Some Basic Indicators that will Reveal Surprising Network-Economics About Crypto-Assets
The analysis used in this article was generated by the IntoTheBlock BETA platform.
Crypto-assets are a new asset class that brings new set of fundamentals. While the behavior of crypto-tokens shares many commonalities with other asset classes, they also bring together very unique factors that have no equivalent in other asset classes. As a result, the analysis of crypto-assets typically requires new statistical indicators that factor in the native characteristics of cryptocurrencies and tokens. Among those characteristics, network economic metrics are leading indicators of the behavior of crypto-assets. The traditional way to look at network metrics in crypto-assets is by counting the number of transactions and addresses related to a specific crypto-token but that metric only scratches the surface of what the network dynamics in a crypto-token. Today, I would like to showcase some interesting variation of that analysis that can yield super interesting insights about the behavior of a crypto-asset.
Network-Economics and Crypto-Assets
One of the differentiated characteristics of crypto-assets is that they live and operate on networks of nodes that represent the financial participants in the lifecycle of the asset. Every transaction in a crypto-asset is mathematically, and therefore economically, dependent on the composition of the network and the interactions between its nodes. In addition to traditional wallets/addresses, a crypto-network will include nodes that perform specific roles such as transaction validation(miners), address obfuscation(mixers), asset-custody(cold wallets), exchange-transactionality(hot wallets) and many others. A typical transaction in a crypto-asset might touch upon one or many of those nodes before its completion. Therefore, when comes to crypto-assets, a healthy network is a highly correlated indicator to the health of the asset itself.
The influence of network dynamics and economics in a crypto-asset is different whether we are talking of tier 1 blockchains or tier 2 tokens. In a tier 1 blockchain, the composition and activity of the network is a clear indicator of both the financial health as well as the transaction processing dynamics of a crypto-asset. There are other factors such as blocks or hash-rate that are relevant in tier 1 blockchains that have no equivalent in other crypto-assets. A tier 2 token is clearly dependent on an underlying network for its transaction processing dynamics but the correlation in terms of financial health is not that clear. From that perspective, it is important to contextualize the characteristics of the token when analyzing network-economics in a crypto-asset.
The Traditional Way
Addresses and transactions are the fundamental indicators for network-economics in a crypto-assets. If you’ve ever used an crypto-asset analytics tool, you have probably seen address and transaction counts graphs like the following.
While those charts are somewhat helpful, they provide an incomplete and often misleading perspective of the network behavior of a crypto-asset. To understand a the network economics of a crypto-asset we need to look a bit deeper.
There are dozens of relevant indicators that can provide relevant insights about the network-economic of a crypto-asset. Instead of starting with boring statistical formulas, let’s think about some of the key questions you have when thinking about network economics in a crypto-asset.
· Is the network growing and how fast?: The number of new addresses coming into a crypto-network is often more important than the total number of addresses.
· Is the network shrinking and how fast?: The number of addresses that abandon the network is important to understand its composition.
· Are new addresses actively transacting?: In the context of a crypto-network, addresses that actively transact are more relevant than those that don’t
· How is the network geographically distributed?: Some crypto-networks have worldwide distributions while others are constrained to a few countries. This information is relevant for traders in order to understand the geographic distribution of their counterparties.
· What’s the time between transaction in a network?: Understanding the transaction spread during the trading day provides interesting insights about the activity of a crypto-network.
The answer to some of those questions can be derived by just looking at the volume numbers of the crypto-asset and requires to analyze the underlying blockchain. However, once we do that, there are some basic indicators that can be used to answer the previous questions:
· New Addresses: Number of new addresses added to the network during a specific timeframe.
· Active Addresses: Number of addresses that are actively transacting during a specific timeframe.
· Zero-Balance Addresses: Number of addresses whose balance goes to zero during a specific timeframe.
· East-vs-West: Number of transactions that happen in Asia versus other parts of the world.
· Average Time Between Transactions: Average time elapsed between transactions in a given timeframe.
The combination of the aforementioned indicators yield remarkable insights about the behavior of crypto-assets. For instance, the address analysis of Basic Attention Token shows a healthy picture with new addresses regularly joining the network. Its important to highlight that the number of new address in BAT regularly surpasses the addresses leaving the network. This means that the BAT network has been consistently growing healthier over time.
Tokens like Polymath don’t present the same clearly health picture of the health of its network.
Looking at the East vs. West indicator for a worldwide network like Litecoin surprisingly shows a larger adoption in non-Asian countries. As an investor in Asia, this picture might concern you as a lot of the trading happens while you are sleeping.
The same indicator for Ethereum shows a well balanced network but with a slightly superior Asian adoption.
Analyzing the time between transactions for a stable coin like Tether shows an average time of about 20 seconds between transactions in the last month. As a trader this shows a healthy level of activity that will allow you to get on or off Tether in a few seconds.
The same analysis for TrueUSD shows a five times larger interval between transactions.
Leveraging statistical and machine learning models in blockchain datasets reveal incredible insights about the network-economics of cryptocurrencies and tokens. Taking the analysis of counting transactions and addresses a level deeper and adding strong qualifiers helps to contextualize the analysis of network economics of crypto-assets and provide more meaningful information for traders and investors.