Jiahua Xu, Krzysztof Paruch, Simon Cousaert, Yebo Feng
As an integral part of the decentralized finance (DeFi) ecosystem, decentralized exchanges (DEXs) with automated market maker (AMM) protocols have gained massive traction with the recently revived interest in blockchain and distributed ledger technology (DLT) in general. Instead of matching the buy and sell sides, automated market makers (AMMs) employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function. To facilitate the improvement and development of automated market maker (AMM)-based decentralized exchanges (DEXs), we create the first systematization of knowledge in this area. We first establish a general automated market maker (AMM) framework describing the economics and formalizing the system's state-space representation. We then employ our framework to systematically compare the top automated market maker (AMM) protocols' mechanics, illustrating their conservation functions, as well as slippage and divergence loss functions. We further discuss security and privacy concerns, how they are enabled by automated market maker (AMM)-based decentralized exchanges (DEXs)' inherent properties, and explore mitigating solutions. Finally, we conduct a comprehensive literature review on related work covering both decentralized finance (DeFi) and conventional market microstructure.
Ahmet Faruk Aysan, Asad Ul Islam Khan, Humeyra Topuz, Ahmet Semih Tunalı
This paper explores the applicability of universal cryptocurrency exchange by analyzing crypto exchanges of Binance, Latoken, Kucoin and Qash, which also have their own cryptocurrencies in the crypto market. Results of the recursive Johansen cointegration test proved that even though all of the cryptocurrencies have cointegration among each other, Binance positively disassociated itself from the others after it moved to Malta on 23 March 2018. Based on the daily prices of cryptocurrencies over the period from 6 November 2017 to 10 November 2019, taken from coinmarketcap, we conclude that Binance can be considered as a survival of the fittest among all of the crypto exchanges in this natural experiment.
Michael Demmler, Amilcar Orlian Fernández Domínguez
This paper examines historical Bitcoin price data together with the price data of a well-known and generally accepted historical asset price bubble (the 1720 South Sea Bubble) with the aim of identifying possible similarities. In order to find empirical evidence of speculative bubble tendencies, the article analyses distribution moments and autoregressive models of time series of both assets. Results show that historical daily prices of both assets—taking into account one year before and one year after the maximum price level—clearly show the two phases of bubble expansion and subsequent crash. Furthermore, various similarities between the South Sea Bubble and Bitcoin can be found in descriptive statistics, such as mean of return, standard deviation, and skewness. Statistical tests also show several explosive moments in the time series of the South Sea Company and Bitcoin returns, which implies that both assets exhibit more than one financial bubble.
Paolo De Angelis, Roberto De Marchis, Mario Marino, Antonio Luciano Martire · 5 authors
Abstract In this paper, we come up with an original trading strategy on Bitcoins. The methodology we propose is profit-oriented , and it is based on buying or selling the so-called Contracts for Difference, so that the investor’s gain, assessed at a given future time t , is obtained as the difference between the predicted Bitcoin price and an apt threshold. Starting from some empirical findings, and passing through the specification of a suitable theoretical model for the Bitcoin price process, we are able to provide possible investment scenarios, thanks to the use of a Recurrent Neural Network with a Long Short-Term Memory for predicting purposes.
Since the appearance of Bitcoin, cryptocurrencies have experienced enormousgrowth not only in terms of capitalization but also in number. As a result, thecryptocurrency market can be an attractive arena for investors as it offers manypossibilities, but a difficult one to understand as well. In this work, we aim tosummarize and segment the whole cryptocurrency market in 2018 with the helpof data analysis tools. We will use three different partitional clustering algorithmseach of them using a different representation for cryptocurrencies, namely: yearlymean and standard deviation of the returns, distribution of returns, and timeseries of returns. Since each representation will provide a different andcomplementary perspective of the market, we will also explore the combination ofthe three clustering results to obtain a fine-grained analysis of the main trends ofthe market. Finally, we will analyse the association of the clustering results withother descriptive features of the cryptocurrencies, including the age, technologicalattributes, and financial ratios derived from them. This will help to enhance theprofiling of the clusters with additional insights. As a result, this work offers adescription of the market and a methodology that can be reproduced by investorsthat want to understand the main trends on the market and that look forcryptocurrencies with different financial performance.
CryptoKitties was the first widely recognized blockchain game. Players could own, breed, and trade kitties, which are the only prop in the game. The game gained explosive growth upon its release but quickly collapsed in a short time. This study analyzes its entire player activity history for the first time in literature and tries to find the reasons for the rise and fall of this first blockchain game mania. First, we extracted the five million transaction records among 100 thousand addresses involved in CryptoKitties in the past three years. Based on the numbers of addresses involved in the game each day, we divide the game progress into four stages: the primer, the rise, the fall, and the serenity. We construct a temporal kitty ownership transfer network and analyze the varying network parameters in the four stages. We find that a large number of players poured in during the 10th and 18th days since the game release and quickly exited in the following month. Since then, a few big players have gradually dominated the game, concentrating the game resources. Through further analysis, we find that the main reason for the rapid increase in the game popularity was the increase of public attention by media outlets, while the reasons for the rapid decline in the game popularity include the oversupply of kitties, the decreasing of player income, a widening gap between the rich and poor players, and the limitations of blockchain systems. Based on these observations, we advise on the further blockchain game design: (1) to finely control the production of props and avoid an oversupply, (2) to balance the gaming cost and revenue and protect the enjoyment of players, (3) to narrow down the gap between rich and poor and create an equal gaming community, (4) to consider the limitations of blockchain systems in their game designs.
Στόχος της παρούσας μελέτης ήταν η διερεύνηση της συμπεριφοράς των τιμών πέντε κρυπτονομισμάτων BTC, LTC, ETH, XMR και XRP, των διακυμάνσεων, των πιθανών μέγιστων τιμών, των ελάχιστων τιμών και εάν υπάρχει σύνδεση, συνεργασία στη συμπεριφορά των κρυπτονομισμάτων. Για τον λόγο αυτό, οι ημερήσιες τιμές των πέντε κρυπτονομισμάτων από το 2013 έως το 2020 ανακτήθηκαν από την ιστοσελίδα www.coinmarketcap.com. Αρχικά, πραγματοποιήθηκε ανάλυση συσχέτισης με τη χρήση κυλιόμενου παραθύρου 100 ημερών κάθε ζεύγους κρυπτονομισμάτων, BTC - LTC, BTC - ETH, BTC - XMR, BTC - XRP, LTC - ETH, LTC - XMR, LTC - XRP, ETH - XMR, ETH - XRP και XMR – XRP. Επίσης, πραγματοποιήθηκε μια ανάλυση συνολοκλήρωσης με τη χρήση της δοκιμής Johansen. Η ανάλυση κυλιόμενης συσχέτισης κατέληξε στο συμπέρασμα ότι και τα πέντε κρυπτονομίσματα πριν από το έτος 2017 παρουσίασαν ένα ασταθές μοτίβο. Εν αντιθέσει, μετά το 2017, το επίπεδο συσχέτισης ήταν υψηλότερο από 0,6 και για τα πέντε κρυπτονομίσματα το οποίο αποτελεί ένδειξη σταθερού και παρόμοιου μοτίβου μεταξύ των κρυπτονομισμάτων. Τέλος, η ανάλυση δοκιμής Johansen/συνολοκλήρωσης κατέληξε στο συμπέρασμα ότι υπήρξε μια εξίσωση συνολοκλήρωσης για την περίοδο 2017 έως το 2020. Αυτό το αποτέλεσμα ήταν σύμφωνο με το αποτέλεσμα της ανάλυσης κυλιόμενου παραθύρου.
A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency.
Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.
Beata Szetela, Grzegorz Mentel, Yuriy Bilan, Urszula Mentel
Abstract The aim of the paper is to verify the existence of short- and long-term relationships between the strength of a trend and the volume in bullish and bearish cryptocurrency markets. We applied the vector error correction model to bitcoin daily data from 14.01.2015 to 22.12.2019. Based on the prices and following Wilder’s algorithm, the average directional movement index was calculated, and upward and downward trend periods were determined. No long-term relationship was found to exist between the strength of a trend and the volume in both bearish and bullish markets. Hence, trends do not react to volume changes. However, a long-term relationship exists between volume and trend—but only for the downward trend—with an adjustment speed of 88%. In the short-term, a statistically significant but very weak dependency is revealed; hence, the conclusion that trend strength is insensitive to volume changes can be reached.
Cryptocurrencies have several features that set them aside from traditional currencies. In terms of market capitalization, the top five cryptocurrencies are considered for the analysis to strengthen the research's validation. The most successful crypto asset, being Bitcoin, possesses several characteristics that pose advantages and disadvantages in the financial markets. Digital convenience ensures the safety and ease of use for Bitcoin users, while decentralization also poses a primary benefit. Extreme volatility and the impact of negative externalities on the value of Bitcoin contribute to the assessment of Bitcoin trends in the market. The recent outbreak of the coronavirus (COVID-19) has shown evidence of influencing Bitcoin prices as the virus is spread across continents, leaving the global financial environment in turmoil. The classification of Bitcoin as a hedge is dependent on various factors, including global economic uncertainty. The extent to which the coronavirus impacts cryptocurrencies' hedging capabilities, especially that of Bitcoin’s, is of particular interest during the 2020 pandemic. Analyzing the literature on the influence of crisis on Bitcoin movement will explain why COVID-19 has had such a significant impact on the global financial markets, especially that of cryptocurrencies. The performance of Bitcoin, Ethereum, XRP, Tether, and Bitcoin Cash is compared to that of seven factors including commodities and indices: gold, USD, S&P 500 index, SSE index, world and emerging markets MSCI indices, and Economic Uncertainty, to better understand the hedging capabilities throughout the time of the crisis. This is done using four different multivariate GARCH specifications that account for the nature of the interaction between the cryptocurrencies and the financial variables. Although previous research finds that Bitcoin should act as a hedge during times of economic turmoil, the performance observed during COVID-19 suggests otherwise.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies.
Ana Fernández Vilas, Rebeca P. Dı́az Redondo, Daniel Couto Cancela, Alejandro Torrado Pazos
Cryptocurrencies are a type of digital money meant to provide security and anonymity while using cryptography techniques. Although cryptocurrencies represent a breakthrough and provide some important benefits, their usage poses some risks that are a result of the lack of supervising institutions and transparency. Because disinformation and volatility is discouraging for personal investors, cryptocurrencies emerged hand-in-hand with the proliferation of online users’ communities and forums as places to share information that can alleviate users’ mistrust. This research focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values. In particular, the most popular cryptocurrency Bitcoin (BTC) and a related active discussion community, Bitcointalk, are analyzed. This study shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC, therefore analysis of this interaction would be a perfect base to support personal investments in a non-regulated market and, to confirm whether cryptocurrency forums show evidences to detect abnormal behaviors in BTC values as well as to predict or estimate these values. The experiment highlights that forum data can explain specific events in the financial field. It also underlines the relevance of quotes (regular mechanism to response a post) at periods: (1) when there is a high concentration of posts around certain topics; (2) when peaks in the BTC price are observed; and, (3) when the BTC price gradually shifts downwards and users intend to sell.
Abstract In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.
Marco Ortu, Nicola Uras, Claudio Conversano, Giuseppe Destefanis · 5 authors
This work aims to analyse the predictability of price movements of\ncryptocurrencies on both hourly and daily data observed from January 2017 to\nJanuary 2021, using deep learning algorithms. For our experiments, we used\nthree sets of features: technical, trading and social media indicators,\nconsidering a restricted model of only technical indicators and an unrestricted\nmodel with technical, trading and social media indicators. We verified whether\nthe consideration of trading and social media indicators, along with the\nclassic technical variables (such as price's returns), leads to a significative\nimprovement in the prediction of cryptocurrencies price's changes. We conducted\nthe study on the two highest cryptocurrencies in volume and value (at the time\nof the study): Bitcoin and Ethereum. We implemented four different machine\nlearning algorithms typically used in time-series classification problems:\nMulti Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short\nTerm Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM).\nWe devised the experiments using the advanced bootstrap technique to consider\nthe variance problem on test samples, which allowed us to evaluate a more\nreliable estimate of the model's performance. Furthermore, the Grid Search\ntechnique was used to find the best hyperparameters values for each implemented\nalgorithm. The study shows that, based on the hourly frequency results, the\nunrestricted model outperforms the restricted one. The addition of the trading\nindicators to the classic technical indicators improves the accuracy of Bitcoin\nand Ethereum price's changes prediction, with an increase of accuracy from a\nrange of 51-55% for the restricted model, to 67-84% for the unrestricted model.\n
Dataset retrieved with an Ethereum client, and used by the code hosted here for this paper published in the Proceedings of the Web Conference 2021 (WWW ’21) Abstract: Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.
Mohamed Arbi Madani, Zied Ftiti, Waël Louhichi, Hachmi Ben Ameur
We investigate intraday hedging and the safe haven role of Bitcoin for stocks, currencies, and oil. The hedge concept depends on non-correlation or negative interaction, on average, while the safe haven concept depends on non-correlation or negative correlation in times of market turmoil. We look at Bitcoin’s ability to be a hedge or safe haven asset with standard financial assets by considering a short investment horizon using high frequency data. Accordingly, we propose a new measure, the q-detrending moving average cross-correlation coefficient, to characterise intraday market interdependence between Bitcoin and these assets during medium and extreme movements. During medium fluctuations, Bitcoin is a weak hedge against currencies, oil, and stocks. During high fluctuations, we find a negative relationship between Bitcoin and oil, meaning Bitcoin can serve as a safe haven against extreme down movements in this market. However, Bitcoin is a weak safe haven asset for the other two markets.
The present study aims to establish the model of the cryptocurrency price trend based on financial theory using the LSTM model with multiple combinations between the window length and the predicting horizons, the random walk model is also applied with different parameter settings.
Gianna Figà‐Talamanca, Sergio M. Focardi, Marco Patacca
Abstract In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.
Chi‐Wei Su, Meng Qin, Xiaolei Zhang, Ran Tao · 5 authors
This paper probes the interrelationship between Bitcoin price (BP) and the U.S. partisan conflict (PC) by performing the bootstrap full- and sub-sample Granger causality tests. The positive influence from PC to BP reveals that Bitcoin can be considered as a tool to avoid the uncertainty caused by the rise in PC. However, this view cannot be supported by the negative impact, the major reason is that the burst of bubble undermines the hedging ability of Bitcoin. The above results are inconsistent with the intertemporal capital asset pricing model (ICAPM), underlining that high PC may drive BP to rise, in order to compensate for the losses and costs from factionalism. Conversely, BP has a negative impact on PC, suggesting that the U.S. political situation can be reflected by the Bitcoin market. Under the circumstance of the fiercer factionalism in the U.S., this investigation can benefit investors and related authorities.
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price–volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis, which can cover the nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the asymmetric DCCA coefficient. The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH. In contrast, for XRP and LTC, inverted reactions are present where cross-correlations are stronger in uptrend markets.