Update of the Data in the Macro-Mathematical Model for the Observed Value of Digital BlockChain Networks to End 2017
In a previous article, it was demonstrated that the growth in value of several top cryptocurrency assets could be modeled by an S curve or exponential, driven mostly by adoption. (The complete peer reviewed paper is available here.) The model derived in the study utilized Metcalfe’s law as well as a newly proposed network effects law. The models showed the growth to be correlated to the growth in the number unique addresses used daily on the network; data that is easily extracted from the blockchain. An update to that study is presented here, extending the data from around June when it was presented, to December 2017.
As explained in the prior article, the network model as a measure of value is simply a statement to the effect that the more actual people are using a network the more people on the network have other users to interact and/or trade with, and consequently the greater the value the network would ultimately have. As a measure of value, it might turn out to be a lagging indicator and is not intended here to be used for day trading decisions; or indeed any investment decisions.
There are a few observations that can be immediately gleaned from the update:
a) There is a noticeable dip in the trend of the daily unique users on the network around September, that could be due to the ban on exchanges in China. However, we see the gap close somewhat in December.
b) The dip in price to match that loss in adoption was not as steep, resulting in significant gap between the price and the models, through the price run up to December. To the extent that many would label that a bubble, from the plot, that gap does not appear as large as during the 2014 bubble and as such the correction from it is unlikely to be as severe. The recent drop in price as the Christmas holiday was about to start appears to have brought the model and the price closer.
c) Both the Metcalfe’s law and the author’s model suggesting a fit based on the root of the exponent of the number of users model continue to approximate the network’s price trend. However, the latter model appears to be approximating the data slightly more closely at this point.
The next update will be in about six months. In the meantime, if you want to have the spreadsheet used for this analysis, you can simply request it via email from the author’s name, kenalabs7, at gmail.com. Simply follow and like this article and mention your medium name.
To use the spreadsheet:
(1) Update column B in sheet “MarketPrice” with the data on price (from a website with daily data such as https://blockchain.info/charts/market-price or if a programmer you can also write a simple program to extract the data yourself form the public blockchain)
(2) Update column B in sheet “BCHAIN-NADDU” with the data on Unique Addresses (same website has this info: https://blockchain.info/charts/n-unique-addresses)
In the Currency Law Sheet
(3) Extend the columns for the date (A), WMA for the unique addresses (B), price (F)
(4) Extend the columns for the growth function modeling adoption based on unique addresses (E)
(5) Extend the columns for the author’s network law (G) Metcalfe’s Law (H)
(6) Extend the charts that plot these columns
(7) Save or copy out any of the charts and print, as needed.
Update: On the macro level, to the extent that there is a gap between the value based on a network-effect model and the current value, that gap would place values at about 4K based on Metcalfe’s model and about 11K based on the root-exponential model. These values might represent the floor on a snap back to network value based on a network-effect model.
References:
1. Ken Alabi, July 2017, “Digital blockchain networks appear to be following Metcalfe’s Law”, Electronic Commerce Research and Applications, Volume 24, July–August 2017, Pages 23–29
https://doi.org/10.1016/j.elerap.2017.06.003
