We also analysed dark markets using data scraped from the websites. We relied on data collected for a previous study [67] which contains a snapshot of product listings and buyer reviews of several dark markets, namely Al- phabay, Dream Market, Hansa, Traderoute, and Valhalla in the summer of 2017.
For each market, a single snapshot of the full catalogue of their website was scraped in late June to late July 2017. Trading volumes were estimated
from buyers reviews for the listed products, similar to previous work in the literature [2,16]. Every review is taken to correspond to one purchase, even if multiple items were mentioned in the review. Buyer reviews are not mandatory on all markets, thus the resulting estimates represent a lower bound of the number of trades. Through this approach, the data provided the reviews of Alphabay and Traderoute, the last 6 months of Hansa’s reviews, and the last 3 months of Valhalla reviews. In total, the collected data contains almost 1.5M trades.
Vendors list their location which later on was standardised by mapping them to ISO 3166 country codes. Based on the product’s title and category, products were recategorised to wider category. Products labelled under terms such as “bud”, “weed”,“hash”, “cannabis”, “cannabis concentrates”, or similar were labelled as Cannabis. Finally products that were labelled or title contained “heroin”, “morphine”, or “opium” were titled Opiates. In Chapter 6 we investigate the ability to predict drug sales using Wikipedia page views. For this analysis, it is important to investigate the data sta- tionarity. Drug sales on the darknet have risen over time. This means the sales data is nonstationary, which is problematic for assessing time series model performance [166]. Figure 3.8 shows global darknet sales for MDMA where the sales over time are growing rapidly, so they are not stationary. We formally test for stationarity in Section 7.3.1.
Jan
2016 Apr Jul Oct 2017Jan Apr
0 500 1000 1500
sales
FIGURE 3.8: Darknet MDMA sales over time. The weekly MDMA sales across the5dark markets.
The sales timestamps are continuous, so we could conduct our analysis at different levels of time aggregation. The higher frequency the aggregation, the more granular the measure of drug demand would be. However, higher
frequencies make the sales data sparser with more zero observations (see Figure 3.9).
To manage this trade-off, we aggregate the sales data to monthly frequency, which is still much more frequent than the annual official drug surveys (for more details see Section 7.1).
A potential limitation of the scraped review data is that it only captures drug listings that were still available in June-July 2017. If a vendor were to create a listing and remove it before that point, we would not observe any of the sales in the scrape. We could reduce the impact on our analysis by limiting our data to be as close to the scraping period as possible. For example, if we only consider sales from May - July 2017 then there would be far fewer removed listings. However this would also reduce our sample size. Instead, we use all available data for our analysis and assess the impact of restricting the sample period.
FIGURE 3.9: Further analysis of sparsity in the drug sales data. The distributions of the percentage of changes in drug
demand at daily (A), weekly (B) and monthly f(C) requencies.
The higher frequencies are problematic because the data is more sparse. For example, if we aggregate to daily frequency then 18% of percentage changes are zero. For weekly frequency, this falls to 8% and for monthly to 3% are zeroe. The analysis
4 Evolutionary dynamics of the
cryptocurrency market
Cryptocurrency market at October 2019 included more than 3000 cryptcour- rencies and had a value of∼240 billion dollars. All cryptocurrencies share the underlying blockchain technology and reward mechanism, but they typi- cally live on isolated transaction networks. Many of them are basically clones of Bitcoin, although with different parameters such as different supplies, transaction validation times, etc. Others have emerged from more significant innovations of the underlying blockchain technology [97] (see Chapter 2).Bit- coin currently dominates the market but its leading position is challenged both by technical concerns [167,168,169,38,79] and by the technological improvements of other cryptocurrencies [170], see more details on Bitcoin challenges in Section 2.1.1.
Despite the theoretical and economic interest of the cryptocurrency mar- ket [25,171,172,173], however, a comprehensive analysis of its dynamics was lacking. Existing studies have focused either on Bitcoin or on a restricted group of cryptocurrencies (typically 5 or 7) of particular interest (see Chap- ter 2). But even in this case there is disagreement as to whether Bitcoin dominant position may be in peril [97] or its future dominance as leading cryptocurrency is out of discussion [15].
Here we present a first complete analysis of the cryptocurrency market, considering its evolution between April 2013 and May 2019. We first analyse the period from April 2013 until May 2017. We focus on the market shares of the different cryptocurrencies (see Section 4.1) and find that Bitcoin has been steadily losing ground to the advantage of the immediate runners-up. We then show that several statistical properties of the system have been stable
for the past few years, including the number of active cryptocurrencies, the market share distribution, the stability of the ranking, and the birth and death rate of new cryptocurrencies. We adopt an “ecological” perspective on the system of cryptocurrencies and notice that several observed distributions are well described by the so-called “neutral model” of evolution [174,175], which also captures the decrease of Bitcoin market share. We believe that our findings represent a first step towards a better understanding and modelling of the cryptocurrency market.
Finally, in Section 4.6 we extend the results to the period from May 2017 untill May 2019, reflecting on our results after the work publication in December 2017. We show that despite the prices fluctuation, the market is still well described by the neutral model. On the other hand, ranking dynamics are becoming more stable. The work presented in this chapter is based on publication [I].
4.1
Materials and methods
Cryptocurrency data was extracted from the website Coin Market Cap [4]. The website has changed the definition of some of the measurement (see Section 3.1 for more details on the changes) ; however, these changes did not impact our results. The dataset which covers the period from April 28, 2013 up to May 13, 2017 was extracted before the changes. Results shown in Section 4.6 rely on dataset following the new measurements.
For the first dataset, the website collected data from 157 exchange markets. Now the website provide data relying on 285 exchanges. For all active cryptocurrencies, the website provides the market capitalisation, the price in U.S. dollars and the volume of trading in the preceding 24hours. Data on trading volume was collected starting from December 29, 2013.
The website lists cryptocurrencies traded on public exchange markets that are older than 30 days and for which an API as well as a public URL showing the total mined supply are available. Information on the market capitalisation of cryptocurrencies that are not traded in the 6 hours preceding the weekly release of data is not included on the website. Cryptocurrencies inactive for
7 days are not included in the list released. These measures imply that some cryptocurrencies can disappear from the list to reappear later on.
Thecirculating supplyis the number of coins available to users. In the second dataset the website update the calculation of the market capitalisation to consider the dormant coins, see Section 3.1 for more details. Thepriceis the
exchange rate, determined by supply and demand dynamics. The market
capitalisationis the product of the circulating supply and the price. Themarket shareis the market capitalisation of a currency normalized by the total market capitalisation.