Understanding counterparties is one of the arts of trading in capital markets. The investor composition of a given asset in terms of demographics, trading activity and even sentiment can be important indicators to predict the behavior of a given asset. In traditional capital markets, investors are constrained to analyses based on price, volume and order book datasets. Not surprisingly, there is an arms race between different quant funds to get access to alternative datasets that can provide an edge in their trading strategies. In the crypto space, blockchain datasets represent a unique source of information in order to understand the behavior of individual investors. Today, I would like to highlight a few interesting blockchain analyses that reveal fascinating metrics about the ownership behavior of crypto-assets.

Qualifying the investor ownership in crypto-assets is a tricky endeavor. After all, what is a good qualifier for an ownership position: Amount? Time? Activity? Before diving into specific ownership analytics, we should start by understanding how to qualify ownership positions in crypto-assets.

Qualifying ownership in crypto-assets is a function of extrapolating human and financial behavior from the key elements of a blockchain such as addresses, transactions and blocks.

There are many ways to describe the characteristics of an investor in a given crypto-asset. Up in his position, excited, active trader, long-term holder, momentum-trend follower are some of the definitions that we often heard to describe investors and traders. When comes to blockchain datasets, there are five fundamental vectors that can be used to understand the ownership composition of a given crypto-asset:

· Financial Position: Are investor realizing gains or losses? Are investors moving large positions or small amounts?

· Type: Are investors individual or institutions? Miners or exchanges?

· Time: Are investors holding for a long time or actively trading?

· Concentration: Are investors accumulating large or small positions?

· Demographic: Geographical composition of the investor-base?

Other ownership qualifiers such as behavioral or informational are also super relevant but they can be derived from the aforementioned qualifiers. Also, keep in mind that we are exclusively referring to blockchain datasets. Other interesting ownership analysis can be derived from derivatives or order book data feeds.

Using the five fundamental dimensions we can derive meaningful ownership analyses from blockchain datasets. Let’s look at a few examples specifically focused on Bitcoin.

IntoTheBlock’s In-Out Money Around the Current Price analysis reveals the different positions of investors in close proximity to the current price. This analysis can be used to infer potential levels of support and resistance.

IntoTheBlock’s Large Transaction analysis measures disproportionally large transactions in a given network. This analysis can be used to anticipate trade positions for a specific crypto asset.

IntoTheBlock’s exchange qualifier analysis reveals which addresses in a network belong to exchanges versus individual wallets. This is an important qualifier to understanding ownership positions in a given crypto-asset.

IntoTheBlock’s UTXO analysis segments individual crypto instances by age. This analysis is helpful to identify unusual money movements across the different segments.

IntoTheBlock’s Ownership by Time analysis qualifies addresses based on their activity as long-term holders or Hodlers, medium-frequency investor or Cruisers and high-frequency investors or Traders. This analysis gives you an idea of time positions of individual investors.

IntoTheBlock’s Concentration analysis segments large individual investors from the rest of the population. This analysis is helpful to understand risks and exposures in a given crypto-asset.

IntoTheBlock’s Exchange Netflow analysis monitors funds moving in and out of exchanges. This analysis can be used to understand the inventory and risk factors in various exchanges.

IntoTheBlock’s East-West analysis segments Bitcoin positions between Asia and the rest of the world. This analysis is helpful to understand geo exposures to a given crypto asset.

These are some of the cool analyses that could help you better understand the ownership composition of a crypto assets. Blockchain datasets provides a unique source of information to understand the ownership composition of a crypto-asset in a way that has no equivalent in other asset classes. We should take advantage of it.