In a fast-changing world of technological innovation, many have realised the potential of investing in digital assets, and as among all the other asset classes, especially the newest ones with a shorter track record and no suitable reference models, there is a lot of speculation about the price. We are certain that many of our readers are interested in finding a reliable and scientific approach to forecasting a highly speculative world of digital asset prices, and for this reason, we are showing three models that rely on power law functions on predicting the price. They take into account supply, demand and the cost of production, three essential elements for pricing any kind of asset in the economy. Our modelling earned the consideration of the financial community, when during Quant 2020 (and in an article that you can find on CoinTelegraph) we predicted a maximum Bitcoin price for the cycle at 63.000$, and we got closer to the real top of 67.000$ much more than other famous models around.

The first model that we want to present to you, is called Stock to Flow (S2F) model and bases its foundation on the Bitcoin scarcity, developed by the analyst called with the pseudonym PlanB. This model is flawed and struggle to make correct predictions, but we would like to go through it because it is useful to understand some mechanisms regarding the underlying scarcity and value. Namely, stok-to-flow takes into consideration the quantity of the Bitcoin already available in circulation (Stock), and the amount of the Bitcoin that gets mined each year (Flow). The relation between Bitcoin Market Value and the necessary number of years for Flow to replicate Stock (SF ration), can give us some useful information for the future. As an example, we show the original table used by PlanB about several metals and their Stock-to-Flow relations.

If we take a look at gold, we will see that current Stock of Gold in circulation is around 190,000 tonnes, while that yearly inflow (Flow) is approximately 3,500 tonnes, which gives us a SF ratio of 54. This means that it will take 54 years of gold mining to reach a current Flow present on the market.

Plotting the relation between SF ratio and the Market Value in the cartesian plane with both the axis in log scale, we get the following graph.

We can notice that both metals and Bitcoin follow their own lines (created with a power law regression) and that after each halving period, characterized by the rise in price, there is a consequent recovery in the price after which the growth reverts to its natural course. Although this model is not very reliable in the real world, its notion can give us a nice understating of the mechanics of scarcity.

The second model , the first one created by the research department of Diaman Partners is called Rate of Adoption model. The rationale here is rather simple, we know that Total Market Capitalization of Bitcoins equals:

i.e. the price of Bitcoin times the number of Bitcoins in circulation.

We can calculate the Market Capitalization also in another way:

i.e. the number of Bitcoin wallets in circulation times the average amount of money in those wallets.

In the above chart you can appreciate that the number of wallet Is increasing despite the price cycles, and even in the *crypto winter* in 2022 the number of non zero wallet continue to increase.

A different story of course is the average price in USD for each wallet, because the variation reflect the very high volatility of the bitcoin price along time; but even in this case, a power law regression can predict a future expansion of average USD for each wallet.

Thanks to ETFs approval in January, the bitcoin is creating more than 1500 new millionaire wallet a day, so you can easily understand the power of this dynamic.

To get the fair value price of Bitcoin based on rate of adoption, we can simply divided for the number of bitcoin in circulation the capitalization based on wallets and average price

Using the fact that the number of wallets is increasing, we will plot this relation in the cartesian plane as we did for our previous model and we get these forecasting bands:

The final model is based on Hash Rate Remuneration, and takes into the account the production costs of Bitcoin, that in this case is the “hash rate”, a measure of power of servers performing algorithms to secure each block on the blockchain (i.e Proof-of-work algorithms), and they get a reward of freshly produced Bitcoin in exchange of their work. Namely, if we have a bigger number of miners mining, this measure is larger.

This relation directly involves the cost of the production, in the form of energy input, and the reward the miners get for this effort, Bitcoin. However, bearing in mind that the available number of Bitcoins to be mined is decreasing over time, the hash rate remuneration graph is downward slopping. Similar to the Rate of Adoption model, here we will also be using the formula for the market value of Bitcoins, in this case the market value of all the Bitcoins to be mined. Our formula in this case is

and the formula for market value based on hash rate and remuneration is

Based on these estimates, the price of Bitcoin is driven by the supply and demand conditions, as well as the cost of the production, which gives it a fundamental foundation in an economic setting to consider the Bitcoin a good that deserve to exist and to be purchased or sold in USD or with another Fiat currencies.

These are our contributions on the topic but never forget to DO YOUR OWN RESEARCH!

The Diaman Partners Research Team

(published on April 2024 edition of __Crypto Entity Crew Journal__)

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