Coming from a traditional finance background, one of the things I miss the most is a clear way to value certain assets. To value equities, you can use a Discounted Cash Flow model, or for options, you can use the Black Scholes model. And though the validity of the assumptions you make for these models can be up for debate, the models themselves are not up for debate. Unfortunately, that is not the case in Crypto. There is no clear, universally accepted, model for valuing cryptoassets.
From velocity models to DCFs using network fees to intimidate Cash Flows, an increasing number of crypto market participants have brought forth frameworks for valuing cryptoassets. With the amount of thought and manpower that has gone into valuing cryptoassets, it has quickly become one of my favorite topics within the space. Because of this, I thought I would share some of my favorites. Inspired by a16z’s Crypto Canon, I have created my own list specifically on valuation models and metrics.
Source: A New Way to Find Discount Rates in Crypto Models
By: Chris Burniske
About: One of the original valuation models, Chris Burniske introduces velocity valuation models. By limiting the taxonomy of a cryptoasset and only observing the asset as a currency you can apply the quantity theory of money. Using the equation of exchange, MV=PQ, as the basis of the valuation model, you can effectively solve for the future value of M and discount back to the present. This is explained in great detail in the post as well as including a downloadable spreadsheet for the valuation model.
Understanding Token Velocity
By: Kyle Samani
About: In this post, the Multicoin team further breaks down the idea of token velocity with the example of concert tickets. They also break down certain token economic models that mitigate velocity within projects. An example of this is the work token model that Augur has implemented. In order for people to provide work on the network and receive an award, they must hold REP tokens before they are allowed to work. More examples in Kyle’s post.
A New way to Find Discount Rates in Crypto Models
By: Alex Woodard
About: This is a shameless plug of my first Medium blog post. In this post, I introduce a discount rate matrix as a way to quantify the discount rate for specific crypto assets. Previously with velocity models such as Chris Burniske’s, a discount rate was arbitrarily picked based on the analyst’s perception of the cryptoassets’s risk. The discount rate matrix removes one of the key assumptions within cryptoasset valuation models and has helped standardize my own analysis.
A Framework for Valuing Governance Tokens: 0x
By: Phil Bonello
About: In this post, Phil introduces a framework for valuing governance tokens. He states “The maximum price a network participant (maybe a relayer) will pay for 51% of governance tokens is bound by the cost associated with a network fork. Cost is equal to the difference between the net present value of the pre-fork and post-fork business.” Simplified tokens only used for governance should be valued at the cost to fork. This article is incredibly interesting when you take into consideration the recent fork of the 0x protocol by DDEX.
The Next Step in Cryptoasset Valuation
By: Rustam Botashev
About: This post by Rustam Botashev builds upon Chris Burniske’s velocity valuation model by challenging the specific assumption that velocity stays the same over time. Similar to how discount rates were arbitrarily picked in valuation models, so too were velocity models. This is an issue because different blockchain projects will likely have different levels of velocity because they are all at different stages of their blockchain development. Rustam introduces his own model called the Rational Network Value.
Note: I have begun implementing similar ideas into my own valuation models.
An Efficient Markets Valuation Framework for Cryptoassets using Black-Scholes Option Theory
By: Johnny Antos and Reuben McCreanor
About: This piece reviews previous pieces on valuation models within the space as well as introducing a new model that values unknown possibilities that new technology unlocks. They do this with the Black-Scholes model. The Black-Scholes model is typically used for pricing European Option calls and derivatives, but in this case, applied to cryptoassets. Really interesting piece.
Bitcoin NVT Ratio
By: Willy Woo
About: The NVT ratio is more of a metric most similar to the PE ratio. It takes the network value (market cap) and divides that by the daily USD volume on the Blockchain. “When Bitcoin`s NVT is high, it indicates that its network valuation is outstripping the value being transmitted on its payment network, this can happen when the network is in high growth and investors are valuing it as a high return investment, or alternatively when the price is in an unsustainable bubble.”
Thoughts: As I said above, this is a collection of some of the more valuable pieces on valuation models and metrics used to find the price of Cryptoassets. These are all relatively new thoughts, with valuation models really only beginning to form near the end of 2017. With that in mind, these models will likely change as more brain power is directed to this topic by investors. I am excited to see where valuation models go and if any become universally accepted over the coming years. If that does happen, it would be a significant step forward for investing in the crypto space.
If you have any thoughts or readings to recommend on this topic, feel free to reach out on twitter!
This article was originally published on Medium.