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Bitcoin Valuation by Savings Adoption

From CoinShares Research Blog by Christopher Bendiksen

Executive Summary

  • Our existing toolkit for financial modelling has tended to fall short when applied to bitcoin
  • Even the more theoretically defensible models have issues with timing and progress-tracking based on fundamentals — making them more reasonable over the long term, but difficult to use over the short and medium term
  • We have attempted to fill this gap by using two fundamentals — adoption levels and savings allocations — to model inflows into bitcoin at a global level over the next 5 years
  • Using all available bitcoin adoption data and simple assumptions regarding growth and savings rates, we arrive at a 5-year outcome where today’s ~270m bitcoin owners (~3% of the global population) have grown 20% annually into ~670m owners (~7% of the global population)
  • If each modelled bitcoin owner allocates an ~$86/year to bitcoin (<1% of gross national income for each country, individually), our approach suggests that such flows, at a 10x flow-to-market-cap multiplier could support a bitcoin value floor of ~$133k in 2028

Bitcoin is something entirely new, something never before observed. Because of this, many of the trusted tools in our financial toolkits might simply not do a very good job of modelling its performance, and it’s causing apprehension among analysts who are used to deploying certain modelling tools to make sense of asset pricing.

A commodity monetising globally is not something that’s been seen before, at least not since economics has been a discipline and certainly not over such a short time. But it’s not an unexplored or misunderstood concept in itself. What’s been hard for a lot of people, is to conceptually grasp that bitcoin is indeed a digital thing that is in fact monetising and that is where its increase in value comes from, rather than cash-flows, usage as a tax utility coin, cost of production, or anything else.

This does not mean that there is no value in modelling bitcoin price. It just means that we might have to make do, so to speak, with approaches that are uncommon or unfamiliar to many financial professionals. We have been writing about bitcoin valuation for years, and we believe that we have a solid understanding of what’s been tried, what has been shown to work, and what has been shown to be unhelpful.

Some examples of approaches that have struggled to add value to the discussion are Metcalfe’s Law, and the now infamous Stock-to-Flow (S2F) model. In the case of Metcalfe’s Law, it’s not so much that the theoretical grounding is unsound per se, it’s more that its requisite data is very hard to sample and — on top of that — is extremely noisy. Metcalfe’s Law may indeed apply to network-based money, but it’s not an approach that historically has successfully been applied to money.

The S2F model, on the other hand, struggles to find a sound theoretical grounding, the outcome being that it basically amounts to over-extrapolation of past performance. Hardly something worth spending too much time on, even if the ‘fit’ has been periodically good.

At this point, we would argue that the only approach that has achieved widespread agreement with regards to applicability and theoretical grounding is the Total Addressable Market (TAM) model. While simplistic, the approach at least makes sense, and it enables modelling of bitcoin’s ‘terminal’ value using only a small set of assumptions. We’ve written about TAM-based bitcoin valuation several times over the years. As always however, a major problem with calling TAM a valuation model is that you have to address the likelihood of it successfully competing and base that assessment on something firm.

That is all fine and well, but the problem is that a terminal value with some likelihood of success assigned to it, is still set at some arbitrary point in the future, and if we’re being realistic, this is likely quite far into the future. How far? Well that’s half the problem — there’s no reasonable answer to that question. So while we’re able to estimate bitcoin’s terminal value at some end state sometime in the future, we’re left with a huge gap of time-based uncertainty in the meantime.

Our Mid-Term Approach is an Adoption-Based Valuation Model

In order to plug this gap in time, we’ve developed a mid-term valuation model that uses bitcoin adoption, savings rates, and a flow-to-market-cap-multiplier to estimate future bitcoin value over a 5-year period. The basic idea is to use bitcoin adoption as a savings tool by retail investors as a model for sticky, non-speculative inflows over a short- to mid-term period.

Our arrival here has taken some conceptual groundwork. First, we’ve been adamant about wanting a valuation model that rests on some kind of measurable fundamentals, and as little vibes as possible. The kind of obvious fundamental here is adoption, but this has been a notoriously difficult thing to measure, let alone as a time series.

Second, we wanted the model to be grounded in bitcoin usage as money, not as a pure speculative object (although we fully recognise that this is not a binary distinction). We therefore landed on long-term savings as our generator of flows into bitcoin. In our model, these savings flows result from increasing adoption creating increasingly larger ongoing bitcoin purchases as more and more people allocate a small amount of their income towards bitcoin savings every year.

Using this as our foundation, we hope to be able to model demand in a manner that strips out as much speculation as possible while resting squarely on what we consider to be the fundamental investment case for bitcoin, namely its increasing monetisation as a form of global money. In other words, we are modelling bitcoin savings usage as our inflow driver — the form of bitcoin usage we consider the stickiest, and the least likely to gyrate with speculative fervour.

The model builds on our bitcoin adoption meta-study from 2023, and adds in savings rate assumptions based on data from the World Bank as well as some previous empirical studies on the relationships between inflows and market caps in markets that are not highly elastic.

Simplified, the model looks like this:

Now let’s look at our assumptions in a little more detail.

Adoption Rate

Our first assumption is that bitcoin adoption rates will grow at a set pace from some existing base. We take this base to be 269m global bitcoin users at the end of 2023 (we’ve chosen to assume zero growth in 2023), unequally spread across all countries in the world according to our findings in the above-mentioned adoption meta-study. The assumed growth rate is selected based on two data points:

  1. The existing 7-year average growth rate we found in our meta-study, and;
  2. The average second-decade growth rate of the Internet, with the browser revolution of 1996 acting as the starting date

In our study, we found a 7-year compound annual growth rate (CAGR) of 146% for global bitcoin adoption between 2016 and 2022. When we looked at second-decade Internet growth rates, we found numbers around 15% on average, annually. Taking those two together, we’ve arrived at a 20% CAGR for global bitcoin adoption being a reasonable assumption for growth over the next 5 years. Under these assumptions, global adoption figures would look like this:

At the end of our 5 year valuation period, the total number of global bitcoin users would stand at 669m, approximately 7% of the global population, versus approximately 3% today. This adoption rate would be applied equally to all countries such that the relative distribution of bitcoin ownership between countries would be exactly the same as today. In other words, even if at the end of the model period the total global number of owners would have approximately doubled, the percentage of Indians owning bitcoin would still be about double the percentage of Australians owning bitcoin.

And as you can see, ownership is not equally distributed among countries. In fact, ownership tends to be highest exactly in the type of country where you’d expect it to be — that is, in those where the local fiat competitor tends to be bad. As mentioned above, we have assumed that this distribution remains unchanged.

Savings Rate

The next component in the model is our savings rate assumption. Here, for each country in the world, we have found two economic metrics:

  1. Gross National Income per capita, not adjusted for purchasing power parity, and;
  2. Average National Savings rates over the last 5 available years

We then assumed that out of the average savings rate, 10% would be allocated to bitcoin. So if a country’s savings rate is 10%, 1% would be allocated to bitcoin. On average, the actual numbers come out to be a little bit less than 1% of the global GNI per capita.

What we are doing here is establishing an annual bitcoin purchase rate per owner. These rates differ by country depending on GNI per capita and average savings rate. Rich countries with high savings rates would result in large flows per owner for that country, and if their adoption and population size are also high, they would contribute strongly to overall flows. Poorer countries with low savings rates, small populations and low bitcoin adoption would do the opposite, contributing very little to overall flows.

For absolute clarity, let’s look at two examples. Say that a country has a GNI of $10,000 per annum, and a savings rate of 10%. Our model would then assume that $100 is allocated per bitcoin owner, per year. Below you can see a table of what these allocation sizes would look like for a small selection of countries with different income and savings rates. If another country has a GNI of $20,000 and a savings rate of 1%, the resulting flows for that country would be $20 per owner per year.

If, as in the first example country, it has a high population and high levels of bitcoin adoption, it would contribute strongly to overall flows. If, as in the second example country, it has the same population and level of bitcoin adoption, they would contribute much less to overall flows.

So after we use GNI and savings rates to estimate the average allocation size in each country, their population and level of bitcoin adoption further determines their contribution to overall flows. Therefore, it is the balance between how much people save, how prevalent bitcoin is in each country, and how many people live in each country that impacts bitcoin’s ongoing flows and value accrual.

Source: CoinShares Research

Note here that, as we mention above, each country in the world has its own individual current bitcoin adoption level. Moreover, bitcoin ownership is more common in emerging economies than in developed ones. This means that even with ownership at close to 700m at the end of the model period, most of these owners would still live in low to middle income countries, giving a rather low global average bitcoin allocation rate per capita ($88/year).

When summed across all countries, each with their own savings rates and bitcoin adoption levels, the total yearly flows look like this:

Source: CoinShares Research

In sum, the estimated inflow over the five year period is ~$210bn.


The final piece in our model is a flow-to-market-cap-multiplier. The way to approach this problem theoretically is to keep in mind that flows can only purchase available supply. One of the interesting properties of Bitcoin is that we can read directly from the blockchain which coins are likely for sale and which ones are likely not.

In the above chart, we’ve charted out the total number of coins (out of ~19.3m) that have moved over the last year, and the total number that have remained inactive for over a year. Assuming that coins that have not moved in the last year are not — at least not immediately — for sale, we can estimate that only about 6m coins are currently ‘in the market’. It is however not possible to know at what price active coins are available

Since no material amount of new supply will be added to the total number of bitcoin (only ~1.7m new bitcoin remain unmined), if a significant amount of inflows start chasing a limited number of coins, the only way to satisfy demand is via upwards re-pricing — thereby enticing current holders to bring dormant coins onto the market.

Even though this theoretical grounding makes sense, it has proven infeasible to determine exactly how inflows impact price. There is simply not enough data available on dollar inflows to determine this relationship with precision. We have therefore elected to use a simple multiplier, estimated from empirical studies done on assets that are similar enough that we consider the assumption reasonable.

Sources: BofA, NBER, Janus Henderson, CoinShares Research

The above studies have all looked at assets that have some degree of inelasticity in their supply profile. Using ETF flows as a proxy for general inflows, they’ve estimated the impact each dollar of inflows has on the total market cap of the asset. As you can see, the less elastic the supply of the asset, the higher the multiplier, which makes perfect sense.

While it might actually be the case that bitcoin’s multiplier is as high as BoA’s estimate, or even that it’s as high as their estimate for gold, we find those multipliers a little too aggressive, and so in the name of conservatism, we have chosen a 10x multiplier as a reasonable assumption.

And so we have arrived back at our overall model:

Now let’s have a look at the results.


Just to recap our assumptions, we’ve assumed 669m bitcoin owners by 2028 (a little more than twice the current number of estimated owners). We’ve then assumed that each bitcoin owner puts a little under 1% of their GNI towards bitcoin each year. This comes out to be $88 per person on average, and a total of $210bn for all owners over five years. The last assumption is that these flows generate a 10x uplift in market cap over that period.

Source: CoinShares Research

Using a starting price of $28,880 (the average 2023 bitcoin price), these flows translate into a bitcoin price of ~$133,000 at the end of the five year period. Year five here would be 2028, and year one would be 2024. It’s important to note that we do not consider this to be a bull case. Rather, we consider this type of bitcoin usage as a savings tool to be bottom-supporting, not peak-driving. In other words, using this type of adoption- and savings-based approach is more of an estimate of price bottoms under the chosen assumptions than it is an estimate of price tops.

With this method we argue that we’ve adequately filled the time scale gap in bitcoin’s existing valuation approaches. The method is simple, relies on very reasonable assumptions, and returns outputs that are entirely plausible given the current state of the world.


The information contained in this document is for general information only. Nothing in this document should be interpreted as constituting an offer of (or any solicitation in connection with) any investment products or services by any member of the CoinShares Group where it may be illegal to do so. Access to any investment products or services of the CoinShares Group is in all cases subject to the applicable laws and regulations relating thereto.

Although produced with reasonable care and skill, no representation should be taken as having been given that this document is an exhaustive analysis of all of the considerations which its subject-matter may give rise to. This document fairly represents the opinions and sentiments of CoinShares, as at the date of its issuance but it should be noted that such opinions and sentiments may be revised from time to time, for example in light of experience and further developments, and this document may not necessarily be updated to reflect the same.

The information presented in this document has been developed internally and / or obtained from sources believed to be reliable; however, CoinShares does not guarantee the accuracy, adequacy or completeness of such information. Predictions, opinions and other information contained in this document are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Third party data providers make no warranties or representation of any kind in relation to the use of any of their data in this document. CoinShares does not accept any liability whatsoever for any direct, indirect or consequential loss arising from any use of this document or its contents.

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