RootProject’s CEO is also a political economist. Judge Research is a new research firm founded and staffed by PhDs. We combine our experience as formal students of markets with years of experience in crypto markets.
The Critical Nature of Token Buy-Backs
Token-issuing organizations whose models involve the purchase of their own token face special challenges. Most importantly: the efficacy of those buy-backs can have more impact on an organization’s long-term success than any other variable.
An organization’s operations in this area are (1) critical, and (2) poorly researched and understood. That’s a major reason RootProject’s whitepaper contained plans for an advanced research group. In a normal market we could pick up where others’ work left off and make some fairly quick judgements about what to do. In crypto asset markets, we have to basically start over from scratch, with months — frankly, more than a year — of basic empirical research to fill in knowledge gaps about how the market functions.
The choice is totally dichotomous: a cohesive research agenda, or abandon in crypto the type of academic rigour that is one vital ingredient in any market system.
Take some evidence from our first major study, Understanding the New Paradigm of Liquid Venture Capital: We compared the top 25 tokens by market cap along with 5 small cap projects. What was the token in the current market era that outpaced all the others? BNB, Binance’s token, whose token model is heavily focused on buy-backs.
The other feature to notice about the above chart is how spread out outcomes are. There is a cluster at the bottom but otherwise, even in a very down market, there is a fairly even distribution of crypto asset price outcomes.
The Open Question: Sources of Efficacy
BNB’s lead over the other crypto assets in our sample is not the result of Binance’s trade volume, which has remained fairly steady. Rather, it seems to be the result of their token model — and the buy-back operations at the heart of it.
The next chart shows correlations as they change over time between the two most important crypto assets, and it is fairly representative of patterns seen throughout the market (we studied 435 time series of correlations).
These correlations are different from some time-varying measures of correlation that I have seen in crypto. Few people would care if I went into the details as to why. I’ll just say here that models of correlations as they change over time were the topic of my dissertation, and I and my coauthor, Dr. Dennis Zhang, are confident in this representation. If you’re super-interested in this topic, check out Engle (2002) or ping me on twitter: Nicholas Adams Judge.
So, we are left with (1) very high daily correlations, yet (2) widely divergent price outcomes. In that way — and a lot of others — we find a market whose basic (baaaasic) descriptive statistics suggest do not suggest behavior similar to other markets.
The steps necessary to understand how best to conduct buy-backs, then, require not a few tests, but a large, full research agenda. The good news is that, thanks to a few nobel-winning economists, we know how to do it. The bad news is it is a lot of work.
We have been working away at it for some time, and we are glad to be able to get to a point where we can start sharing some findings with the community.