comparatively little treatment has been spent on Bitcoinâs relationship to established economic indicators, at least in IS research. This study examines Bitcoin across its history using a time-series structural break analysis to differentiate five distinct periods within Bitcoinâs evolution. Data obtained includes the open-high-low-close and volume Bitcoin trading data by the minute from October 15, 2010 until January 1, 2020. Within each period, we examine Bitcoinâs closing price in relation to an established set of economic indicators that encompass economic health, long-term economic stability, monetary policy, and investor sentiment. We find that Bitcoin has matured from a speculative trading mechanism to an independent investment instrument that is responsive to underlying macroeconomic factors.
Purpose: The purpose of this research is to analyze the price movements of bitcoin, which has become a new phenomenon in financial markets since 2009, the first year of its release, and can be defined as virtual money or crypto money, to be seen as a financial investment tool. Design/Methodology: In the study, volatility, return behavior and reliability as a financial investment tool are examined with autoregressive Conditional Variable Variance modeling. In this context, symmetrical and asymmetrical ARCH models were used. Findings: As a result of the analysis; it has been found that it has an asymmetric effect in the first period for the bitcoin return series examined with symmetric and asymmetric ARCH models. In addition, it has been determined that shocks occurring in the bitcoin return series according to the half-life criteria are exposed to the volatility effect for more than 30 days in each period. It has been determined that bitcoin, which is examined by periods, has higher volatility in its first years. Limitations: The volatility of bitcoin, which has become a new phenomenon in financial markets today, can be defined as virtual money or crypto money, has been analyzed. Originality/Value: In fact, there are many virtual currencies or cryptocurrencies traded in the market. However, among many virtual currencies, bitcoin is the most known and the most market volume. Analyzing the price movements of bitcoin, which has started to be seen as a financial investment tool, is of great importance in the framework of reliability. The examination made in this respect constitutes the original value of the research.
Decentralized cryptocurrency exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. We propose a dynamic AMM approach where input from a market price oracle is used to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities.
Abstract Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.
Xiao Fan Liu, Huanhuan Ren, Si-Hao Liu, Xian-Jian Jiang
Abstract The cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency usersâ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real âoff-chainâ identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agentsâ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networksâ features provide the richest information in the economic agentâs identification.
The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.
Abstract This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.