Ever since bitcoin became a phenomenon way back in 2017 (some may even argue before then), there have been attempts to forecast its price trajectory into the future. The most common way of course was to use linear projection. But there are many other approaches that may get better results, and through our close and long standing analysis and investing in the asset, we provide here a very brief exposition of how price projections can be done for this, and perhaps all fast growing crypto assets.
There are logs, and then there are logs
Chart 1: BTC price trend on linear log scale - not a bad fit |
We as a result, resorted to higher order polynomial log fitting to put the relationship back on a sound footing, starting with 3rd order polynomial log correlation. As one can see from the 2010-1 blue shaded areas in Chart 1, the gap was very large, but on 3rd order polynomial fitting, much reduced (see same shade in Chart 2). Further, the 2015-6 shaded area in Chart 1, although quite close to the actual price progression, was actually far from what a decaying trend should be, as seen in the distance between price and the 3rd order polynomial approach below:
Chart 2: BTC price trend on 3rd order polynomial log fitting – somewhat improved correlation |
What is more, the average distance overtime between the trend line and actual price is now better at 69% compared to 168% using the linear approach. A pat on the back!
The story does not end here – as progressive price cycles set in – one can now count four cycle peaks and 4 cycle troughs from 2010 to date – the ability of the 3rd order polynomial approach to curve fit disappears. By adding orders of degree to the trend equation, we may play catch up, but that really is too mechanical and ultimately unwieldy, even though the degree of fit did improve (variance reading improved from 69% to 62%), here is 5th order fitting:
Chart 3: BTC
price trend on 5rd order polynomial log fitting – better fit still! |
Stock to flow model also has issues
Chart 4: Stock-to-Flow modelSource: https://buybitcoinworldwide.com/stats/stock-to-flow/ |
Commodity or currency, it is a monetary phenomenon
In other
words, we should increasingly factor in availability of money as an input
variable for any projections of bitcoin prices. The other key variables we deem
important in this calculation include also:
a) the number of non-zero balance bitcoin addresses, this acts as a proxy for
the level of adoption in the population at large, and would be useful in
modelling the network effect of rising usage of the asset;
b) number
of BTC in issue, which obviously is increasing, but at a ever diminishing rate.
In a sense, this metric is a bit like the money supply element of fiat
currencies, where more supply debases the value and resulting in higher prices.
As an overall
comparison, we put the three factors on the same scale so as to visualise the
comparative impact they may have had on the price of Bitcoin in the recent
past:
Chart 5: BTC
price in the context of demand (# of addresses), supply (# of BTC in issue),
and fuel (Global M3) |
Obviously,
some factors will have stronger influence over Bitcoin price at different
phases of the asset’s history, eg the number of addresses featured heavily in
the 2010-2 period when the growth was highest, while in the longer run, it may
well be money supply which will dictate future price changes, when for example
the user and supply growth both taper off.
In compiling
the monetary facet of the input variables, we picked only the top 5 economies
as proxy, but obviously more countries might produce better results. We will
for now stay at the more manageable level this stage in our adventure. Here is
the composition of the top 5 countries by M3:
The new Grand Unified Theory of BTC?
Chart 7:
BTC price as predicted by our improved factor inputs |
Chart 8: BTC
price as predicted by our “grand unified theory” model |
The
advantages of the new methodology are multiple: a) it is not purely
statistically driven; b) the model inputs are relevant and quantifiable; c)
input variables allow for significantly more real world twists and turns in the
fitted line which the statistical method is incapable of producing. Perhaps at
some stage, this new grand unified theory will be useful as an input in our
investment models…
We are definitely not finished in this quest for perfect modelling of Bitcoin prices, and there is a lot more work to be done, we leave you with a mystery chart that seems to do away with the problem of decaying exponential growth:
Chart 9: another elegant way to present linear BTC price trend |
沒有留言:
發佈留言