The Elephant in the Crypto-Analysis Room: Counterparty Intelligence
Chief Scientist, Managing Partner at Invector Labs. CTO at IntoTheBlock. Angel Investor, Writer, Boa
There is nothing more important on a trade than understanding who will be on the other side of it and, yet, is so difficult. Understanding the investor composition of an asset class has long been considered the holy grail of analytics in financial markets. If we can get insights into the positions of different investors on a given asset, we can better understand the risks and timeline of specific trades. Financial markets typically refer to this type of intelligence as counter-party analysis and is a fundamental element of any investment methodology. However, clear counter-party analysis is next to impossible to achieve in traditional asset classes as they are operating in fragmented markets in which investors interact via intermediaries.
Crypto-assets are the first asset class to bring a decent level of investor behavior transparency as a significant percentage of the trade activity is recorded in some form in public ledgers. Even if that on-chain information does not cover 100% of the asset behavior, it is statistically-relevant enough to provide unique insights about the investor/counter-party composition of a specific crypto-asset. Blockchains are more than a settlement and ownership transfer layer for crypto-asserts. By recording individual transfers, blockchains represent a unique source of information for counterparty analysis that is unprecedented in financial asset classes.
What Makes Counter-party Analysis so Difficult in Traditional Markets?
In traditional asset classes, understanding the investor composition of a specific asset is nothing short of a nightmare not to mention impossible. While there are many factors that contribute to this challenge, they can be summarized into two main groups:
· Market Fragmentation: Asset classes such as stocks, bonds, commodities or currencies trade across global markets that maintain their own liquidity pools. As a result, the investor information of a specific asset is fragmented across different financial markets making it impossible to analyze.
· Obfuscation by Intermediaries: Intermediaries such as broker dealers obfuscate the behavior of different investors behind centralized entities introducing a lot of obscurity when comes to analytics. From that perspective, is next to impossible to understand the financial composition of an asset class in which the behavior of investors is hidden behind intermediaries.
Given the lack of transparency about investor behavior in traditional asset classes, traders rely third parties that assess risks of specific trades based on different data sources. Those risk analyses are similar to a credit score model which remain relatively subjective and typically yield overly simplistic assessment of a counter-party.
Why Crypto is Different?
Having part of the transaction history of an asset class in public ledgers opens a universe of possibilities to understand the behavior of crypto-assets. Specifically, there are several dimensions of counter party analysis that can be revealed by digging deeper into blockchain datasets:
· Risk: Understanding the risk levels of certain positions based on the counter-party distribution.
· Liquidity: Analyzing the liquidity levels of investors based on price movements as well as historical trades.
· Investor Sentiment: Understanding the investor psychological disposition based on past trades, gains or losses.
Each one of those factors can be represented by dozens of analytics that interpret blockchain information. Let’s look at several examples.
Examples of Counter-party Intelligence for Crypto Assets
Analyzing blockchain datasets can reveal fascinating insights about the investor composition and disposition on a specific crypto assets. The following examples illustrate some of the initial counter-party analysis we have done in the IntoTheBlock platform
Financial Positions of Investors
Financial gains and losses are one of the dominant factors that determines the behavior of investors in a specific crypto-assets. The In-Out of the Money Analysis leverages machine learning to group investors based on their financial position depending on price. In the case of Ethereum, the analysis reveals that a significant percentage of the investor population is out of the money which might introduce friction in a potential price rally.
Exposure to Whales
Assets that are dominated by a small group of investors are vulnerable to market manipulation. The whales analysis identifies addresses with disproportional large positions as well as their trading activity. In the case of Litecoin, we can see that the concentration is not critical but, more importantly, the current whales population is not actively trading.
Old Money vs. New Money
New investors are an important element of price rallies in a crypto-asset. The HOLDLRs vs. TRADERs analysis evaluates long investors versus new money coming into a crypto asset. That analysis in Bitcoin reveals that there is a lot of new investors deploying capital into Bitcoin which is contributing to the recent price rallies.
Understanding the geographic distribution of investors is another important factor for quantifying counter-pary risk. If you are an investor based on Beijing, you should think about it twice before investing on an asset that trades mostly in US EST time. The East vs. West analysis segments transactions that happen in Asian time zones versus the rest of the world. The analysis Tether reveals that the stablecoin is traded predominantly in Asian markets.
Counter-party analysis in crypto assets is far from being an easy endeavor. The noise introduced by centralized exchanges, the obfuscation of identity of different actors introduces noise that should be considered when evaluating these metrics. Regardless, blockchain datasets reveal statistically relevant counter-party information that could add a different dimension to the investment in crypto-assets.
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