We discuss the idea of a purely algorithmic universal world iCurrency set forth in [Kakushadze and Liew, 2014] (https://ssrn.com/abstract=2542541) and expanded in [Kakushadze and Liew, 2017] (https://ssrn.com/abstract=3059330) in light of recent developments, including Libra. Is Libra a contender to become iCurrency? Among other things, we analyze the Libra proposal, including the stability and volatility aspects, and discuss various issues that must be addressed. For instance, one cannot expect a cryptocurrency such as Libra to trade in a narrow band without a robust monetary policy. The presentation in the main text of the paper is intentionally nontechnical. It is followed by an extensive appendix with a mathematical description of the dynamics of (crypto)currency exchange rates in target zones, mechanisms for keeping the exchange rate from breaching the band, the role of volatility, etc.
Bitcoin draws the highest degree of attention among cryptocurrencies, while coin mining is one of the most important fashion of profiting in the Bitcoin ecosystem. This paper constructs fresh coin circulation networks by tracking the fresh coin transfer routes with transaction referencing in Bitcoin blockchain. This paper proposes a heuristic algorithm to identifying coin miners by comparing coin circulation networks from different mining pools and thereby inferring the common profit distribution schemes of Bitcoin mining pools. Furthermore, this paper characterizes the increasing trend of Bitcoin miner numbers during recent years.
Stablecoins promise to bridge fiat currencies with the world of cryptocurrencies. They provide a way for users to take advantage of the benefits of digital currencies, such as ability to transfer assets over the internet, provide assurance on minting schedules and scarcity, and enable new asset classes, while also partially mitigating their volatility risks. In this paper, we systematically discuss general design, decompose existing stablecoins into various component design elements, explore their strengths and drawbacks, and identify future directions.
Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele · 7 authors
Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.
Alexandre Bovet, Carlo Campajola, Francesco Mottes, Valerio Restocchi · 7 authors
Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants.These cryptocurrencies can also be acquired in exchange markets.The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users' behaviour and the cryptocurrency pricing in exchange markets.This paper analyses the properties of the transaction network of Bitcoin.We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions.Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price.By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology.Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users' activity.
Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryptocurrency trade. We find that when engaging in cryptocurrency trading investors simultaneously increase their risk-seeking behavior in stock trading as they increase their trading intensity and use of leverage. The increase in risk-seeking in stocks is particularly pronounced when volatility in cryptocurrency returns is low, suggesting that their overall behavior is driven by excitement-seeking.
Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.
Blockchain technology, and more specifically Bitcoin (one of its foremost applications), have been receiving increasing attention in the scientific community. The first publications with Bitcoin as a topic, can be traced back to 2012. In spite of this short time span, the production magnitude (1162 papers) makes it necessary to make a bibliometric study in order to observe research clusters, emerging topics, and leading scholars. Our paper is aimed at studying the scientific production only around bitcoin, excluding other blockchain applications. Thus, we restricted our search to papers indexed in the Web of Science Core Collection, whose topic is "bitcoin". This database is suitable for such diverse disciplines such as economics, engineering, mathematics, and computer science. This bibliometric study draws the landscape of the current state and trends of Bitcoin-related research in different scientific disciplines.
Ingolf Gunnar Anton Pernice, Sebastian Henningsen, Roman Proskalovich, Martin Florian · 6 authors
The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during the last two years, multiple so called stablecoins have surfaced---cryptocurrencies focused on maintaining stable exchange rates. In this paper, we systematically explore and analyze the stablecoin landscape. Based on a survey of 24 specific stablecoin projects, we go beyond individual coins for extracting general concepts and approaches. We combine our findings with learnings from classical monetary policy, resulting in a comprehensive taxonomy of cryptocurrency stabilization. We use our taxonomy to highlight the current state of development from different perspectives and show blank spots. For instance, while over 91% of projects promote 1-to-1 stabilization targets to external assets, monetary policy literature suggests that the smoothing of short term volatility is often a more sustainable alternative. Our taxonomy bridges computer science and economics, fostering the transfer of expertise. For example, we find that 38% of the reviewed projects use a combination of exchange rate targeting and specific stabilization techniques that can render them vulnerable to speculative economic attacks - an avoidable design flaw.
Sandra Johnson, Peter Robinson, Kishore Atreya, Claudio Lisco
Supply chains lend themselves to blockchain technology, but certain challenges remain, especially around invoice financing. For example, the further a supplier is removed from the final consumer product, the more difficult it is to get their invoices financed. Moreover, for competitive reasons, retailers and manufacturers do not want to disclose their supply chains. However, upstream suppliers need to prove that they are part of a `stable' supply chain to get their invoices financed, which presents the upstream suppliers with huge, and often unsurmountable, obstacles to get the necessary finance to fulfil the next order, or to expand their business. Using a fictitious supply chain use case, which is based on a real world use case, we demonstrate how these challenges have the potential to be solved by combining more advanced and specialised blockchain technologies with other technologies such as Artificial Intelligence. We describe how atomic crosschain functionality can be utilised across private blockchains to retrieve the information required for an invoice financier to make informed decisions under uncertainty, and consider the effect this decision has on the overall stability of the supply chain.
This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) Bitcoin's market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.
The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrencies, which supports the theories of investor herding behavior. Moreover, we propose two profitable long-only strategies: the illiquid losers and liquid winners, which exhibit improved risk adjusted performance over the market capitalization weighted portfolio.
Avinash Barnwal, Hari Pad Bharti, Aasim Ali, Vishal Krishna Singh
Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminative classifiers to create the stack, particularly 3 generative and 6 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators used are not limited to trend, momentum, volume, volatility indicators, and sentiment analysis has also been used to gain useful insight combined with the above features. For Cross-validation, Purged Walk forward cross-validation has been used. In terms of accuracy, we have done a comparative analysis of the performance of Ensemble method with Stacking and Ensemble method with blending. We have also developed a methodology for combined features importance for the stacked model. Important indicators are also identified based on feature importance.
Abeer ElBahrawy, Laura Alessandretti, Andrea Baronchelli
The production and consumption of information about Bitcoin and other digital-, or crypto- , currencies have grown together with their market capitalization. However, a systematic investigation of the relationship between online attention and market dynamics across multiple digital currencies is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analyzing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better than baseline strategies, in terms of returns on investment, for most of the covered period although the ‘buy and hold strategy’ dominates during the periods of explosive market expansion. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors.
The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts' transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.
In this paper, we will describe a concept of a cryptocurrency issuance protocol which supports digital currencies in a Proof-of-Work ( ) like manner. However, the methods assume alternative utilization of assets used for cryptocurrency creation (rather than purchasing electricity necessary for ).
Christian Fries, Peter Kohl-Landgraf, Björn Paffen, Stefanie Weddigen · 11 authors
In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a centralized and on a DLT platform respectively. Beyond that we highlight some insights on legal aspects as well as on common integration challenges regarding existing process and system landscapes. For a further introductory note and motivation on the theoretical concept we refer to https://www.law.ox.ac.uk/business-law-blog/blog/2018/12/smart-derivative-contract-constructing-digital-financial-derivative . A very detailed methodological overview of the concept of a smart derivative contract can be found in doi:10.2139/ssrn.3163074.
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.
... This interdisciplinary article discusses the potential consequences due to distributed ledger technology (DLT), tokenization as well as the emergence of new kinds of firm stakeholders, ie the crypto-assets holders, on the governance of small and medium-sized enterprises (SMEs) as well as of publicly traded companies. Since early 2016, a new way of issuing assets and raising funds has rapidly emerged as a major issue for FinTech founders and financial regulators. Frequently referred to as initial coin offerings, initial token offerings (ITO), token generation events (TGE) or simply ‘token sales’, we use in our article the terminology initial crypto-asset offerings (ICO), as it describes more effectively than ‘initial coin offerings’ the vast diversity of assets (utility tokens, security tokens, crypto-currencies) that could be created and which goes far beyond the sole payment instrument issue. An ICO can be summarized as follows: a new method to issue assets, frequently to raise funds, through the offer and sale by a group of developers or a company to a crowd (ie investors or contributors) of ad hoc crypto-assets (also coined as ‘tokens’) specifically created and issued on a distributed ledger, sometimes preceded by an early sale of the crypto-assets called ‘pre-sale’, for the purpose of launching a business or of developing ad hoc asset functions/features and/or governance of projects based, in several cases, on the distributed ledger technology, typically in exchange for pre-existing ‘mainstream’ crypto-assets, such as Bitcoin and Ether among others, or fiat currencies. Perceived by several entrepreneurs as a less burdensome way of fundraising, at least 25 billion dollars have been raised between March 2016 and August 2018 through ICOs only.1
Abstract A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.
We investigate connectedness within and across two major groups or assets: i) five popular cryptocurrencies, and ii) six major asset classes plus two commonly employed risk factors. Granger-causality tests uncover six direct channels of causality from the elements of the mainstream assets/risk factors group to digital assets. On the other hand there are two statistically significant causal links going in the other direction. In order to provide some perspective on the magnitude of the uncovered linkages we supplement the analysis by estimating networks from forecast error variance decompositions. The estimated connectedness within the groups is relatively large, whereas the linkages across the two groups are small in comparison. Namely, less than 2.2 percent of future uncertainty of any cryptocurrency is sourced from all non-crypto assets combined, while the joint contribution of all digital assets to non-crypto uncertainty does not exceed 1.5 percent.
The world of cryptocurrency is not transparent enough though it was established for innate transparent tracking of capital flows. The most contributing factor is the violation of securities laws and scam in Initial Coin Offering (ICO) which is used to raise capital through crowdfunding. There is a lack of proper regularization and appreciation from governments around the world which is a serious problem for the integrity of cryptocurrency market. We present a hypothetical case study of a new cryptocurrency to establish the transparency and equal right for every citizen to be part of a global system through the collaboration between people and government. The possible outcome is a model of a regulated and trusted cryptocurrency infrastructure that can be further tailored to different sectors with a different scheme.
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy possess the characteristics of strong incentives and low cost, facilitating integrating renewable energy and demand side management, and providing guarantees for improving energy efficiency and reducing emission. This article describes the concept and functionality of token economics, and then analyzes the feasibility of applying token economics in the energy systems, and finally discuss the applications of token economics with an example in integrated energy systems.
Optimization methods are used to determine equilibria of investment in cryptocurrencies. The basic assumptions involve existence of a core group (the "wealthy") that fears the loss of substantial assets through government seizure. Speculators constitute another group that tends to introduce volatility and risk for the wealthy. The wealthy must divide their assets between the home currency and the cryptocurrency, while the government decides on the probability of seizing a fraction the assets of this group. Under the assumption that each group exhibits risk aversion through a utility function, we establish the existence and uniqueness of Nash equilibrium. Also examined is the more realistic optimization problem in which the government policy cannot be reversed, while the wealthy can adjust their allocation in reaction to the government's designation of probability. The methodology leads to an understanding the equilibrium market capitalization of cryptocurrencies.