The Blockchain  Through  Behavioral Analysis

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The Blockchain Through Behavioral Analysis

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The blockchain is an amazing view of the internal plumbing of an emerging financial system. We see the transactions happening between large institutions and the exchanges they are trading with, we see the internal transactions of that large institution, we also see the exchanges transferring between themselves.

In the traditional financial world, it is incredibly difficult to see these transactions due to the power of monopolies (Swift, Visa, and MasterCard), opaque financial institutions, and overall complexity.

To be able to paint a real picture, an analyst would need a relationship with a large financial institution (i.e. have lots of money or trade a lot). Even then, it would be limited; the analyst could only request a view of the flows going through the bank in a certain market. The bank would share what they are seeing (perhaps ~5% of volume) in the asset of interest to the analyst. The data would be a small sample set and most likely stale by the time the analyst received it.

The Blockchain has transparency at its core. By downloading the blockchain, we can see all activity happening outside centralized companies like exchanges. Once we have downloaded the blockchain we quickly notice behavioral patterns:

  • Block rewards are being distributed to certain addresses so they must be miners
  • Contracts are being created by other addresses so they must be developers
  • Accounts are inactive for months on end so they must be savers

We can keep segmenting by behavior with manually tagged addresses. When dealing with exchanges we notice they have a unique address with our account, but within hours move Cryptoassets from our unique address to their wholesale (hot) addresses, and sometimes even move to their cold addresses. We can watch this pattern of behavior and try to find others exhibiting similar behavior.

Going through this process we end up with 5 categories

  • Miners: Receive block rewards
  • Whales: Maintain large balances (based on % of supply)
  • Retail: Maintain small balances (based on % of supply)
  • Savers (Cold): Have not sent Cryptoassets in 6 months or more
  • Exchanges: Manually tagged

We can then sum up the Cryptoasset balances of each account to get a better view of what is happening beneath the macro metrics:

Patterns

  • Isolating retail (by double-clicking ‘retail_balance’ on the above chart), shows us that retail balances are heavily influenced by the price of Cryptoassets
  • Isolating whale looks like they could be a leading indicator of the price of Cryptoassets. ETH ‘whale_balance’ bottomed in Nov 2018 and again in Jan 2020, both bottoms preceded a large move up in price. More to come on this in a follow-up post.
  • Isolating cold balance paints a different story. People are generally saving their BTC (80% of BTC balances are in accounts that haven’t sent BTC in 6 months) almost as if they were storing their wealth. Yet, they can still be price sensitive at the extremes. Clearly today’s price is not compelling enough for the savers to become active, unlike the $20k we hit in 2018. Implying price will have to be much higher to pull people out of saving in this bull market. ETH exhibits different patterns: savers are much less of balances (60% vs. BTC at 80%) and are less price sensitive.

Why are we giving this data away?

Covey, has been collecting behavioral data on ETH and BTC on-chain activity. We built this database on publicly available blockchain data and believe in continuing that trend of openness.

This is a small sample of our data and we will be sharing more in the coming months to get feedback and improve our data collection. Stay tuned by subscribing here: www.covey.io

Our entire database is open to strategic partners, check out our website for more detail.

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The Blockchain  Through  Behavioral Analysis was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.