How to Improve Your White Paper: Tokenomics

In my third article in this series*, I’m going to focus on the tokenomics section of the White Paper. Before 2018, ICOs were being funded primarily due to the the prospects of the new blockchain technology, the business use cases, and the teams that were behind the proposed projects. It was comparatively easy to launch an ICO since the cryptocurrency analytics space was far less sophisticated, and since the average investor or end user was far less knowledgeable in how to assess a potential ICO.

Today, given the high failure rate and the number of non-operating ICOs, investors and users have improved their ability to scrutinize White Papers to weed out those that lack sufficient analytic thought behind the secondary market behavior of the proposed token. As a result, the fact that most White Papers still lack a thorough analysis of the tokenomics of their proposed ICO presents a significant hurdle to a successful launch.

To address this shortcoming, issuers need to focus on a few key areas that will demonstrate to investors that they’ve thoroughly thought through the medium and long term token price implications of their decisions. Right now, most tokenomics sections include the following: the valuation of the token, the total number of tokens to be made available in the pre-sale and sale, the discount offered during the pre-sale, the total number of available tokens (if the economy is inflationary), and the type of token economy (inflationary, deflationary, or stable).

As you can see, there is no analytic thought put into how the token will function on a day to day, week to week, month to month, and year to year basis (whichever time period is most relevant for the token in question), nor is there any analysis of where the value might be from the perspective of the investor. As a result, I highly recommend that the following sections be included in each and every ICO White Paper so that investors and end users can feel more comfortable with the analytic rigor applied to the proposed token:

1. Token Life Cycle Map: This is what I call a very clear and easy to follow start to finish map of a) how the token begins its life cycle, b) any areas of potential leakage (e.g. if the token is given as a reward, will the end user be incentivized to keep their reward tokens in the economy? Or will they be more likely to convert their reward into fiat, thus reducing the demand for that token at a given supply), c) motivations for keeping or selling the token, d) any mining, proof of stake/work, or other factors that release or hold back tokens into or from the economy, respectively (this includes a detailed discussion of why an inflationary/deflationary/ or stable economy was chosen), and e) how the token ends its life cycle (if there is a buy back and burn model in place, or whether tokens are expected to cycle indefinitely).
2. Numerical Estimates of Future Supply and Demand: Once the map is clearly drawn, numbers should be put to it. It is important to have a baseline estimate of token demand derived from the levers of value discovered through the drawing of the life cycle map. That way, it can be clear to the investor on what the future value (and assumed price appreciation) of the proposed token will be based. Importantly, this section should include any velocity reducing methods being undertaken to support the long-term viability of the token (Binance is an excellent example of one such method: a customized buy back and burn model).
3. Volatility Analysis: This is especially important for utility tokens in open or semi-open economies since it is extremely difficult for an end user to depend on a token’s value when that value is highly volatile. Some tokens can withstand significant volatility (for example, those that have high incentives for the end users to keep their tokens within the economy) while others could suffer greatly from speedy price changes (for example, those that have the end user purchasing and selling tokens on a regular basis to take advantage of the business use case. This reduces the attractiveness of this type of token to an end user). As a result, based on the numerical estimates from the supply and demand model, White Papers should look at how the token‘s price will be likely to move over time.
4. Sensitivity Analysis: It is vital that a White Paper include a thorough and comprehensive sensitivity analysis of the above models and estimates. After all, any analysis is only as good as its assumptions. So you should always test those assumptions to see how much impact each has on the end result. This analysis can be accomplished either through manual testing, where the modeler changes values of key levers and assumptions and records how sensitive the results are to those changes, or through a brute force statistical approach using iterative methods like Monte Carlo simulations. Either way, the investor gets to see how important each assumption is to his or her investment decision.

Including these areas in your White Paper will greatly improve your ICO’s chance of being one of the few successful launches.

Here’s to hoping the industry heeds this recommendation and the overall success rate increases!

*To read my first two articles in this series, you can follow the links here: https://medium.com/@barrongati/why-do-most-white-papers-suck-92385cc27f0a and here: https://medium.com/@barrongati/how-to-improve-your-white-paper-telling-a-cohesive-story-e8770fef6d04

About Barron Gati: Barron Gati is the Head of Tokenomics for The Startup Station. Barron has close to twenty years of econometrics and finance experience, including an audited track record generating 75% returns over two years during the Great Recession. Previously, he was the COO of a mathematical and statistical consulting firm that serviced Fortune 500 companies and Academia. He is a contributing author for Seeking Alpha, a popular economics and finance website: https://seekingalpha.com/author/barron-gati/articles

Barron also worked for Bridgewater Associates as an Investment Associate and for the Department of Labor as an Economist. He holds a Bachelor’s Degree in International Affairs and Economics from George Washington University as well as an MBA focusing on mathematical finance from Washington University in St. Louis.