Behind the Scenes of Bloomberg’s Crypto Asset Vetting Model
A detailed look at how the top 50 crypto assets were selected for the Bloomberg Terminal
Insight into Bloomberg’s crypto asset vetting model
The crypto asset class is rapidly evolving. Unfolding regulation and market maturity are pulling institutional investment capital into crypto. And many traditional institutions investigating this space want the same reliability and transparency they have with data and analytics in more traditional asset classes, such as equities or fixed income, to make informed investment decisions.
On June 9, 2022, the Bloomberg Terminal took a significant step in addressing institutional investor demand by expanding coverage to the top 50 crypto assets. But their standard for inclusion was not as simple as choosing assets with the largest market cap or any other single metric. There are many factors and a wide range of data points to consider.
We spoke with Alex Wenham, Bloomberg’s Digital Assets Product and Strategy Lead, to gain insight into the firm’s approach to this rapidly evolving space.
Wenham and his team started this process with the needs of institutional clients in mind. But there are different types of institutions and they don’t all have the exact needs. In addition to considering the needs of crypto-native operations, niche hedge funds, market makers or family offices, Bloomberg focused on helping more traditional buy- and sell-side customers looking to develop more familiarity and comfort with the crypto market.
Filtering by custody and compliance
In 2021, at the start of this vetting process, more than 10,000 projects were trading on hundreds of different exchanges. Given the large number of assets, the team starts by maintaining a database of the Top 1000 crypto assets by market cap. The team’s next goal was to filter that number down to a list of assets that could broadly meet the needs of the traditional institutional client.
Chief among these requirements were fully compliant crypto custodians. These clients need a custodian following the same regulatory parameters used for traditional asset custody.
Wenham and his team excluded any asset that did not have that custody standard. This filter enabled Bloomberg to filter the pool of 1,000 digital assets below 200.
Next, Wenham’s team evaluated the quality of all trading platforms offering these assets. There are about 550 exchanges that fall into this category, so Bloomberg partnered with Digital Asset Research to help vet the growing list.
Together, they evaluated regulatory compliance, the integrity of trading activity (taking account of items such as bot trading), AML/KYC processes and IT infrastructure. This evaluation organized exchanges into Vetted, Watchlist or Out of Scope categories.
Quality liquidity is another imperative when it comes to institutional trading. Any firm moving large amounts of capital must be confident that it can trade in and out of an asset. Wenham explains, “if an asset only trades on one high-quality platform and that platform goes down, some institutions won’t have a way out.” For that reason, the next part of Bloomberg’s digital asset vetting requires that the asset have adequate liquidity across a minimum of two high-quality (vetted) trading venues.
Market cap and turnover consistency
The previous step filters the vetted digital assets to approximately 150, but Bloomberg’s Vetting process doesn’t stop there. Wenham’s team then ranks each asset by market cap based on circulating supply and turnover consistency.
Wenham points out that you can’t accurately measure market cap without properly accounting for circulation of the supply. “Realistically, you need to understand the market cap of readily available tokens,” he explains.
But market cap alone doesn’t tell the whole story – as it frequently changes in volatile crypto markets. So, the team uses a second measure to balance it out. Once they have an accurate market cap reading, they factor turnover consistency into their ranking process.
Wenham says, “We gauge BTC turnover by first calculating total traded volume (converted to USD) across all trading pairs from reputable exchanges and divide it by the total market cap. If the ratio of volume to market cap is high one day and low the next, the asset would have a low consistency.”
An evolving process
Wenham notes that efforts to refine the process are unlikely to stop here. He acknowledges that crypto is unique, as is much of its reference data, and change is one of the few reliable constants in the asset class. But financial market participants are used to adapting to change.
Another example he points to is liquidity analysis tools and models that have evolved to accommodate the need for speed as fixed-income markets have become more electronic in recent years. They’re a necessary part of the industry, helping an institutional client understand to what extent it would move the market if it had to quickly liquidate a position.
And while “you just can’t plug a fixed income model into crypto,” Wenham clarifies, “you can take a model that works and tweak it to make it more relevant to this asset class.”
Wenham and his Bloomberg colleagues faced the challenge of developing a model to curate crypto assets for the firm’s institutional client base in 2021. Even though they created a cohesive methodology, it’s clear that their work remains an iterative process. They will evolve the vetting standards as the digital assets market continues to convene with the traditional market and its top-tier institutions.
This content is sponsored by Bloomberg.
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