The idea behind stock-to-flow
Stock-to-flow comes from commodity analysis, where it is used to describe how scarce a resource is. Stock is the total amount that exists. Flow is the amount newly produced in a period, usually a year. The ratio is stock divided by flow, and it can be read as the number of years of new production it would take to match existing supply.
Gold is the classic example. A very large stock accumulated over centuries is added to by only a small fraction each year, so its ratio is high, often quoted as somewhere around 60, though estimates vary. Commodities that are consumed or produced in large amounts relative to what is stored have low ratios.
How it was applied to Bitcoin
A well-known version was published in 2019 by an author using the pseudonym PlanB, who applied the ratio to Bitcoin and proposed that its price followed a relationship with it. Bitcoin’s ratio rises in steps because each halving cuts the flow. Roughly speaking:
- Stock is the number of coins issued so far.
- Flow is the new coins created over a year, which depends on the current block subsidy.
A simple check using approximate figures after the 2024 halving: flow is about 164,000 coins per year (3.125 x 144 x 365), and stock is about 19.7 million. Dividing gives a ratio of about 120. Before that halving the flow was about twice as large, so the ratio was about half as high, around 60. These are rough figures, and different authors define stock and flow slightly differently.
The model then fitted a line through historical data, comparing the ratio with the market value or the price, and extended the line forward. In its original form, it suggested that each halving, by raising the ratio, should move price to a higher level. Some versions added different adjustments, for example comparing the ratio to a longer-term average. This article does not repeat any specific price the model produced, because the point is to understand the method, not to anchor on a number.
Why some found it appealing
Part of the appeal is simplicity. The inputs are known in advance, the story links an observable feature of Bitcoin to price, and the model seemed to fit past data well. It also supports the intuitive notion that scarcity matters, which is consistent with basic supply and demand reasoning.
The main criticisms
It treats price as a function of supply alone. The model has no place for demand, adoption, regulation, interest rates or investor sentiment. Prices are set by buyers and sellers, so a model that ignores demand cannot explain why prices rise or fall in any given period.
Few data points. The fit rests on a handful of halvings and a limited price history. With so little information, many different curves can look good on the past.
Spurious correlation. Two quantities that both trend upward over time can appear tightly related even when there is no causal link. Statisticians warn that regressions on such data, known as non-stationary series, can produce an impressive-looking fit that has no predictive power. Critics have argued that this describes the stock-to-flow charts.
Known information should already be in the price. Halvings are scheduled years ahead, and the supply schedule is public. If a market is reasonably efficient, widely known supply changes should already be reflected in prices, rather than creating a predictable jump afterward. How well that holds is debated, but it is a standard objection.
Out-of-sample failure. A model is tested by whether it works on data it did not see when it was built. Critics point to periods after publication when actual prices departed substantially from the model’s path. Defenders argue the model was never meant for short periods. Either way, a model that cannot be shown to forecast reliably should not be treated as a guide.
Gold is not a good analogy for everything. Gold’s value rests on long-standing demand for jewellery, industry and reserves. Applying the same ratio to a very different asset assumes the relationship carries over, which has not been shown.
Where it fits among forecasting methods
Stock-to-flow is one example of the kind of forecasting covered in the wider articles on prediction models and forecasting methods. Any model should be judged by how well it handles data it has not seen, how many assumptions it needs and whether its logic is sound. If you are looking at a chart that claims to project future prices, evaluating predictions with these questions is a healthy habit. Some research also finds that short-term prices behave much like a random walk, which makes confident forecasts especially hard.
What to take from it
The ratio itself is a legitimate description of scarcity. Bitcoin’s is high and rises with each halving. What is unsupported is the claim that it determines or reliably predicts price. Nothing here suggests how prices will move. Cryptocurrency prices are volatile, past performance does not predict future results, and people can lose all they invest. This article is educational and is not financial, tax or legal advice.