Tokenisation part 3 – Price valuation

In this third blog post in my tokenisation series, I will share some thoughts around price valuation of tokens. But before you read further you must know that there isn’t a magic formula that will tell you the true value of anything. At the end of the day, the space around blockchains and crypto assets are incredibly young, without any established models and the price is largely driven by speculation.

The only valuation model that’s been used anecdotally, is the ‘equation of exchange’. This model is explained in the context of crypto asset valuation, by Chris Burniske.

Another way of assessing the value of something is simply by looking at the market behaviour in which a token is traded. Through fundamental analysis, phycology, market dynamics and price discovery, one can argue that “the market is right” on average. And if the volatility of an asset is low, one can argue that the market is right more frequently than if the volatility is high.
I have therefore constructed a very simple volatility index of a few tokens from each model, based on the difference between highest and lowest weekly closing price in 2018, based on data from Coin Market Cap.

Currency tokens

For money (or currency) to be valuable it has to be somewhat scarce and be a good store of value — see my previous post about Money on Blockchains.

Network effects are also incredibly important for any kind of currency. It has to be in the hands of many people for it to become a useful unit of account and have good liquidity as a means of exchange. Therefore, it’s very difficult to price a currency that is still in the adoption phase and currency tokens tend to be prone to speculative trading.

That said, the ‘equation of exchange’ model is a decent model to calculate the value of a currency.

Volatility index: 58-64%, for Bitcoin (BTC), Litecoin (LTC) and Monero (XMR).


The valuation method for a security token is very straight forward; you get a proportional share of some revenue on a balance sheet. And while there’s definitely speculative action happening with securities (e.g. stock markets), there are established methods for enterprise valuation, using cashflow, debt, earning reports etc.

Volatility index: 13-25%, for Apple Inc. (AAPL) and Tesla (TSLA).

Note: The above securities aren’t tokenised on any blockchain (yet). But there’s no difference i valuation of a regular security, and security as a crypto token.

Utility tokens

Work tokens

With work tokens, one can compare the value of services and goods provided within a network to some other network that already exist with some market cap. So while there are models that can be applied, it’s very difficult because the nature of blockchain and crypto network tend to be very disruptive and very different to existing products.
Therefore, these tokens are candidates for speculative trading and venture capital investment. While that’s not inherently bad, it creates additional risk and volatility for early adopters and users.

That said, it is possible to compare the value of a network or for usable products or networks models can be applied

Volatility index: 67-81% for OmiseGO (OMG), Augur (REP) and Kyber Network (KNC).

Governance tokens

Compared to work tokens, governance tokens are even harder to evaluate. What is the real value of taking part in decision making for a network, assuming the network already provides some value to its users? Ultimately, the value of governance will depend a lot on who you are, and what role in the network you have. Because of this, I believe there’s even more speculative trading and volatility baked into the price of governance tokens.

Volatility index: 78-85%, for 0x (ZRX), Melonport (MLN) and Status (SNT).

Discount tokens

Discount tokens are much easier to evaluate compared to any other type of token. Here’s a quote from my last blog post in this series:

An over-simplified model would say that all discount tokens on a network should be worth as much as the total amount of fees being paid. However, that doesn’t mean the value of a discount token is fixed or can’t increase (e.g. the value will most likely increase if the supply of tokens is fixed but the network value grows). It just gives a much clearer and easer way to evaluate a token and doesn’t give as much room for speculators to speculate on.

I can recommend reading CoinFund’s article Discounts vs. Payments: Comparing Discount Tokens with Utility Currencies where a comparison to the equation of exchange is mentioned.

Volatility index: 46-60%, for DigixDAO (DGD) and Binance Coin (BNB).

Burn-and-mint tokens

As I explained in my last post, this model is fairly complex but very well designed. There aren’t many good examples of tokens implementing this model. The only one is Factom (FCT).

Volatility index: 74%, for Factom (FCT).


In conclusion, it’s clear that valuation models are largely missing for crypto assets and tokens in general. But I think that market behaviour and price discovery of tokens of different models will tell us something about how accurate the pricing is for any given model.

If we exclude tokens that represent financial securities (which are “easy” to evaluate), my very simple study with a volatility index shows that the order in which models are more likely to have their tokens correctly valued are:

  1. Discount tokens
  2. Currency tokens
  3. Burn-and-mint tokens
  4. Work tokens
  5. Governance tokens

2 thoughts on “Tokenisation part 3 – Price valuation

Leave a Reply