# Cryptoasset Valuations By [Harrison](https://paragraph.com/@harrison-3) · 2023-01-15 --- Recently, an increasing number of crypto market participants and observers have become interested in a framework for valuing cryptoassets. Over the years many a dinosaur has proclaimed bitcoin valueless, an asset worse than tulips (at least with tulips you got a flower). Now they’re trying to figure out how valuable these assets really are. That’s a big win for the magic internet money community. In this piece I share some early attempts at crypto valuations to give perspective on how early we still are, then discuss the theory I’m currently using and why, before walking through a fictional bandwidth coin valuation that includes a link to the actual model. Each section operates as a standalone, so feel free to skip amongst them. The first time I attempted to value bitcoin was at [ARK Invest](https://ark-invest.com/), where I started as a buy-side analyst in 2014. ARK became the [first public fund manager](https://bitcoinmagazine.com/articles/wall-steet-interest-in-bitcoin-grows-with-ark-fund-investing-in-silbert-s-bitcoin-investment-trust-1442507319/) to invest in bitcoin in September of 2015, and to do so we had to have some basis to justify current prices ($200's), or at least quantify the potential for significant asset appreciation. Other asset managers will have to do the same as part of their fiduciary duty, which is one reason everyone’s become so interested in cryptoasset valuations. Below is an example valuation from a [paper](http://research.ark-invest.com/bitcoin-currency) I wrote with Dr. Arthur B. Laffer to complement ARK’s 2015 investment, which serves as a nice starting point. While an overly simplified assessment of value, this graph gets across a few key concepts, mainly total addressable market (TAM), percent penetration of that market, velocity, and number of coins outstanding. The thinking was as follows (with notes where my thinking was flawed): * The TAM for remittances in 2014 was $436 billion (using a present TAM for future adoption was a mistake). * Potential percent penetration of that market could be 10%, meaning Bitcoin’s blockchain would have to transact 10% x $436 billion, or $43.6 billion, to satisfy this demand (not providing a time frame for adoption makes it impossible to deduce percent return each year). * The “same bitcoin” could be used multiple times in the transmission of value for remittances — in this case 1.5 times per year. Hence, $43.6B / 1.5 = $30 billion in value that bitcoin would need to store (more on velocity later; 1.5 was too low). * At the time of publication, there were 14.7 million coins outstanding, so $30B / 14.7M = $2,000 per bitcoin (using the present number of coins for future adoption was another mental error). The above valuation could be stacked with bitcoin’s use in another target market, with another percent penetration and velocity. The values for each target market would then be additive; dual demands on a single supply. Clearly I was still struggling with all the variables — [Spencer Bogart](https://twitter.com/bitcom21) and [Gil Luria](https://twitter.com/gilluria) were doing better work than myself at the time, putting out [reports](https://www.coindesk.com/wedbush-report-projects-400-bitcoin-price-by-2016/) on Grayscale’s GBTC, which I recommend looking at. I’ve thought about cryptoasset valuations a lot more since then. The first thing to note with crypto valuations is these aren’t companies; they don’t have cash flows. Hence, using a discounted cash flow (DCF) analysis is not suitable. Instead, valuing cryptoassets requires setting up models structurally similar to what a DCF would look like, with a projection for each year, but instead of revenues, margins and profits, the [equation of exchange](http://www.investopedia.com/terms/e/equation_of_exchange.asp) is used to derive each year’s current utility value (CUV). Then, since markets price assets based on future expectations, one must discount a future utility value back to the present to derive a rational market price for any given year. I believe in a [taxonomy of cryptoassets](https://medium.com/@cburniske/why-i-like-the-term-cryptoassets-ab6b76e1ee33) that goes far beyond currencies. That said, within its native protocol a cryptoasset serves as a means of exchange, store of value, and unit of account. By [definition](http://www.imf.org/external/pubs/ft/fandd/2012/09/basics.htm), then, each cryptoasset serves as a currency in the protocol economy it supports. Since the equation of exchange is used to understand the flow of money needed to support an economy, it becomes a cornerstone to cryptoasset valuations. The equation of exchange is MV = PQ, and when applied to crypto my interpretation is: * M = size of the asset base * V = velocity of the asset * P = price of the digital resource being provisioned * Q = quantity of the digital resource being provisioned > _A cryptoasset valuation is largely comprised of solving for M, where M = PQ / V. M is the size of the monetary base necessary to support a cryptoeconomy of size PQ, at velocity V._ Let’s start with P and Q, as those seem to trip people up the most. The first thing to note is P does _not_ represent the price of the cryptoasset, but instead the price of the resource being provisioned by the cryptonetwork. For example, in the case of Filecoin it would be the price per gigabyte (GB) of storage provisioned, represented as $/GB. Q represents the quantity of that resource provisioned, in the case of Filecoin the GBs of storage. Multiplying $/GB x GB = $. This dollar amount represents the exchange of value in the Filecoin economy to provision cloud storage (and whatever other utilities Filecoin may provide over time). In other words, it is the GDP of the Filecoin economy, which fits with classical monetarism where PQ is the gross domestic product (GDP) of a country. Fortunately for crypto folks, we have transparent and immutable ledgers to track this GDP — they’re called blockchains. > _Hence, the GDP of a cryptonetwork is represented by the on-chain transaction volume of its cryptoasset._ A sidenote: While I believe on-chain transaction volume nicely represents a cryptonetwork’s GDP, it is imperfect because often 30%+ of a cryptoasset’s on-chain transaction volume can be shuttling the asset between exchanges. Doing so is not an exchange of value for the digital resource of the network, but instead a means of speculation, which is excluded from GDP metrics. For example, FX volume is not incorporated into the GDPs of nation states. Additionally, second-layer scaling solutions will make this assessment of GDP conservative, though I would likely consider the assets used in second layers as bonded, falling into the “bonding bucket” that I later discuss. Turning now to V, velocity shows the number of times an asset changes hands in a given time period. Re-arranging MV = PQ, we can calculate V = PQ / M. Taking bitcoin in 2016 as an example, that year the network processed an average of $160 million in [estimated USD transaction value](https://blockchain.info/charts/estimated-transaction-volume-usd) per day, for a total of $58 billion in the year (PQ). The average size of bitcoin’s asset base through 2016 was $8.9 billion (M). Hence, V = $58B / $8.9B, or 6.5. A velocity of 6.5 means that in 2016 each bitcoin changed hands 6.5 times. In reality, a small percentage of bitcoin in the float likely exchanged hands a lot more than that, while a larger percentage sat locked in hodlers’ hands, but more on that later. For perspective, the velocity of the [USD M1 money stock is 5.5](https://fred.stlouisfed.org/series/M1V) right now, though this has declined precipitously since the Financial Crisis of 2008 (increase M significantly, while PQ squeaks along, and V is bound to decline). Lastly, the asset base, M. Note that I have used an average size of bitcoin’s asset base through the year, which is necessary due to the inflationary nature of the asset. Accounting for an expanding monetary base is particularly important for younger cryptoassets that could be classified as hyper-inflationary with annual rates of supply increase that clock in north of 20%. Now that we’ve covered the variables of the equation of exchange, and touched upon the idea of a total addressable market and percent penetration of that market, there’s one more key concept to cover: discount rates. We’ll do that in the context of an actual model. --- *Originally published on [Harrison](https://paragraph.com/@harrison-3/cryptoasset-valuations)*