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APPROACH 1: TOTAL ADDRESSABLE MARKET
The most popular approach to value cryptoassets is to estimate their addressable markets and compare that estimate with their current market capitalization.
For instance, many people believe that bitcoin is competing with gold as a nonsovereign store of value. At current prices of roughly $1,762.00 per ounce, the total stock of gold held above ground amounts to approximately $13 trillion.
As we have noted, the maximum number of bitcoin that will ever be available is 21 million. And so, the thinking goes that if bitcoin matches gold as a nonsovereign store of value, each bitcoin would be worth roughly $620,000 (on a fully diluted basis); if bitcoin captures 10% of the gold market, each bitcoin would be worth roughly $62,000; and so on. With its current market capitalization of roughly $400 billion bitcoin captures less than 4% of the value stored in gold.
The clear advantage of this approach is its simplicity. It is easy to understand and provides a solid framework for considering order-of-magnitude comparisons between cryptoassets and the markets they address.
This approach also makes introducing additional use cases easy. For example, one can consider that bitcoin is going after not only the gold market but also the entire “store-of-value” market. In that case, one can add offshore assets, parts of the real estate market, art, negative-yielding bonds, and other potential markets to the mix. This would increase bitcoin’s target market by multiple tens of trillions of dollars.
However, while directionally helpful, this type of back-of-the-napkin valuation exercise falls short in many ways. To start, it provides at best a rough estimate of the order of magnitude of value that a cryptoasset might attain. It also supposes that bitcoin will create a new store-of-value market, above and beyond the existing gold market.
Additionally, beyond bitcoin and other store-of-value use cases, comparative valuation metrics hold little meaning. If Ethereum is going after the programmable money use case and competing with the broader financial industry, how do you estimate the size of that market? Even for the payments use case, this calculation is significantly challenging.
APPROACH 2: THE EQUATION OF EXCHANGE (MV = PQ)
A widely discussed alternative valuation model was proposed by Chris Burniske, a crypto researcher and partner at the venture capital firm Placeholder Ventures, and Jack Tatar, managing partner of Doyle Capital, in a book called Cryptoassets: The Innovative Investor’s Guide to Bitcoin and Beyond.
Burniske and Tatar’s framework is widely referred to by the monetary equation of exchange that drives its calculation:
MV = PQ.
The equation is borrowed from traditional models of valuing currencies and is based on the assumption that a currency’s value is related to the size of the market it supports and to its velocity as it moves through that market. The definition of M, V, P and Q in both traditional monetary economics and cryptoasset markets.
These numbers can be estimated for some point in the future for a mature market and then discounted into present value. As an easy example using round numbers, let us assume bitcoin will process 100 billion transactions (Q) of $100 each (P) per year. Then P × Q= 100 billion × $100 = $10 trillion per year. If on top of that we assume that bitcoin has a velocity of 5 (in other words, on average, one bitcoin changes hands five times per year), we arrive at a potential market capitalization of $10 trillion per year/5 per year = $2 trillion. If we divide this number by the fully diluted amount of bitcoin outstanding (21 million), it yields a price target of $2 trillion/21 million, or $95,238 per bitcoin. If we assume further that this level will be achieved in five years, we can discount this amount by an appropriate rate and arrive at an estimated present value.
One important challenge with this approach is that it requires estimating velocity, which is notoriously hard to do—even for a stable currency such as the US dollar—and velocity has historically varied significantly over time. According to data from the Federal Reserve,one key measure of money velocity (MZM) has ranged between 0.9 and 3.5 over the past 30 years; cryptoasset velocity is likely to vary more. Small changes in this estimate can lead to very large changes in proposed valuations.
APPROACH 3: VALUING CRYPTOASSETS AS A NETWORK
A third approach to valuing cryptoassets is borrowed from “Metcalfe’s law,” a popular theory in technology that states that the value of a network is proportional to the square of the number of participants. If you consider a social network, such as Facebook, Instagram, or LinkedIn, for instance, its value when it has a single user is zero. If, however, a second user is added, the network becomes valuable. As more users are added, the network’s value grows.
A key part of Metcalfe’s law is that the value of the network is not linearly related to the number of users but is instead related by a square function. In other words, if the value of the network of two users is expressed as “4” (2 squared), the value of a network with four users is 16 (4 squared)-four times as large.
Metcalfe’s law has been used to value social networks with some degree of accuracy.Ken Alabi first proposed applying Metcalfe’s law to the valuation of cryptoassets in his 2017
paper “Digital Blockchain Networks Appear to be Following Metcalfe’s Law. Using the number of active daily users participating in the network, Alabi showed that the valuation differences between certain cryptoassets (he used bitcoin, Ethereum, and Dash) can be explained with a high degree of accuracy.
The Metcalfe valuation method makes intuitive sense, given that daily active users are a proxy for interest in and adoption of a cryptocurrency. Among its key limitations is that it is appropriate only for relative valuations between cryptoassets or for proxying current valuations on the basis of historical analogs. Another potential drawback is that it gives equal weight to each participant, which is less true in financial settings than in advertising-driven social networks. For example, the decision by Paul Tudor Jones II in May 2020 to allocate 2% of his portfolio in bitcoin (and to promote that allocation heavily in his investor letter) is exponentially more important for valuation purposes than a new retail client at Coinbase buying her first $100 of bitcoin.
On top of that, given the large historical volatility of cryptoassets—bitcoin, for instance, has had six bear markets of more than 70% in its history—the choice of the starting point can have a dramatic impact on the suggestion for current valuations.
APPROACH 4: COST OF PRODUCTION VALUATION
The “cost of production” valuation thesis was first proposed by Adam Hayes in 2015 and has been expanded upon by multiple researchers since. The theory holds that crypto, just like any commodity, is subject to traditional pricing challenges on the supply side. Crypto miners the computers that process transactions and are rewarded with the underlying cryptoasset spend fiat money to produce each marginal cryptoasset, through both energy and hardware expenditures.
Hayes and others suggest that, viewing bitcoin as a commodity and according to traditional microeconomic theory, the cost of producing each marginal bitcoin should align with the price of that bitcoin. After all, if bitcoin mining were to become unprofitable, miners could simply turn their attention to another cryptoasset or exit the market altogether. As a result, the value of each bitcoin can be estimated by examining the marginal cost of mining (specifically, the electricity burned in running the computations as part of mining) versus the expected yield of new bitcoin.Empirical backtesting shows a relatively strong alignment between bitcoin’s price and the marginal cost of production, lending some credence (thought no directional causality) to this approach.
The “cost of production” analysis, however, involves some significant challenges. For one, it is circular in its reasoning because the decision made by miners to enter or exit the market is driven by the cryptoasset’s price. Using two necessarily cointegrated variables to value one another has very little predictive or explanatory power. The model also fails to account for or explain the massive short-term volatility of bitcoin’s price or the fact that bitcoin’s mining difficulty is programmatically adjusted on a biweekly
basis depending on the level of effort miners have focused on it.
Beyond that, many cryptoassets use a consensus mechanism different from that of bitcoin, one that does not lend itself to this kind of analysis. In proof-of-stake systems, for instance, little or no energy is consumed in mining; instead, miners lock up assets in escrow in exchange for securing the network. For these markets, no direct concept of the cost of production exists.In the end, although cost of production has aligned roughly with prices for some cryptoassets in the past, the cause-and-effect relationship is not clear and its predictive value for the future is very much in question.
APPROACH 5: STOCK-TO-FLOW MODEL
A fifth approach, dubbed the “stock-to-flow” model, was first published in the 2019 paper “Modeling Bitcoin Value with Scarcity” by PlanB, a pseudonymous crypto quant researcher. The stock-to-flow model states that bitcoin’s price is a reflection of its scarcity and that scarcity can be measured by the stock-to-flow ratio—the relationship between the extant value of bitcoin and the amount of new bitcoin being produced each year. The paper showed that the price of bitcoin has historically been tightly correlated with increasing scarcity expressed by the stock-to-flow model.
In 2020, PlanB published a new iteration of this model focused on the relationship of the stock-to-flow ratios of bitcoin and other stores of value, such as gold and silver. This new version also accounted for state transitions, or different evolutionary stages in bitcoin’s monetization process.
The stock-to-flow model is intended to apply only to bitcoin and is appealing to some who see scarcity as the dominating characteristic of hard monetary assets. We are skeptical of this approach because it appears to conflate correlation with causation. It is true that one of bitcoin’s strengths is its strictly limited supply, but assuming that this is the only factor driving its price is an overreach. It is also overly convenient for crypto bulls because bitcoin’s stock-to-flow ratio is program-matically increasing over time and, therefore, “predicts” in this model a perpetually rising price for the assets.
Also, given the programmatic nature of the model, many have pointed out that the market (even if only modestly efficient) should price in the impact of bitcoin’s future stock-to-flow ratio, impounding future value today.35 Though widely discussed in some crypto circles, the stock-to-flow ratio is not seriously considered by academic researchers.
CONCLUSION
The unfortunate reality is that none of the proposed valuation models are as sound or academically defensible as traditional discounted cash flow analysis is for equities or interest and credit models are for debt. This should not come as a surprise. Cryptoassets are more similar to commodities or currencies than to cash-flow-producing instruments, such as equities or debt, and valuation frameworks for commodities and currencies are challenging. Cryptoassets add another wrinkle in that they are still extremely early in their development, and we are still uncovering the utility that these assets can provide.
New York University professor of finance Aswath Damodaran has compared cryptoasset valuations with those traditional commodities and currencies. He has noted, “Not everything can be valued, but almost everything can be priced,” pointing out that “cash generating assets can be both valued and priced, commodities can be priced much more easily than valued, and currencies and collectibles can only be priced.”
Commodities, of course, are analyzed from a supply-and-demand perspective, and this is where cryptoassets might have an edge. Imagine that an investor could have real-time access to a transparent ledger that contains a record of every instance in which a single barrel of oil changes hands. Although this is not feasible for oil, it is easily at hand for cryptoassets. In fact, a nascent but burgeoning field of analysis combines data from what is happening in the blockchain (on-chain data) with market data–like prices and volumes (off-chain data). We are optimistic that more-refined modeling techniques looking at these data wells will bear fruit in the years to come.
In the end, most investors approach cryptoassets as some combination of commodity, currency, and early-stage venture capital investment, borrowing techniques from each approach and emphasizing long-term holding periods. This makes precision challenging but might be enough to justify or reject the idea of adding a cryptoasset allocation to a portfolio. We examine the impact of such an allocation in the next section.
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