By Christian Gazzetta from WIT.insights
What are the main approaches to the valuation of traditional assets — like stocks and bonds — and how to adapt them to cryptoassets?
Evaluating an asset means estimating its value, which is conceptually different from its market price. If we think about a company, for example, the valuation should point to a target as an approximation to the value that it creates and captures given the costs and other relevant conditions of the company and the market. On the other hand, market price is mostly sensitive to supply and demand dynamics, which makes it way more volatile than value but, yet, easier to come up with.
And that difference of concepts is what generates opportunities for long term investors, since the main point of valuation models is to identify assets priced below their inner value. Though, there is no perfect valuation model, hence it’s necessary to use some of them combined, knowing the advantages and blind spots of each in order to come to a final result. In traditional finance manuals, these techniques are bundled in three main groups with respect to what they take as inputs for the calculation of target prices: costs, cash flows or multiples. Let’s take a look on each of these approaches.
The first one is mostly used in real estate market and takes into account all the costs an asset took to be set or the costs it’d take in order to be replaced. The second one is based on the idea that a company is worth the cash flows it will generate in the future. Based on that idea, if one can predict them and discount the average return he’d have by investing in similar assets, he or she has a target price for the given company. The multiples approach is way less data-intensive than the latter: using parameters like revenue, operational profit or return to shareholders, one can say if investors are paying too much for the stock, considering the indicators of what’s most important about that company.
But each of these approaches have been studied and applied to the stock market for, at least, decades. On the other hand, cryptoassets have unique characteristics that gave birth to a whole new economical dynamic, which demands some considerations before applying to them the traditional valuation models. Let’s consider how to adapt those metrics. For that, we’ll start talking about cryptocurrencies because, since Bitcoin and its forks offer the richest sources of data in the cryptoeconomy, most of the discussion about crypto valuation concentrates on them.
Evaluate these assets based on costs would take a big set of informations that are hardly obtained. We highlight the average price of mining hardware used and their efficiency, plus the cost of electricity to power them, which varies depending on the location where the farms are installed. Though, some efforts have been made in that sense, amongst which we cite Adam Hayes’ paper published in 2015.
And what about the cash flows approach? Presently, Bitcoin and most of cryptoassets provide no income to their holders, hence at a first glance it makes no sense to apply the traditional method. But Nic Carter, founder of Coinmetrics.io, argues that hardforks and airdrops (distribution of tokens to holders) can be seen as cash flows and, consequently, used to calculate a valuation. It is, by now, a theory that still needs proper testing and discussing, which we’ll put off for a while.
Now, let’s talk about the multiples approach. Because of its simplicity, it is the baseline for most used crypto valuation models. For valuing cryptocurrencies, for example, investors may assume that the volume, in dollars, of transactions that happen on-chain is a good indicator of the network’s value. The metric derived from that, called Network Value-to-Transaction (NVT), have been discussed, applied and improved in the last years and have sometimes shown that it can be a good index to identify trends on market price movements.
Obs: Market Cap is log scaled
But we’ll talk later about NVT and other methods for crypto valuation. In next posts, we’ll explore the debate on each metric, discussing their use cases, backwards efficiency as predictive tool and concerns about the underlying hypotheses.
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