Cryptocurrencies as an investment opportunity: Valuation 101

Crypto. Defi. Blockchain. Distributed Ledger Technology. Web3. WAGMI. HODL.

These buzzwords have gained more prominence than ever in the last two years. It seemed just like yesterday, in September of the dreaded year 2020, I was sitting with a friend over a few beers discussing how much of a scam all crypto assets are.

Fast forward to 6 months of active research and deep learning later fueled by the rise of the "dogefather" aka Elon Musk, I started of crypto journey as a degen, my first crypto purchase being dogecoin. A few weeks and multi-bagger profits later, I completely went down the rabbit hole and never looked back since.

A lot of people in TradFi (Traditional Finance) will tell you one thing: Crypto is a scam. And I can see where they come from, after all, I used to be one of the naysayers myself.

Their number one argument for the above?

The intrinsic valuation.

After all, how can one value a bunch of computer code which is missing publicly available information like equity, debt, cash and cash equivalents?

Let's dive right in.

The question of how to appropriately value crypto assets is one of the most complex, challenging, and disagreed-on aspects of the crypto market. Thus, I will now discuss why I believe the crypto asset valuation question will remain open for a while and more importantly, how investors can think about this issue.

Let's start with a brief but critical examination of the four most widely used crypto asset valuation techniques and end with a proposal for how to consider the issue holistically.

Approach 1: Total Addressable Market

The most popular approach to value crypto assets 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 $2,000 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 $200 billion, 25?bitcoin captures less than 2% 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 crypto assets 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 crypto asset 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 payment 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 definitions of?M,?V,?P, and?Q?in both traditional monetary economics and crypto asset markets are shown below.

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 the 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; crypto asset 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 crypto assets 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 a 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 crypto assets 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 crypto assets (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 crypto assets or for proxying current valuations based on historical analogues. 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 crypto assets—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 201531?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 crypto asset— spend fiat money to produce each marginal crypto asset, 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 crypto asset 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 (though 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 crypto asset’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 crypto assets 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 the cost of production has aligned roughly with prices for some crypto assets in the past, the cause-and-effect relationship is not clear and its predictive value for the future is very much in question.

How Much Bitcoin Is the Right Amount?

Perhaps the most important question when allocating to crypto is, How big a position should you have??The below table?examines that question, looking at the impact of allocating between 0% and 10% of a portfolio to bitcoin over rolling three-year periods.

The table suggests that for this set of rolling periods, increasing the allocation to bitcoin consistently led to higher average returns and higher average Sharpe ratios. For instance, a 1% allocation to bitcoin added 5.3%, on average, to the portfolio’s return and boosted the Sharpe ratio by 0.19, whereas a 5% allocation to bitcoin added 28.1% to the portfolio’s return and boosted the Sharpe ratio by 0.69, on average.

Note, however, that the impact on risk statistics is not linear. As shown, the average maximum drawdown of the portfolio remains largely flat for allocations to bitcoin between 0% and 4% because, at this size allocation, bitcoin never competes with the equity allocation to drive the portfolio’s maximum drawdown. Above 4%, however, the maximum drawdown rises rapidly, with each 1% additional allocation to bitcoin increasing the maximum drawdown by roughly 1%. This might provide a ceiling on appropriate allocations for risk-sensitive investors.

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. Crypto assets 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. Crypto assets 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 crypto asset 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 collectables can only be priced.”?Crypto assets fit somewhere between the second and third buckets.

Commodities, of course, are analyzed from a supply-and-demand perspective, and this is where crypto assets 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 crypto assets. 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 modelling techniques looking at these data wells will bear fruit in the years to come.

In the end, most investors approach crypto assets 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 crypto asset allocation to a portfolio.