The one underlying theme that we have seen in the myriad valuation attempts of cryptocurrencies is the all too common, proverbial “we are still too early”. Valuations, which is referring to the exercise of running financial models in excel based on numerous factors that are subject to other subjective exercises of 拍脑袋 (a Chinese way of saying pulling numbers out of your ass), are always early considering the fact that we are running models out to 10 years later in some cases. Early in the sense of “when mass adoption?” and/or early in the sense of lack of adequate historical data. The latter is definitely a concomitant of the former but there is a reason why modeling crypto valuations is important in which I’ll get to in a second.
The second crypto bull run was the solidifying of the word “blockchain” in mainstream media and also the large attraction of traditional finance people into the world of crypto. And what they brought with them was the frameworks (outdated some could say) and theoretical thinking behind how to value these novel digital assets that are borne out of the internet. At the time, these digital assets were more or less penny stocks (some could argue that they still are) but they were enough to capture the attention of some that wanted to justify their prices.
In this piece, I would like to highlight some notable crypto valuation frameworks that caught the crux of attention during the second bull run in crypto and then hopefully seek out what frameworks are being used to this day to deduce a valuation of a cryptocurrency.
Before we dive in, I want to first acknowledge what we already know, which is that, any valuation model isn’t run for the sole purpose of predicting accurate prices. Equity research reports done by numerous young witted minds have gotten bad rap for just blowing hot air. The same may more or less could be said of crypto valuations but to side step and point back to my question posed in the title of this article, I’d say it is neither an exercise for shilling nor futility, but rather an exercise to work out the kinks and incentives of crypto token models in bolstering adoption. Interestingly enough, I’m sure Satoshi never ran a valuation model for bitcoin, but working out an economic model as perfectly crafted as that of Bitcoin is an effort worth trying.
Also, keep in mind that it’s easy to conflate crypto valuation frameworks and tokenomic frameworks (and there’s a ton of these out there as well), but this piece will focus more on the former. And let’s not misconstrue these valuation frameworks for being price targets, one could say these are isomorphic, but there are nuances to its intentions.
Chris Burniske’s INET Model and Update
By far the first attempt at a full fledged valuation framework to gain traction has to be Chris Burniske’s (Partner at Placeholder) INET Model published back in 2017. Although the article itself has amassed close to 17,000 claps on the original article’s Medium post, a few iterations have been made to the original framework which more or less still leave the framework open ended; considering the arduous task of properly adapting to what we envision digital assets to be.
Published over 3 years ago, the model is still considered one of the more clear cut ways to view a crypto asset’s yearly utility value and its present value based on those future expected utility values that are derived from the crypto asset’s market penetration of an existing market. Very similar to a typical cash flow model, but with nuances that includes fitting an S-curve growth or “Take Over Time” to account for initial mainstream adoption, using the equation of exchange in place of revenues/profits, and a float value after Bonders and Hodlers.
Even though there have been many attempts before at valuing bitcoin and all its intricacies, Burniske’s INET model has been, hands down, the general valuation gateway to crypto assets and without a doubt, a lure for others to stab at the model’s overlooked faults. His follow up to this piece, which was published in 2019, attempted to bifurcate what we think of cryptoassets to be: either in the buckets of being Capital Assets, Consumable/Transformable Assets, or Store of Value Assets.
Despite Burniske’s framework being such an immediate hit, many drilled down into his unintentional mistake of keeping velocity, in the equation of exchange, linear or correlated 1:1 with growth.
Alex Evans’ Velocity Approach
The next iteration, which is more of an adaptation to Burniske’s framework is coincidentally, Alex Evans, who joined the reigns at Placeholder with Burniske back in 2018. A UVA econ grad who felt first hand the vehement effects of weak economies in Greece during the recession, Alex proposes that velocity should always be dynamically changing and should “model endogenous velocity as time-varying and distinct from the money supply term”. The issues with velocity has been an ongoing debate with many attempted velocity thesis being published with the overall consensus being: high velocity bad, low velocity good, with the conclusion consisting of non-tested proposals on how to lower velocity.
Evans tries to dissect the individual factors affecting velocity by leveraging the use of Baumol-Tobin’s “cash inventories” approach, which essentially dictates consumer behavior with how much cash they are willing to hold on hand. Or specifically the tradeoffs “between the liquidity provided by holding money (the ability to carry out transactions) and the interest forgone by holding one’s assets in the form of non-interest bearing money.”
By taking the derivative of the total cost function and obtaining the cost-minimizing value of N, which corresponds to the # of transfers each year taken by a user to purchase a token, then taking this cost-minimizing N function back into the average money balance (Y/2N) to obtain the average token holding, or money demanded.
“We can thus say that VOLT ‘money demanded’ is equal to the cost-minimizing VOLT balance that users hold each year, which is a function of the GDP that the VOLT economy facilitates, the expected rate of return on the store-of-value asset, and the cost per transaction.”
you’re confused, don’t worry. You’re not the only one.
Wang Chun Wei and Bonnie Yiu’s Equilibrium Valuation
The next valuation framework I would like to highlight is a bit more simplistic in the sense that it uses something that we’ve all been taught in our economics 101 course: supply and demand curve equilibrium. This piece, published in 2018 by Wang Chun Wei and Bonnie Yiu of Consulere, is a very underrated framework. In essence, they introduce market clearing conditions by setting both a supply and demand curve equal in terms of the price of a transaction in the native tokens.
When the two equations above are set equal to each other you can then solve for the equilibrium price (# of tokens required for a service) and the quantity (units of service demanded).
As with the case of many crypto valuation frameworks, this model also utilizes the Quantity Theory of Money, or Equation of Exchange, in solving for the optimal token value. Taking P(token)* and Q* from above and substituting them into the MV=PQ equation, you can then solve for X, which is the notation for the exchange rate for tokens to 1 USD, or token value. This could get a bit convoluted to some but more crypto valuation frameworks tend to be. And may I add that this model also is victim to the velocity issue as both writers note “Increasing token velocity reduces token value.” In some regards, “this thesis is directionally correct, but hard to operationalize”, as Alex Evans puts it.
On the Velocity Issue
By now you can see an underlying theme of the above valuation frameworks in which they all center on the velocity issue. The core issue that was proposed eloquently by Kyle Samani, Managing Partner at Multicoin Capital, is simply that tokens that experience high velocity, while being linear with transaction volume, will induce zero network value per the equation below:
Average Network Value = Total Transaction Volume / Velocity
But as Alex Evans and a handful of others have thrown rebuttals, velocity should not be assumed linear but rather dynamically dependent on other factors. The velocity issue could really be laid out in its own separate report so I’m going to avoid diving further, but will leave it on this quote shared to us at PANews by Kyle Samani who still firmly stands by his thesis:
“I think that among most sophisticated entrepreneurs and investors, it is now widely accepted that the velocity problem is real, even if we don't have strong evidence in the market that medium-of-exchange tokens (which are the kind that are subject to the velocity problem) cannot sustain value (for example, XRP is still #4 on CoinMarketCap).” - Kyle Samani, Managing Partner at Multicoin Capital
TokenInsight’s exchange token valuations
TokenInsight has been one of the few teams actually implementing a valuation framework to deduce a target price for a token. Considering exchanges are one of the few businesses in the industry making money, their respective native tokens have been prime examples of utilizing a Discounted Cash Flow methodology (specifically the H-Model) considering that they more or less align with how a security token would behave. And it’s utterly crucial that exchanges have been reporting their financials similar to how a public company would.
Taken from their recent August 2020: Exchange Token Valuation Report, TokenInsight could probably take the gold standard of creating a crypto “Fitch ratings” style of valuation. With a hybrid approach of combining a DCF value along with using a comparables valuations, TokenInsight has given it a more holistic approach, not to mention their qualitative factors for leveraging a grade based rating.
There are other examples of exchange token valuation frameworks such as the one by Decentral Park Capital, in which they emphasize these tokens by their Price to Assets ratio.
Overview of Token Valuations
The list below is a good snapshot of the more notable pieces of valuing cryptocurrencies. It’s not a complete exhaustive and exclusive list by any means, but a great starting point for anyone who dares to build upon these frameworks for cryptocurrencies.
As most of the above aforementioned crypto valuation frameworks were published around 2017, there really hasn’t been any new groundbreaking frameworks put forth since. But there are a handful of funds that occasionally do put out their own valuation piece, albeit with muted fanfare.
Have the swarm of changes we’ve seen in blockchain, DeFi, the rotation of market leaders, etc. have inadvertently positioned these frameworks obsolete? Are we still trying to find the right rational frameworks to value irrational markets? Regardless of whether these exercises are formulating an accurate crypto valuation, the value of consistently molding and tinkering the way we approach it is something we shouldn’t debase.
As stated in the beginning, these exercises have been a way in rethinking token models and accentuating faults that may have not been noticed. This can lead us to better questions such as, is it the token’s velocity or the protocol’s staking incentives that we should dissect further? How should we divvy up capital allocation in a fair manner? Or does it stem from the way tokens are originally minted and burned? Or instead of burning tokens should protocols “buy-back and make” as proposed by Placeholder?
“There is no one size fits all model for crypto valuations, and valuations are complicated, especially in the crypto market. As we are still in the early stage in this fast-moving crypto financial market, I think we need to attract significant more inflow of a diverse range of institutional money such hedge funds, banks, asset managers, mutual funds, etc. to drive the crypto market, in general, to be even more efficient.” - Johnson Xu, Director of Research at Huobi DeFi Labs and Former Head of Research at TokenInsight, told PANews.
For the newcomers that carry a finance background, stabbing at these through the lens of a CFA curriculum is a natural inclination. But to the event we want to venture down this road of price prediction based on fundamental drivers, the below points will eventually need to show fruition before we end up swerving into futility:
-More reporting and transparency around token projects
-More historical data (this one is obvious)
-More competitors to the existing market leaders
-Better data usage of layer 2 applications
-More HODLers of other cryptocurrencies
Although the above suggestions may or may not be in conflict with the original ethos of crypto, mass adoption is going to need more structure and frameworks rather than going at it in a haphazard manner.
Thanks to Kyle Samani, Managing Partner at Multicoin Capital and Johnson Xu, Director of Research at Huobi DeFi Labs for their insights on this subject matter.