Literature
 
In their book “Beyond Smart Beta: Index Investment Strategies for Active Portfolio Management” Kula, Raab, and Stahn define Total return as the amount of value an investor earns from a security over a specific period when all distributions are reinvested. While it is still too early in the development of crypto assets to account for all distributions (dividends, coupons, capital gains), it is customary to use at the very least the price increase to measure the investment’s performance.
Typically, those historical returns would be the “goal” in a predictive model catered to “learn” (in an interactive fashion) what demand signals are also signs of value appreciation.
 
In our paper Crypto Economy Complexity, we argued that crypto economies tend to converge to the level of economic output that can be supported by the know-how that is embedded in their economy — and is manifested by attention flows. And, since a fork is really an event at the macroeconomic level (for instance, the economy of BitcoinCash vs the economy of Bitcoin), the aggregate demand for output is determined by the aggregate supply of output — there is a supply of attention before there is demand for attention. We also discussed the practicalities of quantifying economic complexity by ranking economies, focusing on the specific case of cryptocurrencies and tokens. Here we will demonstrate how to develop the heuristics of such an approach. But as we will see, a more fundamental question about value arises:
Are the nodes running blockchain software essentially a material expression of people’s beliefs? Particularly, is the “belief consensus” the fundamental source of intrinsic value, than can be measured by intangible attention flows and tangible transaction activity?