Do the rich get richer? An empirical analysis of the BitCoin transaction network

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze BitCoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions, and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling relation between the degree and wealth associated to individual nodes.Comment: Project website: http://www.vo.elte.hu/bitcoin/; updated after publicatio

Explorations in Evolutionary Design of Online Auction Market Mechanisms

This paper describes the use of a genetic algorithm (GA) to find optimal parameter-values for trading agents that operate in virtual online auction “e-marketplaces”, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agent-based e-marketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auction-types explored by the GA includes the Continuous Double Auction (CDA) mechanism (as used in most of the world’s financial exchanges), and also two purely one-sided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden “shock” changes partway through the evaluation process, two-sided hybrid market mechanisms can evolve which may be unlike any human-designed auction and yet may also be significantly more efficient than any human designed market mechanism

Collective Dynamics of Dark Web Marketplaces

Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving drugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and several police raids and scams have caused large losses to both customers and vendors over the past years. However, this uncertainty has not prevented a steady growth of the dark market phenomenon and a proliferation of new markets. The origin of this resilience have remained unclear so far, also due to the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark market ecosystem re-organises following the disappearance of a market, due to factors including raids and scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets of 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD. We show that coordinated user migration from the closed market to coexisting markets guarantees overall systemic resilience beyond the intrinsic fragility of individual markets. The migration is swift, efficient and common to all market closures. We find that migrants are on average more active users in comparison to non-migrants and move preferentially towards the coexisting market with the highest trading volume. Our findings shed light on the resilience of the dark market ecosystem and we anticipate that they may inform future research on the self-organisation of emerging online markets

On the Activity Privacy of Blockchain for IoT

Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting unregistered devices or traffic patterns. In recent years, blockchain has received tremendous attention as a distributed trustless platform to enhance the security of IoT. Conventional device identification methods are not directly applicable in blockchain-based IoT as network layer packets are not stored in the blockchain. Moreover, the transactions are broadcast and thus have no destination IP address and contain a public key as the user identity, and are stored permanently in blockchain which can be read by any entity in the network. We show that device identification in blockchain introduces privacy risks as the malicious nodes can identify users' activity pattern by analyzing the temporal pattern of their transactions in the blockchain. We study the likelihood of classifying IoT devices by analyzing their information stored in the blockchain, which to the best of our knowledge, is the first work of its kind. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90\% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20%

Tracing Transactions Across Cryptocurrency Ledgers

One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have evertaken place, is globally visible. As one consequenceof this degree of transparency, a long line of recent re-search has demonstrated that even in cryptocurrenciesthat are specifically designed to improve anonymity it is often possible to track money as it changes hands,and in some cases to de-anonymize users entirely. With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether ornot money can be traced as it moves within the ledgerof a single cryptocurrency, but if it can in fact be tracedas it moves across ledgers. This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades. In this paper, weuse data scraped from ShapeShift over a thirteen-monthperiod and the data from eight different blockchains to explore this question. Beyond developing new heuristics and creating new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whetherthey serve a criminal or a profit-driven agenda.Comment: 14 pages, 13 tables, 6 figure

A value at risk analysis of credit default swaps

We study the risk of holding credit default swaps (CDS) in the trading book. In particular, we compare the Value at Risk (VaR) of a CDS position to the VaR for investing in the respective firm's equity. Our sample consists of CDS – stock price pairs for 86 actively traded firms over the period from March 2003 to October 2006. We find that the VaR for a stock is usually far larger than the VaR for a position in the same firm's CDS. However, the distance between CDS VaR and equity VaR is markedly smaller for firms with high credit risk. The distance also declines for longer holding periods. We also observe a positive correlation between CDS and equity VaR. -- Kreditderivate wie Credit Default Swaps (CDS) haben in den letzten Jahren den Handel mit Kreditrisiko signifikant vereinfacht. Ein standardisiertes Kontrakt-Design, niedrige Transaktionskosten und eine große and heterogene Gruppe von Marktteilnehmern haben dazu beigetragen, dass CDS die Benchmark - Funktion für die Preisbestimmung im Markt für Unternehmens-Verschuldung erreichen. Heute ist der CDS das am meisten gehandelte Kreditderivat. Wir analysieren das Risiko von CDS, die im Handelsbuch gehalten werden. Wir vergleichen den Value at Risk (VaR) der CDS Position mit dem VaR für eine Position in der Aktie der gleichen Firma. Unsere Stichprobe umfasst CDS ? Aktien Paare für 86 aktiv gehandelte Firmen im Zeitraum von März 2003 bis Oktober 2006. Wir finden, dass der VaR der Aktie meistens den VaR der CDS - Position deutlich übersteigt. Die Distanz zwischen dem CDS - VaR und dem Aktien - VaR ist jedoch bei Firmen mit hohem Kreditrisiko deutlich geringer. Die Distanz sinkt auch bei längeren Haltedauern. Wir beobachten weiter eine positive Korrelation zwischen dem CDS - VaR und dem Aktien - VaR.Credit default swap,Value at Risk,Capital structure arbitrage

Regulatory Implications of Monopolies in the Securities Industry

Since the mid-1990s, investors and regulators have benefited from a high degree of competition in the Indian securities industry. Even more than all the policy changes that have taken place, it is technology and competition that have transformed the Indian capital market in the last 7-8 years. This paper shows that there is now considerable evidence that critical elements of the Indian securities industry are becoming significantly less competitive than in the past. Reduced competition would remove the single most important driver of capital market modernisation in this country and would create several serious regulatory problems. The paper argues that rather than applying the traditional solution of “regulated monopolies”, regulators need to adopt strong measures to stimulate competition. The regulator must also ruthlessly discard those elements of the regulatory regime that are anti-competitive in nature.