Pricing is one of the elements that has been at the center of the cryptocurrency debate almost since the beginning. Many well-known crypto-skeptics argue that most cryptocurrencies are doomed because the only factor influencing their price is how much someone is willing to pay for it. If you are reading this article you (just like me) strongly disagree with that statement. However, the question of modeling the right price/valuation for crypto-assets remains an interesting challenge. With security tokens becoming a new relevant asset class in the crypto space, this problem just takes a new dimension.
We are in the very early stages of the evolution of the security token ecosystem and many of the foundational building blocks of the market are just being built. However, nobody doubts that valuation models is a key element that needs to be solved if we want security tokens to become a viable asset class. At the moment, the first wave of security token offerings(STO) are using valuation models that are highly subjective which is unsustainable as the space becomes more mainstream.
The crypto industry has already produced some interesting models for calculating the valuation of utility tokens and cryptocurrencies so why couldn’t we adapt those to the security tokens? What makes the pricing and valuation of security tokens so challenging? I am going to borrow a term from the deep learning space to summarize the challenge: The Curse of Dimensionality.
Dimensionality and Security Token Valuations
The curse of dimensionality is a famous theoretical dilemma in the deep learning space that states that problems grow in complexity with the number of dimensions. For instance, an image recognition problem has many more dimensions than a classic linear regression model. What does that has to do with security tokens? Very simple, the number of factors/dimensions and the combinatorial variations of them that can be used to value a security token are several multiple higher than their utility token equivalents. As a result, the models for valuing security tokens are exponentially more complex than what we have seen so far in the utility token space.
Let’s dig a little bit deeper into this dimensionality issue. From the models for valuing utility tokens, one of the most popular was the one proposed by Placeholder Capital co-founder Chris Burniske in his book Crypto Assets. Burniske’s model is based on a simple formula M=PQ/V that estimates the valuation of a crypto-asset based on the following factors:
- 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
Burniske’s method is incredibly clever and simple ( so simple that I think it needs to be modified for more sophisticated token models). The formula clearly reflects the key factors that influence the price of a utility token and it also assumes that the pricing of those assets can be describe by a linear model. If we try to extrapolate Burniske’s ideas to the security token space, we will rapidly discover that there are many more factors that determine the price of crypto-securities and that the behavior is far from being a linear model.
Factors that Influence the Valuation of Security Tokens
How do you calculate the valuation of security tokens? There won’t be a single answer to that question. Just like asset-backed-securities, we will have many statistical methods that model the valuation of security tokens. Similarly, different classes of security tokens will be valued using different models. The valuation of a tier1 security token that represents a real estate lease, its different than a token that represents shares in a private company which is different from a security token derivative that represents a pool of real estate assets. I have been doing some work on pricing models for security tokens that I plan to discuss here in the near future but, before we go too far, we can start by understanding some factors that are highly influential in the valuation of security tokens.
While there is an, arguably, infinite number of models that can be used to price security tokens, there is also a common set of factors that help to setup a framework for any valuation analysis. A simple way to frame this analysis is by dividing those factors into two categories: crypto-based and asset-based.
Crypto-Based Valuation Factors
Just like utility tokens, the valuation of crypto-securities is influenced by different factors related to the nature of the token itself. Some examples of those factors will be the quantity of tokens being provisioned or the frequency at which the tokens are traded (velocity). At this level, we can borrow a lot of ideas from the utility token space to model the valuation of security tokens. However, we also need to consider a whole set of factors that are not related to the architecture of the crypto asset.
Asset-Based Valuation Factors
Differently from utility tokens, the valuation of security tokens is deeply influenced by off-chain market factors. Without attempting to provide an all-inclusive lists, here are some of the factors I believe should be considered when estimating the valuation of security tokens:
Valuation of the Underlying Asset
Security tokens that represent alternative assets are highly influenced by the price of the underlying asset. Take the example of a token that represents shares in a private company, as the valuation of the company increases it is likely that the price of the corresponding security token will increase as well.
Cash Flow & Dividend
Many security tokens represent cash-generating assets in debt instruments such as real estate leases. Those security tokens are likely to regularly issue a dividend to token holders which is a factor that should be considered in the valuation. Tokens that issue a higher dividend can be priced higher than other tokens with a low dividend (that’s not necessarily true in some asset classes).
Imagine a security token that represents a pool of real estate leases. There is an intrinsic risk related to the fact that some people might not pay or default on their leases. Quantitively estimating that risk should be a factor to consider in the valuation of security tokens.
The valuation of a security token doesn’t necessarily need to have a linear correlation to the underlying asset. In many cases, security tokens can used different collateralization methods to controls and influence its valuation. For instance, a security token can represent a group of real estate leases from different markets that will hedge each other under specific market conditions.
Liquidity is one of those factors that is key in the valuation of the first generation of security tokens. The guys from Securitize have done a masterful job factoring in liquidity as a native component of their platform. Liquidity models such as token reserves or excess cash flows are a highly relevant factor in the valuation of security tokens.
There are several other factors that can be relevant in the valuation of crypto-securities. Taxation models of the underlying asset class, rating criteria by third party agencies or insurance models are factors that should be considered as they are certainly relevant in securitized products. Fortunately, we can borrow a lot of interesting ideas from securitization models to model the valuation of security tokens. I will expand in this subject in a future post.