Valuing a Payment Utility Token: IvyKoin Case Study

Like many token buyers, I am seeking some form of analytical tool that can provide useful and reliable valuation metrics.

Chris Burniske, co-founder of Placeholder Ventures, has published a useful article¹ in which he proposes a model based on the equation of exchange. What I wondered was whether an adapted version of this model would support my decision to invest in this particular token.

In assessing the value of a utility token, there are obvious questions to ask:

• Is the token essential for transacting on the platform hosting it?
• What are the factors likely to influence demand for the token?
• How will token supply vary over time?

According to good old-fashioned demand-supply economics, if demand is high and supply is limited, then we should reasonably expect price to increase.

So far so good. Just find a token with a valid use case, limited supply and unlimited demand, purchase some, HODL and await your new home on the moon.

Not so quick! Short term speculation (hype) might drive the token price up due to its perceived useful application or introduction of new technology. However, for those considering the token for its medium or long-term value appreciation, things are somewhat more challenging.

How do you adequately value a utility token over time and beyond a short-term speculative surge? A traditional valuation method for stocks is to discount a stream of future earnings to present value using a risk-weighted discount rate. This allows you to arrive at a ‘fair value’ price for the stock. However, this is problematic for utility tokens as most of them do not have attributable earnings. What we can do, though, is calculate a theoretical utility value at different points in time and discount those. This is where things get interesting and rather more complex.

Burniske makes reference to the influence of velocity on the calculation of a token’s utility value. Simply put, velocity here means the number of times that a single token is recirculated in a given period. Think about a banknote in your wallet. Even if you only spend that particular banknote once, the same banknote is spent multiple times by others. The more frequently the banknote is spent, the higher its velocity. In short, a banknote that has a higher velocity than another requires a lesser circulating supply to support its required use.

To make things even more interesting, there is little empirical data available to support any decent analysis of the velocity of different types of cryptocurrency tokens or coins. Bitcoin, being the most established, may provide some guidance and I refer to this below.

CASE STUDY: ivyKoin

What is ivyKoin?

ivyKoin is a payments utility token targeted for use by financial institutions. It’s key point of difference is the ability to attach a significant number of data points to payments, to better allow financial institutions to comply with Know Your Customer (KYC), Know Your Transaction (KYT) and Anti-Money Laundering (AML) requirements. It also aims to facilitate cryptocurrency payments and cryptocurrency to fiat exchange.

Token Supply

Total number of tokens to be generated is 1.5 billion. Around 40% of this has been allocated in the seed round and private sale. Another 20% is allocated to the team and advisers, most of which is not subject to a vesting schedule.

So, the initial circulating supply will constitute around 60% of all tokens (i.e. 900 million). This will inflate over time arising from tokens released from the growth pool, reserves and treasury hedging. In the model, I have assumed that these will be released in equal amounts over a 10-year period.

Token free float will be reduced to the extent that some buyers (HODLers) determine to retain their tokens rather than to trade them. In addition, one imagines that the team and advisers are likely not to sell all of their entitlements too early on. The model assumes that, initially, 50% of the circulating supply will be removed from the free float, increasing token scarcity. This is reduced over time.

Market Value Targeted by Token

I have made the following assumptions:

• the primary target market will be for business to business (B2B) cross border payments. According to McKinsey & Co Global Payments report, B2B payments represented more than 98% of all cross border flows and stood at $138 trillion in 2015. Annual compound growth is forecast at 3.5%, so this would put the number at around $150 trillion today.
• only 25% of these payments will be available to an ivyKoin-style solution. My reasoning for this is that there may be barriers to adoption arising from matters relating to sovereignty, politics, reluctance or inability to adopt a Blockchain/cryptocurrency solution, as well as alternative and improved future technologies.

Market Share

Here I have assumed that ivyKoin will garner 20% market share of the available market, over a 10-year period (equivalent to 5% of all global cross border payments). A modest 0.01% market share is factored in for 2018, as the public network will not go live until H2 and little is currently known about the nature of the arrangement with the initial bank client. Market share accelerates to 1% by the end of 2020 and increases at a more rapid rate over the subsequent 8 years as adoption gains traction.

Payments Facilitated by the Token

Based on the market value and market share assumptions above, ivyKoin would facilitate gross payments as follows:

• 2019 — $96 billion
• 2020 — $397 billion
• 2029 — $9.2 trillion

Velocity

Velocity calculates the monetary base required to support economic activity. Here, the relevant economic activity is the value of payments facilitated by the token. So, the question is what velocity is appropriate for ivyKoin?

In the United States the velocity of M1 money stock is around 5.5. Burniske has hypothesised that the velocity of Bitcoin might be 14. I have assumed for this analysis that, given ivyKoin’s function, it’s velocity will be higher than that of Bitcoin and that it will increase over time. The main reason for this assumption is that there appears to be little purpose in holding the token, other than briefly, to facilitate an immediate payment. On the face of it this would imply a relatively high turnover for the token.

There is some reference in the whitepaper to possible burning of tokens by ivyKoin but this is not fully articulated, so I am unable to account for this.

Velocity is a significant factor and my assumption is highly speculative. It will also likely be influenced by the actual rate of adoption of the platform as well as by any additional token utility functions that may be developed (e.g. cryptocurrency exchange).

Token Value & Market Cap

So, what are the outputs, you may ask. Below is a summary table that illustrates token supply schedule, market value metrics including velocity assumptions, token utility value and market capitalisation outcomes.

Turning briefly to token market price. It might be reasonable to suggest that at a given time price would reflect present token utility value plus some amount in respect of forecast future appreciation in utility value. Here, I have calculated the marginal amount as the present value of the increase in utility value forecast 10 years beyond any given date, discounted at a risk-weighted return of 50% per annum.

For 2018 this is $1.84, although one would suspect very little of this value would translate into the token market price, given that the business model would be relatively unproven still. That said, speculative interest in the token would justify a price above its imputed utility value and, depending on the level of interest, possibly substantially so.

Conclusion

I undertook this analysis primarily out of a sense of personal curiosity. Clearly, there are variables (especially velocity) that can significantly change the pricing model’s outputs. The assumptions I have used may prove to be wildly inaccurate, possibly under-stating or over-stating valuations by a significant degree of magnitude. Such are the perils associated with modelling, especially where there is so little empirical evidence to support a hypothesis. It will be interesting to see how things play out once the market is more mature.

Hopefully some of my observations will be of interest to others and will contribute to the current dialogue on token valuation modelling.

Disclaimer: The views expressed in this article are my personal views and opinions and none of this content represents financial advice or a recommendation to purchase any token of whatsoever nature. The data contained in the table above is not a forecast or projection but simply a shared resource provided to stimulate further discussion.

References:

¹ https://medium.com/@cburniske/cryptoasset-valuations-ac83479ffca7