We’re obsessed with wanting to draw rational relationships between the price of crypto tokens and the value behind them. There is no shortage of viewpoints from analysts, entrepreneurs, developers, pundits or investors on this question. Just google “crypto tokens valuation metrics” and you will see the variety of what has been written so far on this topic.
My own last attempt was from September 2019, as I enumerated nine different variables for measuring the health of cryptocurrencies and tokens. But I didn’t profess to have cracked the code on a magical formula, or to have found the magical equation that would become the telling star for these valuations.
As I lamented in 2017 in “The Darkness Side and Long Honeymoons of Token Sales,” we are in dire need of fundamental metrics that equate to how publicly traded companies are routinely valued.
In public companies, analysts and investors use metrics such as revenue, net income, EBITDA (earnings before interest, taxes, depreciation and amortization), EPS (earnings per share), P/E ratio (price to earnings ratio) and sales growth in order to correlate market capitalization justifications.
For ICOs and token-based projects, what are the equivalent performance metrics?
At the end of 2017, as token users started to surpass the number of token speculators, we hoped usage activity would prevail as the mainstay of token valuations. Fast forward to today, three years later, and we find ourselves back to square one.
Two promising blockchain metrics have struggled to assert themselves because they failed the test of time.
First, take gas fees on the Ethereum network. As the network became more popular (a good thing) it also became slower (a bad thing), resulting in increased gas fees to run the variety of smart contracts. The interpretation of the increased gas fees became a point of contention.
On one hand, increased gas fees count as part of “network revenue” (a good thing). But on the other hand, it also meant that each given transaction became too expensive to run on the Ethereum network (a bad thing), and that forced some use cases to consider either moving to Ethereum side chains or other chains (a bad thing for Ethereum).
What did the ethereum token price do during this period? First it went up, then it went back down. But it was very hard to establish a real correlation via any type of equation or quantitative measure that could be followed reliably.
Second, take DeFi, a sector that has an aggressive growth trajectory. As recently as July it was commonly accepted that the DeFi Total Value Locked (TVL) was a good indicator for the valuation of the DeFi market. As TVL continued to grow, so did the total market cap of the top DeFi tokens.
Then the correlation broke down. As I wrote in CoinDesk in early September, the total DeFi market cap was hovering at $16 billion. Today, it is about $12 billion.
One could justify this pullback by citing traditional market behavioral dynamics that typically price underlying instruments ahead of expectations but deflate themselves after the news has been made public. That is a common psychological yin and yang in markets behavior. Perhaps that was the case here.
That said, keep in mind the DeFi “TVL-to-market cap” relationship I cited above draws on “macro” metrics based on the health of the entire segment, and that is a lot easier to quantify than trying to apply metrics to individual tokens based on their own intrinsics. Good luck in trying to translate the macro view at individual token correlation level.
In addition, DeFi had a peculiarly artificial reality. The price of many tokens was often driven by automated market maker algorithms that do not factor in prior judgment or knowledge about usage metrics and, rather, derive their core from a given demand/supply dynamic curve and the presence of a variable liquidity pool. Tight liquidity/float pools create artificial price points that need to be tested over time.
This leaves me to conclude that, in the absence of correlatable metrics, we are only left with vanity metrics, or perhaps just “input-type” data points that could one day find their way into some set of correlative equations that will make sense.
The other peculiar anomaly between traditional stocks and crypto markets is that in crypto markets the same currency that has actual (user) utility is the one that investors/speculators partake in owning. This is in contrast to stocks that are just a unit of account but cannot be used to purchase related products or services from the underlying issuer.
You would think that this should give room for an even tighter correlation to emerge between usage and value, but that hasn’t happened yet, at least not in a way where we can start to build a body of support behind it.
Of course, we still have hope that one day the usage of highly popular cryptocurrencies (whether via user or developer traffic) would append and dictate the actual value trajectory of the underlying token, but that day is yet to come.
Imagine if you were mandated to pay for charging your Tesla via a fraction of a Tesla stock. In essence, you would need to keep buying Tesla stock (therefore creating demand for Tesla stock and contributing to its eventual rise) to pay for an electric charge (excluding from your own charging station at home for example).
Of course, as the Tesla stock price goes up your actual cost for charging would go down, and that would be a good thing. If for some reason Tesla owners stopped driving their cars, the ensuing decrease in activity would result in less demand for Tesla stock/currency and its price would dwindle accordingly. But at least there would be a real linkage between usage and demand, something that is natively intrinsic to crypto tokens.
In the blockchain space, on-chain fees/revenue still hold a good promise for being a leading indicator of blockchain network value. Ethereum has a proposal for making the fees schedule more dynamic (EIP 1559 and Fee Structure), which promises to make payments more equitable but also potentially more difficult to correlate. That is certainly a development area to watch.
My friend Evan Van Ness aptly called a number of chains “Zombie Chains,” based on the little number of transactions that actually pass through them. That is an example of negative metric correlation that is nonetheless logical and easy to understand or validate.
We continue to be in the early stages of finding correlation metrics between crypto tokens value and usage.
For this reason, I believe we are still in the era of qualitative crypto tokens valuation, where the price of tokens is primarily driven by speculative perceptions, brand value and creator promises.
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