CryptoCompare comes up with a new study on the crypto landscape. The paper, published on 16 October, looks at various ways of grouping over 200 different crypto assets.

CryptoCompare describes itself as the “gateway to the world of crypto currencies”. In its recently published Cryptoasset Taxonomy Report, the London-based company attempts to classify the different types of crypto assets. The authors of the study examine the tokens on the basis of four possible groupings:

What is the use case of the asset?
Is the design suitable to guarantee value retention?
Is the token controlled centrally?
Why should someone hold the asset in question?
Through these categories, CryptoCompare aims to achieve a sufficiently precise taxonomy of the examined assets. The most important results at a glance.

Decentralization

The study focuses heavily on the status quo of decentralization. The surprising result: Only 16 percent of the crypto assets examined can be described as completely decentralized. The authors assess 30 percent as “semi-decentralized” and with a good 55 percent more than half of the investigated assets are classified as centralized.

Different basic ideas

Furthermore, the authors examine the basic calculations of investors that can flow into purchase decisions. Or in the authors’ words: “What is the most prominent reason to hold a crypto asset? Since the list of calculations cannot be estimated conclusively, the authors limit themselves to six possibilities of classification, with the following results:

Access to services – 39.5 percent (including ETH)
Reward potential – 35.5 percent (including GNO)
Profits from hard fork – 4.5 percent (including LTC)
Off-chain cash flow – 3 percent (including PAY)
Value memory – 1.5 percent (all stable coins)
Cash and cash equivalents – 16 per cent (including BTC)
Distribution of the various DLTs
The study also shows the distribution of the various distributed ledger technologies (DLT). The vast majority are classic blockchain technologies à la Bitcoin (48 percent of the assets examined). Together with the ERC-20 tokens, this results in a share of 92 percent. The next largest share (three percent) is accounted for by Directed Acyclic Graph (DAG) technology, which is also used at IOTA.

The result: the established crypto currencies such as BTC, BCH and ETH have a lower concentration within the top 100 wallets than the less common tokens such as EOS or NEM.

Conclusion
CryptoCompare confirms its reputation as a reliable source of data on all aspects of the cryptoecosystem. The data sets appear to have been seriously researched and, with more than 40 illustrations, are mostly clearly presented.

Here and there, however, a few inaccuracies creep in. Thus certain definitions are not kept over the entire length of the paper or only insufficiently explained. Moreover, the division into the different basic ideas for the purchase of the tokens seems arbitrary and could just as well have been done differently. As always, it is worth taking a close look.

The complete Cryptoasset Taxonomy Report can be found here.