What Are We Measuring? The Importance of Defining Inputs For Crypto Asset Models

The rise of a new asset class births the critical task of discovering new, applicable methods of valuation. Naturally, we want to lean on the tried-and-true techniques of stock valuations or currency valuations, a closer look reveals the broader crypto asset class has too many unique characteristics to fit any old mold. When viewing (the ever growing) published research, we seek to define the inputs for these new valuation models to gauge future price potential, we find these inputs can be as nascent and opaque as the technology we are trying to value.

We are presented with a paradox. While public blockchains provide a plethora of authentic data for an entire crypto network, how to properly define and extrapolate meaning from this abundance of data is still a developing practice. Familiar metrics, such as transaction volume, market capitalization, and velocity that may seem like reliable inputs are incomplete. Ultimately, the reason for this is because crypto networks are fundamentally different than Web 2.0 technology. First, crypto networks are not companies and tokens are not a claim on ownership. The networks are inherently decentralized, in many cases permissionless and do not have equivalent profit-incentives that a traditionally structured business does. Because the market is still young and developing, it still remains to be seen how market participants (investors, users, founders, etc.) behave.

For these reason explored above, we believe that the metrics used in crypto valuations require additional clarity and expanded definitions to capture the new dynamics of crypto market behavior and help the community shift more towards a Web 3.0 mindset.

Muddled Inputs

In our research, some of the most common metrics we’ve come across include transaction volume, market capitalization (or network value), and velocity. We are not taking a stance against the inclusion of these metrics, however; we think it is critical to clearly understand what these metrics are attempting to convey and what may lie hidden behind these assumptions.

Transaction Volume

Transaction volume is a frequently cited data point in most models. It refers to the total number of transactions confirmed on-chain for a particular crypto network, usually measured per day. Bitcoin has shown between 150,000 and 250,000 transactions per day for most of 2018. Transactions per day can be defined by how much activity is happening on a given network, which can indicate things such as a network’s utility and how many payments are being made.

We find that this data can be deceiving for several reasons. At first glance, it can be incredibly difficult to differentiate transactions that are a result of consumptive or speculative use. When viewing transactions per day, usually the data is only referencing on-chain activity, which can vastly understate the real activity happening, as most trading occurs on centralized exchanges. Centralized exchanges keep an internal account of the transaction and aren’t required to post every transaction associated with a trade. With centralized exchanges facilitating tens of billions of dollars of transaction activity daily, this skews the reality of network activity quite a bit. Additionally, reported transaction volumes of centralized exchanges can be dubious, as many likely engage in active wash trading due to a lack of regulations. Layer 2 solutions, like Lightning Network and Blockstream’s recent Liquid Network, can take many transactions off-chain; further skewing on-chain numbers and statistics that could indicate an increase in consumptive use of the underlying crypto asset.

Transaction volume may also be exaggerated due to other factors. Blockchains that follow a UTXO (Unspent Transaction Output) model, such as Bitcoin and many others, often perform transactions in multiple steps. For instance, if I sent .6 BTC to someone else, the blockchain may send them 1 BTC and then allocate .4 BTC back to me. This indicates a volume of 1.4 BTC when, in reality, it was .6 BTC. This obfuscates this vital metric even further. We aren’t the first to raise this, according to the Coinmetrics team, “transaction volume in USD terms is highly unreliable and may be overstated by a factor of 5–10 or more.”

Defining Market Capitalization or Network Value

Total market capitalization, also known as network value, has always been a key metric when evaluating traditional investments. For crypto networks, the market cap (or network value) is calculated by multiplying the latest price of one coin by the number of coins available or float. The number of coins that are available for trading is typically arrived at by calculating all the coins that have been mined or been issued in the case of a pre-mine.

Evaluating real market cap requires additional optics on the nuance in the actions participants can take in the market and how they interact with the asset class. Crypto assets are digital bearing instruments and the process of self-custody through Public Key Infrastructure has provided plenty of headaches via lost or stolen funds for users. These lost coins can be a result of users forgetting private keys, losing access to hardware wallets, destroying a paper wallet, and a myriad of other possibilities.

Because many of the main crypto assets are decentralized payment networks this means no central authority is there to refund tokens or reset accounts, leaving these coins forever inaccessible. This presents a material impact on perceived network value; this reduction in supply influences underlying asset price due to many coin’s deflationary nature. Although these can’t be retrieved some go great lengths to try.

Chainalysis, a leading data provider on the Bitcoin network, estimates that somewhere between 2.3M and 3.7M bitcoins are forever lost, which translate to somewhere between 13–22% of the total supply. In the age of “Control + Z” or “Forgot Password” many individuals struggle with rewiring their behavior and exercising the appropriate amount of caution in their custody practices, these statistics reflect that.

Another noteworthy practice by investors in crypto is the number of coins held by long-term investors, or HODLers. These are users who are primarily utilizing a crypto asset as a store of value with no plans to trade it, no matter how volatile the market is. This translate to a large chunk of total supply being incredibly illiquid, reducing the available float. Founder lock-ups are another practice that mimics this behavior and its impact on the market. Many times these tokens are included in the market cap by data providers, though they are definitely outside the circulating supply. The lack of standardization around reporting and defining what is being measured further murky’s an asset’s attempted valuation.

Velocity Assumptions

One of the most popular valuation models is The Equation of Exchange, made popular by Chris Burniske. The equation is defined as MV = PQ, where V is defined as velocity. Velocity is the number of times a given asset exchanges hands in an economy. We find this equation incredibly problematic if it's applied across all crypto assets. Utility tokens with high throughput may suffer from a “hypervelocity” problem whereas staking tokens for a right to work and receiving non-productive crypto assets will create an entirely different behavior. The type of asset is also incredibly important to note because of the assumption of 5 for the United States Dollar would be incredibly different from CPU, RAM, or bandwidth being circulated as a means of payment around a crypto network.

This is a case where examining and partitioning the behaviors of someone that is holding for speculative purposes like our HODLers mentioned above would be different than someone engaging the asset via trading or rather yet, an individual using it for its actual intended purpose. How does one differentiate the consumptive demand from that of the speculator? How do our assumption shift in a short, medium, and long-term equilibrium?

Conclusion

We appreciate the myriad of efforts being put towards data aggregation and valuation methodology in the crypto space. It is an extremely immature market, where standards are scarce and investment theories have yet to be tested. However, as investors look to place target prices on crypto assets and structure their portfolios, it is important to clearly understand the input composition for a given valuation model and how the author arrived there. Crypto networks’ unique characteristics make it necessary to further define inputs and take into account the competing behaviors of the varying participants to create new derivative concepts to properly capture market dynamics.

Ultimately, good data is the foundation for the intelligent investor and for creating the proper frameworks to accurately assess the market. As such, diligence in research and an evolution to the right “Web 3.0” mindset are vital for long-term success.

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