Our Model | Portfolio Optimization
I’ve been writing and talking about bargains this week. To be clear, I don’t know if we’re at a bottom in the market, but it does make sense to start building a shopping list.
Paid subscribers (DIY-er and up) received my state of the market update, which covered the counter-intuitive way that interest rate hikes and wars are usually good buying opportunities (if you wait a little bit to enter).
Crypto Riders and up will obviously receive my signals for when to enter the market, and they’ve been pretty good. The AOTB portfolio (because it has a 20% long-term hold) is down 11% this year, but at the time of writing, BTC is down 20%. So, the AOTB strategy is about 2x as good as the benchmark with less volatility (= smart money).
I’ve long been interested in finding contrarian signals to compliment my basic strategy. I do have one and it’s super accurate. The problem is that it’s also super rare (once every 2 years, basically).
The holy grail, of course, is finding a workable intrinsic value model–one that likely turns on measuring network effects. I wrote a whole piece on this as part of the Absolute Beginner’s Guide to Bubble Trading. In brief, it’s possible to use the first half of Metcalfe’s law to value cryptos, but it’s nearly unworkable in practice.
For this week’s piece, I wanted to look briefly at Fidelity’s historical trend model and assess whether it makes any sense. To anticipate, they found an historical substitute for the second half of Metcalfe’s law and it just might work.
So, let’s get to it.
What is the Model?
Here’s the output of their model in an image.
To understand what’s going on, it might help to back up and discuss the two parts of Metcalfe’s Law. The first is often discussed as a way to assess the intrinsic value of a network. The idea is that each network increases in value, exponentially, as more people enter. So V = N^2 (where V is value and N, the number of nodes on the network).
Of course, this is an N^2 rule, so that in practice, you scale it by some constant k, typically derived from some sort of regression analysis. Yes, the practical application of the law is a little messy.
Even messier, for Bitcoin and other cryptos, is the existence of tumblers (like Blender.io). These don’t necessarily exist for nefarious purposes. They just keep most people from snooping around. Unfortunately, they also add fake “nodes” for a Metcalfe analysis.
Reconstituting what the real nodes are (minus tumblers) proves a non-trivial task. You need a full-time operation to do it. And even when it wasn’t such a problem, I found that incorporating this “intrinsic value” metric into my algos didn’t improve them too much.
Enter the second half of Metcalfe’s law (or at least, the second half of his discussion of network value), namely network growth. He holds that the growth of a network follows the pattern of a sigmoid function (like most things in nature)–a typical example is: S(x) = 1/1+e^-x.
Now, this idea would have to be parameterized too (remember the ‘k’ from the first law?). How to do that could again be an exercise in regression, but it might also follow from the extrapolation of historical data. I think that’s what Fidelity’s analysts did.
The one reason I can’t be sure is that their graphic doesn’t look like a sigmoid function. I don’t have access to their dataset, of course, but that’s what appears to be happening is that the ‘S’ curve is smooshed because of the exponential axis on which it’s plotted.
Even if it’s not a sigmoid function, it’s aiming at the same purpose as the second half of Metcalfe’s law, namely to assess the growth of a network. If larger networks are more valuable, then a price lag below the growth of the network suggests that market participants are looking at a bargain.
It’s a bit hard to tell from Fidelity’s graphic, but it suggests that BTC should be worth somewhere in the range of $50k to $80k right now–at least if it tracked other historical networks in growth. So, at the top end, BTC is about 50% below what it should be.
Read the other way around, BTC should do 2x to reach its intrinsic value.
That doesn’t mean BTC won’t continue to slide down because of the pressure from the Ukraine war and the impact of interest rate hikes. It does provide a window on how much BTC might have yet to grow just to catch up to historical trends. And what holds for Bitcoin tends to hold for every other coin, just that they have yet further to grow.
Overall, then, I find the Fidelity analysis a strong one. I might try to develop a similar model myself. It doesn’t account for the many possible threats the crypto world faces, but it performs well at what it intends.
As I’ve been writing all week: ready your shopping lists.
This week I wrote a number of pieces that are related to this post, so you might want to have a look if you’ve missed them.
That’s it for this week. Remember to join us on Discord if you haven’t already.
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