Cryptocurrencies performance

Ever wondered why in the world where blockchain and cryptocurrency are buzzwords there is no extensive overlook of the projects in this area? We try to cover this knowledge gap with presenting infographics and some conclusions we made on the topic. If you are not sure that it’s what you’re looking for, here’s a formal abstract.

This article contributes to cryptocurrencies-related literature with:

1. an attempt to map existing blockchain projects by merging them into ideologically mutual groups,

2. visualisation of their performance as such groups and comparison of some quantitative attributes,

3. considerations about direction and magnitude of project’s evolution based on abovementioned analysis.

There were many attempts to make useful tools to navigate growing as if it’s on steroids crypto-landscape. Some of them got legendary (, the others are encyclopedic (, and the rest of them pursue each other in attempts to make the best use-cases classification.

We improved 2-level classification from the Alex Lange’s article and distributed top-500 coins and tokens by sectors and sub-sectors that we call categories. By the time of writing, we came up with 30 categories each belonging to one of the 5 sectors: we added the sector “cryptocurrencies” for the coins which may also be considered belonging to one of the other sectors but really are more “paypal-like”. The sectors from Protocols&Infrastructure to Application show natural evolution of the crypto world.

Bitcoin and Ethereum, which hold over 3/4 of total cap and skew all the visualisation, went to public blockchains category. We chose market capitalisation as value measure because of the informational function of price, which theoretically signals users and investors. Also, the data was available at Time span for the data is from August 31 to November 23. You can check which category belongs to which sector in the image at the end of the article; we call that image “the map”.

Below you can see the pie chart of the sectors and the table with their intragroup characteristics, such as average, median and standard deviation of capitalisation of projects. The average is significantly higher than the median for each group, which is a typical distribution of projects: one or two projects have a much larger capitalisation than all the rest. In other words, the majority of such groups in market structure are similar to the whole market in general, where Bitcoins are as huge as rest of the thousands other cryptocurrencies in terms of market cap.

Applications are the dominant group, although the projects in it have the smallest capitalisation. One might anticipate this result because there are a real lot of tokens ranging from poker rooms to tombstone cleaning service. Needless to say that most of them are not finished products yet and few of them really will be. There is also about a hundred of coins that share only PayPal features (some have large blocks, others are PoS-powered), but none of them are going to be rockstars really. Projects in other sectors are distributed quite uniformly.

The violin chart below reveals that the majority of the projects have Market Cap under $50M (the y axis shows market cap in USD). We excluded superstars priced over $1B for better visibility, but they all will appear at the map in the bottom of the article. Flat lines on violin bodies indicate median capitalisation as in table above.

The chart may seem a little messy but it is really quite informative. The violin itself helps to visualise the density of projects; each dot answers to a particular project in the corresponding sector. It helps to understand if you “cut” the violin in half and turn it 90 degrees clockwise: then it will show the density like one is used to see it.

As we can see from above, there are more “heavy” projects in Protocols&Infrastructue sector than in the others. However, Applications and Cryptocurrencies are extremely skewed: there are a few winners with large capitalisation but the vast majority of the projects are not even close to the leaders.

Distribution by category, not by sector, is pretty much the same story, in spite of the fact that we selected so many categories that the pie got quite messy on the bottom.

The following table gives a better insight about the divergence of financial metrics throughout the categories. It is nice to notice that categories merge fairly well into sectors without significantly biasing them as outliers. It suggests that the classification is both interpretative and explanatory.

The structure of the categories shows the first conclusion: crypto projects at the first stage of their development are about finance in the first place, so the heaviest categories are the Cryptocurrencies per se, Public blockchains helping to maintain the transactions, and the service categories like E-banking, E-commerce, Exchanges&Wallets, Identity&Privacy, Tokenized real assets. Although, the last one might be the link to the next stage of evolution connecting the digital and the real worlds or, better to say, eliminating the difference between them.

The market dynamics of September-November 2017 shows 30% decline and almost instant play-back in the share of sectors other than Protocols&Infrastructure, which represents them as emerging ones. One of the possible explanations of these phenomena is the fact that the projects are so much beyond reality (legal and institutional issues don’t seem to be solved) that their price is based mainly on expectations. Furthermore, well-established projects yet have scalability problems to be dealt with (i.e. high fees and endless forks debates, ambiguous pricing of forked coins, wallet hacks, ‘dumb contracts’ etc.).

We believe that capitalisation is interdependent not only on cryptocurrency’s usability and hype around it (fundamental and speculative factors), but is also heavily dependent on market conditions, the topic which we would like to leave for our further stories.

In conclusion, for global thinkers we have the financial map of crypto-landscape, faceted by sectors and separated by categories within. Please note that this labeling was based on personal opinion of researchers and does not claim to be 100% objective (i.e. at glance a project may seem to belong to one group, but when you look closer it often gets clear that it should be in another one).

Author: Vahrameev Klim, 02/10/2017