Yue‐Jun Zhang, Elie Bouri, Rangan Gupta, Shujiao Ma
We challenge the existing literature that points to the detachment of Bitcoin from the global financial system. We use daily data from August 17, 2011 - February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and the traditional financial markets for the sake of asset allocation, and risk management. For policy makers, our findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.
The Covid-19 bear market presents the first acute market losses since active trading of Bitcoin began. This market downturn provides a timely test of the frequently expounded safe haven properties of Bitcoin. In this paper, we show that Bitcoin does not act as a safe haven, instead decreasing in price in lockstep with the S&P 500 as the crisis develops. When held alongside the S&P 500, even a small allocation to Bitcoin substantially increases portfolio downside risk. Our empirical findings cast doubt on the ability of Bitcoin to provide shelter from turbulence in traditional markets.
Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments. Despite some studies highlighting the network does not provide full anonymity, it is still being heavily used for a wide variety of dubious financial activities such as money laundering, ponzi schemes, and ransom-ware payments. In this paper, we explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an in-depth analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information. Then, we propose and evaluate a set of classifiers based on four types of graph features: immediate neighbours, curated features, deepwalk embeddings, and node2vec embeddings to classify money laundering and regular transactions. Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29% and an F1-measure of 0.93 and high robustness over a 2.5-year time span. Finally, we demonstrate how effective our classifiers are in discovering unknown laundering services. The classifier performance dropped compared to binary classification, however, the prediction can be improved with simple ensemble techniques for some services.
This article examines cryptocurrency cases decided in the U.S. District and Circuit Courts to determine the applicability of Gottschalk’s convenience theory of white-collar crime to cryptocurrency crime litigation and to empirically analyze whether the conditions under which cryptocurrency offenses occurred show support for the convenience theory. Analysis of U.S. federal district and circuit court case law involving cryptocurrency crimes and fraud indicates support for the convenience theory of white-collar crime. Defendants in various schemes were motivated by financial gain, either for the company or for personal use. Their roles and positions in the businesses allowed them access to resources that helped them perpetrate fraud through the following mechanisms: (1) operating front companies; (2) relationship building by defendants; (3) over representing profits that investors would obtain from purchases of virtual currencies, representing that cryptocurrencies were safe and reliable investments when they were risky, and overestimating abilities and capacities to provide services promised to investors in securities fraud; (4) breaching fiduciary duties to their clients and corporate stockholders by misappropriating profits for their own personal gain; and (5) engaging in dark web transactions that guaranteed anonymity. Defendants also employed various neutralization techniques to justify their crimes.
Agreements, consensuses, protocols, resource-sharing, and fairness are all examples of social and political metaphors that define and shape new computational algorithms. The thought experiments and allegories about resource-sharing or agreement between nodes played a vital role in the development of "concurrent programming" (enabling processor power-sharing and process synchronization) and still later in the development of distributed computing (facilitating data access and synchronization). These paved the way for current concepts of consensus mechanisms, smart contracts, and other descriptions of cryptocurrencies, blockchain, distributed ledger, and hashgraph technologies, paradoxically reversing the relations between metaphor and artifact. New computing concepts and algorithmic processes, such as consensus mechanisms, trustless networks, and automated smart contracts or DAOs (Distributed Autonomous Organizations), aim to disrupt social contracts and political decision-making and replace economic, social, and political institutions (e.g., law, money, voting). Rather than something that needs a metaphor, algorithms are becoming the metaphor of good governance. Current fantasies of algorithmic governance exemplify this reversal of the role played by metaphors: they reduce all concepts of governance to automation and curtail opportunities for defining new computing challenges inspired by the original allegories, thought experiments, and metaphors. Especially now, when we are still learning how best to govern the transgressions and excesses of emerging distributed ledger technologies, productive relations between software and allegory, algorithms and metaphors, code and law are possible so long as they remain transitive. Against this tyranny of algorithms and technologies as metaphors and aspirational models of governance, we propose sandboxes and environments that allow stakeholders to combine prototyping with deliberation, algorithms with metaphors, codes with regulations.
Andrea Jonathan Pagano, Francesco Romagnoli, Emanuele Vannucci
Abstract Risk insurance for disasters plays a relevant part in the implementation of risk reduction strategies during the pre-disaster phase. This is essential to support risk management towards decreasing the marginal risk allowing policy holders to transfer risk to avoid considerable financial loads from the costs incurred during the recovery phase in a post-disaster phase. There is evidence that the introduction of an integrated risk insurance strategy for community resilience planning is still lacking. Thus, this undermines the possibility to have proper optimized holistic risk management; on the one hand this strengthens pre-disaster risk mitigation measures, mostly relying on mitigative infrastructural solutions, and on the other hand it better defines risk prevention strategies mostly connected to land planning and urban development. This paper will show how insurance markets can play a key role towards mitigating the economic consequences of natural and climate change disasters, and how essential it is to better quantify the beneficial effects and costs of engineer-based mitigative solutions. In this context, the legal framework into which the actuarial quantitative model can be implemented will support the creation of an integrated multidisciplinary approach with potential implementation on a novel platform capable of collecting and processing information from different sources and dimensions such as blockchain technology. The scientific community is, in fact, increasingly interested in implementing blockchain technology to overcome problems linked to the contractual dimension of natural disaster risk insurance which can be interpreted as a sort of smart contracting. Through a study that involved four distinct areas, namely: law, environmental engineering, insurance and IT, this paper proposes a specific multidisciplinary methodology to achieve the drafting and implementation of a digital insurance contract on a blockchain platform against natural hazards. This paper proposes the basis to advance a quantitative concept to optimize the impact of catastrophe risk insurance onto the community resilience; in fact providing a key synergy for definition of pre-disaster conditions.
Ever since Bitcoin got its popularity, cryptocurrencies becomes the first choice of investors, tech enthusiasts, and criminals. Nowadays it became a prominent tool for money laundering, hawala and criminal payment system. Money laundering is the process of converting illegal black money to legal white money. This can be done by various ways like transferring money from one account to another via multiple intermediate accounts. Detecting this complex activity is very difficult and challenging, because of the high volume of transactions and bitcoins data structure. There are many methods proposed in different papers to detect money laundering in the conventional banking system. In this study, we proposed a framework to convert the bitcoin's transnational data into a similar data frame of bank's user database, which is used by some existing state of the art intelligent systems to detect the abnormal cluster of transactions and user behavior.
Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.
Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Goldsmith · 6 authors
Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving\ndrugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and\nseveral police raids and scams have caused large losses to both customers and vendors over the past\nyears. However, this uncertainty has not prevented a steady growth of the dark market phenomenon\nand a proliferation of new markets. The origin of this resilience have remained unclear so far, also due\nto the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark\nmarket ecosystem re-organises following the disappearance of a market, due to factors including raids\nand scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets\nof 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD.\nWe show that coordinated user migration from the closed market to coexisting markets guarantees\noverall systemic resilience beyond the intrinsic fragility of individual markets. The migration is\nswift, efficient and common to all market closures. We find that migrants are on average more active\nusers in comparison to non-migrants and move preferentially towards the coexisting market with\nthe highest trading volume. Our findings shed light on the resilience of the dark market ecosystem\nand we anticipate that they may inform future research on the self-organisation of emerging online\nmarkets.
Francesco Zola, Jan L. Bruse, Maria Eguimendia, Mikel Galar · 5 authors
The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity of its entities using machine learning-based techniques, which consider the whole blockchain. This entails two issues: first, it increases the complexity of the analysis requiring higher efforts and, second, it may hide network micro-dynamics important for detecting short-term changes in entity behavioral patterns. The aim of this paper is to address both issues by performing a “temporal dissection” of the Bitcoin blockchain, i.e., dividing it into smaller temporal batches to achieve entity classification. The idea is that a machine learning model trained on a certain time-interval (batch) should achieve good classification performance when tested on another batch if entity behavioral patterns are similar. We apply cascading machine learning principles—a type of ensemble learning applying stacking techniques—introducing a “k-fold cross-testing” concept across batches of varying size. Results show that blockchain batch size used for entity classification could be reduced for certain classes (Exchange, Gambling, and eWallet) as classification rates did not vary significantly with batch size; suggesting that behavioral patterns did not change significantly over time. Mixer and Market class detection, however, can be negatively affected. A deeper analysis of Mining Pool behavior showed that models trained on recent data perform better than models trained on older data, suggesting that “typical” Mining Pool behavior may be represented better by recent data. This work provides a first step towards uncovering entity behavioral changes via temporal dissection of blockchain data.
Mining attacks allow attackers to gain an unfair share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among the most well-known are block withholding (BWH), fork after withholding (FAW), and selfish mining. In this paper, we propose two new strategies: power adjusting and bribery racing, and introduce two novel mining attacks, Power Adjusting Withholding (PAW) and Bribery Selfish Mining (BSM) adopting the new strategies. Both attacks can increase the reward of attackers. Furthermore, we show PAW can avoid the "miner's dilemma" in BWH attacks. BSM introduces a new "venal miner's dilemma", which results in all targets (bribes) willing to help the attacker but getting less reward finally. Quantitative analyses and simulations are conducted to verify the effectiveness of our attacks. We propose some countermeasures to mitigate the new attacks, but a practical and efficient solution remains to be an open problem.
Motivated by the success of Bitcoin, lots of cryptocurrencies have been created, the majority of which were implemented as smart contracts running on Ethereum and called tokens. To regulate the interaction between these tokens and users as well as third-party tools (e.g., wallets, exchange markets, etc.), several standards have been proposed for the implementation of token contracts. Although existing tokens involve lots of money, little is known whether or not their behaviors are consistent with the standards. Inconsistent behaviors can lead to user confusion and financial loss, because users/third-party tools interact with token contracts by invoking standard interfaces and listening to standard events. In this work, we take the first step to investigate such inconsistent token behaviors with regard to ERC-20, the most popular token standard. We propose a novel approach to automatically detect such inconsistency by contrasting the behaviors derived from three different sources, including the manipulations of core data structures recording the token holders and their shares, the actions indicated by standard interfaces, and the behaviors suggested by standard events. We implement our approach in a new tool named TokenScope and use it to inspect all transactions sent to the deployed tokens. We detected 3,259,001 transactions that trigger inconsistent behaviors, and these behaviors resulted from 7,472 tokens. By manually examining all (2,353) open-source tokens having inconsistent behaviors, we found that the precision of TokenScope is above 99.9%. Moreover, we revealed 11 major reasons behind the inconsistency, e.g., flawed tokens, standard methods missing, lack of standard events, etc. In particular, we discovered 50 unreported flawed tokens.
Abstract Hacks are one of the most damaging types of cryptocurrency related crime, accounting for billions of dollars in stolen funds since 2009. Professional investigators at Chainalysis have traced these stolen funds from the initial breach on an exchange to off-ramps, i.e. services where criminals are able to convert the stolen funds into fiat or other cryptocurrencies. We analyzed six hack subnetworks of bitcoin transactions known to belong to two prominent hacking groups. We analyze each hack according to eight network features, both static and temporal, and successfully classify each hack to its respective hacking group through our newly proposed method. We find that the static features, such as node balance, in degree, and out degree are not as useful in classifying the hacks into hacking groups as temporal features related to how quickly the criminals cash out. We validate our operating hypothesis that the key distinction between the two hacking groups is the acceleration with which the funds exit through terminal nodes in the subnetworks.
Cybercriminals exploit cryptocurrencies to carry out illicit activities. In this paper, we focus on Ponzi schemes that operate on Bitcoin and perform an in-depth analysis of MMM, one of the oldest and most popular Ponzi schemes. Based on 423K transactions involving 16K addresses, we show that: (1) Starting Sep 2014, the scheme goes through three phases over three years. At its peak, MMM circulated more than 150M dollars a day, after which it collapsed by the end of Jun 2016. (2) There is a high income inequality between MMM members, with the daily Gini index reaching more than 0.9. The scheme also exhibits a zero-sum investment model, in which one member's loss is another member's gain. The percentage of victims who never made any profit has grown from 0% to 41% in five months, during which the top-earning scammer has made 765K dollars in profit. (3) The scheme has a global reach with 80 different member countries but a highly-asymmetrical flow of money between them. While India and Indonesia have the largest pairwise flow in MMM, members in Indonesia have received 12x more money than they have sent to their counterparts in India.
In this explorative study we provide empirical insight into how organized crime offenders use IT to launder their money. Our empirical data consist of 30 large-scale criminal investigations into organized crime. These cases are part of the most recent, fifth data sweep of the Dutch Organized Crime Monitor (DOCM). We do not focus on cybercrime alone. Instead, we explore the financial aspects of criminal operations in a broad range of types of organized crime, i.e. from ‘traditional’ types of organized crime, such as offline drug smuggling, to cybercrime. Regarding the spending of criminal proceeds (consumption and investment), the analyses show several similarities and no major differences between traditional crime and cybercrime. When it comes to concealing criminal earnings (money laundering), we do see important differences. Financial innovation, such as the use of cryptocurrencies, seems to be limited to cases of IT-related crime. One of the most striking similarities between cybercrime and traditional crime is the offenders’ preference for cash. In the analysed cases, malware and phishing offenders as well as online drug traffickers change their digital currencies for cash, at least in part.
Why this book? In this Liber Amicorum, compiled in honour of the 60th anniversary of CFE Tax Advisers Europe, renowned tax experts discuss key tax issues that challenge tax advisers, tax academics and tax officials on a daily basis. Part I looks at EU decision-making in the tax area and some of the challenges of exercising tax jurisdiction in a digital world (taxing digital business models, robot taxes, etc.). Part II discusses the legal limits, particularly in Europe, to the traditional ways in which states exercise their tax jurisdiction (e.g. the need for equal treatment, the prohibition of discriminatory exit taxes and the ECJ Sofina decision) and the closely related issue of taxpayer rights (under EU law and the European Convention on Human Rights). Part III reports on recent developments in the fight against tax avoidance and tax evasion (e.g. the OECD BEPS Action Plan, the European Union’s external “tax good governance” policy, international exchange of information, transfer pricing documentation requirements, the ECJ Denmark decisions and the Commission’s Apple decision). Part IV presents an in-depth analysis of VAT (lessons learned) and suggests new ways forward, including in respect of dispute management (cross-border rulings). Finally, Part V reflects on non-tax issues that may have implications on international taxation and finance. With its practical approach, the book provides an interesting and insightful read for all those involved in international taxation. Downloads Sample excerpt, including table of contents Editor(s) Servaas van Thiel, Piergiorgio Valente, Stella Raventós-Calvo Contributor(s) Christian Amand, Krister Andersson, Dick Barmentlo, Claudia Barsotti, Andrea Borroni, Paolo Centore, Matteo Dellapina, Christina Dimitropoulou, Ana Paula Dourado, Steef Huibregtse, Georg Kofler, Paul Kraan, Howard M. Liebman, Marco Nicoli, Tom O’Shea, Pasquale Pistone, Stella Raventos-Calvo, Franco Roccatagliata, Marina Menezes da Silva, Kartika Sukmatullahi, Alexander van Thiel, Servaas van Thiel, Alessandro Valente, Piergiorgio Valente, Jeremy Woolf.
Ramiro Daniel Camino, Christof Ferreira Torres, Mathis Baden, Radu State
Ethereum smart contracts have recently drawn a considerable amount of attention from the media, the financial industry and academia. With the increase in popularity, malicious users found new opportunities to profit by deceiving newcomers. Consequently, attackers started luring other attackers into contracts that seem to have exploitable flaws, but that actually contain a complex hidden trap that in the end benefits the contract creator. In the blockchain community, these contracts are known as honeypots. A recent study presented a tool called HONEYBADGER that uses symbolic execution to detect honeypots by analyzing contract bytecode. In this paper, we present a data science detection approach based foremost on the contract transaction behavior. We create a partition of all the possible cases of fund movements between the contract creator, the contract, the transaction sender and other participants. To this end, we add transaction aggregated features, such as the number of transactions and the corresponding mean value and other contract features, for example compilation information and source code length. We find that all aforementioned categories of features contain useful information for the detection of honeypots. Moreover, our approach allows us to detect new, previously undetected honeypots of already known techniques. We furthermore employ our method to test the detection of unknown honeypot techniques by sequentially removing one technique from the training set. We show that our method is capable of discovering the removed honeypot techniques. Finally, we discovered two new techniques that were previously not known.
<ns3:p>Before 2008, one thing that could be said about the derivatives market was that they were anything but regulated. Nowadays, the same thing can be said about the current financial innovation: cryptocurrencies. However, there are many private law institutions which have not been regulated for centuries, yet they did not trigger a worldwide financial crisis. The reason for that is that the financial crisis constituted a complex phenomenon caused by many factors, occurring jointly and affecting the derivatives market in unison, not just by lack of regulation. This paper will elaborate whether factors which, in the opinion of the author, caused the recent financial crisis, currently exist, or can occur in the future, in the cryptocurrency market. Such a study will allow a conclusion to be drawn as to whether cryptocurrencies are capable of triggering a similar financial crisis as derivatives did before their current partial regulation.</ns3:p>
Blockchain technology is one of the most exciting emerging technologies and has the capability to digitally revolutionize many systems for better security, autonomy, transparency, auditability, speed, reduced cost and efficiency. Blockchain can be regarded as a revolutionized distributed ledger technology which uses temper proof database in conjunction with consensus algorithms and cryptography. On the other hand, corruption has been a major problem in developing countries like Nigeria that bedevils their progress and development. In this paper, we investigate corrupt practices in developing countries with Nigeria as a case study. We study some blockchain use cases in some countries like Estonia and Georgia. We propose a blockchain framework to curb corrupt practices related to public funds embezzlement as the biggest corruption act. All transactions made by government agencies are stored in the blockchain shared by anti-corruption agencies, police, judiciaries and other related offices at the same time. We also propose the blockchain architecture and consensus suitable for the framework. Blockchain technology is immune to the influence of central authorities and intermediaries. It is secure against tempering and provides transparency and auditability. Hence using blockchain technology will help to tackle corrupt practices for betterment of developing countries such as Nigeria.
Purpose – The purpose of this paper was to conduct a systemic review of extant cryptocurrency research in order to identify important features of these studies and to provide directions for future cryptocurrency research. Methodology - The Systematic Quantitative Assessment Technique (SQAT) was used to identify and review relevant peer-reviewed journal articles that investigated various facets of cryptocurrency. \nFindings – 54 journal articles were identified from 12 high-quality databases. The findings of the review revealed that most of the studies took place in Europe, North America and Asia, while Africa has been largely ignored. The main focus of cryptocurrency articles has been on a call for regulation of cryptocurrency without much work done on how to mitigate its vulnerability to the financing of terrorism and as a tool for money laundering. Finally, most cryptocurrency articles adopted a single research method – survey. There is a need for future studies to combine a variety of methods so as to gain additional insight into the issues of cryptocurrency’s vulnerability, risk identification and mitigation, regulation and acceptability. \nResearch limitations - The use of limited but high quality academic databases means that some articles were not considered for this review. \nOriginality/value – This study is one of the few studies to conduct a systematic review on a phenomenon which has the potential to transform the global financial landscape.