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Can three models accurately predict Bitcoin price?
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Can three models accurately predict Bitcoin price?

Earlier this year, I discussed one of my favourite models for price prediction: the Bitcoin stock-to-flow ratio.

The model, used to calculate the stock-to-flow of assets with hard caps, was popularised by the prominent Bitcoin analyst PlanB.

Today, I’d like to cover two additional price models which may shed additional light on the future price of Bitcoin.

Please remember that my only goal is to explain these models to help you understand them, not to make future price predictions.

Random ‘black swan’ events can happen at any time and cause major price movements.

A great example is the impact of Mt Gox on BTC price during 2014 and 2015. It completely shattered the market, causing price to drop for a good two years due to close to 90% of all BTC in circulation being lost.

Could a price model have predicted such an event? Definitely not.

Still, the three models I discuss below have so far stood the hardest tests any model can face: time and black-swan events. The three models are:

In essence, in the stock-to-flow model shown above (courtesy of, the stock is the size of the existing reserves of an asset and the flow is the yearly production.

Looking at Bitcoin, the stock is the number of circulating Bitcoins – at the time of writing, this is close to 18,000,000 – while the flow is the number of Bitcoins produced in a year, which currently amounts to an average of 657,000 BTC per year at the current block reward of 12.5 BTC.

Gold has the highest stock-to-flow ratio with 62, as it would take 62 years of production to reach the current level of gold stock. Silver has a ratio of 22, while Bitcoin is currently at 27. These high stock-to-flow ratios make gold, silver, and Bitcoin monetary goods.

As explained by PlanB:

“A statistically significant relationship between stock-to-flow and market value exists. The likelihood that the relationship between stock-to-flow and market value is caused by chance is close to zero.”

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