Bitcoin supply, demand, and price dynamics
We develop a bottom-up, quantity-clearing model of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior.

Here is the SSRN link for a new working paper now out: https://dx.doi.org/10.2139/ssrn.5386623
This is a followup paper on our first modeling paper: https://doi.org/10.3390/jrfm18020066
For a quick rundown on the content, see my thread on X: https://x.com/DrMurrayRudd/status/1955312083932942417
I will be posting various example scenarios and further content over the next while - follow me on X to get messages of when these come out.
Here is the abstract for the paper:
In this research, we develop a bottom-up, quantity-clearing model of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. The model relies solely on first-principles economic supply and demand dynamics rather than assumptions about Bitcoin price appreciation, its historical price trend, or its potential effectiveness in demonetizing other asset classes. To estimate Bitcoin price and market capitalization, we considered five key variables: market demand shifts, intertemporal investment preferences, withdrawal sensitivity, initial liquid supply, and daily withdrawal levels from liquid supply. A Monte Carlo simulation that randomly sampled across all five key variables found a 75% likelihood that Bitcoin price will exceed US $4.81 million by April 2036. Generally, prices from the low single-millions to the low tens-of-millions per coin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below 2 million Bitcoin and withdrawal sensitivity is low. Cross-model triangulation shows alignment with forecasts from institutions at the cutting edge of Bitcoin investing. We identify measurable state variables and policy-neutral levers – time-to-threshold metrics, withdrawal sensitivity estimation, and encumbrance mapping – that could help better locate where right-tail risk concentrates and steer trajectories toward steep, but-bounded, price appreciation.
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