Understanding the rules by which crypto markets play
From Complex to Equilibrium systems. (1⁄2)
A lot has been said recently about the Complex nature of crypto markets. This publication aim’s at putting this idea in context, test its validity and think about the consequences for crypto investors.
Complex systems , short for Complex Adaptive Systems:
A system where the understanding of the parts does not automatically convey an understanding of the whole system’s behaviors.
A subset of nonlinear dynamical systems, Complex systems have been used to describe many natural phenomenons. From clouds and waves formations to cities and swarms evolution.
A few weeks ago, BlockTower CIO’s Ari Paul sent a tweet about his crypto investment strategy.
Implied here is common sense for: “if you don’t know the rules, then don’t play game”.
In all markets, classic and nascents, the game is about figuring the value of an asset ahead of the curve. Because eventually price converges towards value.
In traditional equities and bond markets the prevailing rule is that value is represented by cash flows.
The problem is that crypto markets don’t generate cash flows.
Without cash flows, crypto investors are left scratching their heads and wondering what prevailing rule or set of rules applies to their world. Eventually, these rules will dictate the terms under which token prices will converge towards blockchain value.
To figure out which rules could apply we start by investigating whether crypto markets are in state of equilibrium or in state of flux.
Equilibrium markets and flux markets have differentiated properties. Differentiated properties warrants for differentiated investment rules. As we will see, the answer to this question carries practical implications for crypto investors.
We start with present day crypto markets and will take a shot at future crypto markets in a later publication.
Equilibrium systems
We start with the hypothesis that crypto markets are best explained by equilibrium theories.
On the equilibrium end of the spectrum, we test the applicability of Quantity Theory of Money (QTM) and the Efficient Market Hypothesis (EMH).
- Quantity Theory of Money
The Quantity Theory of Money (QTM) states that the value of money is determined by the relation between supply and demand of money. For QTM to apply in crypto markets, an increase in token supply should lead to a decrease in its price — all else being equal.
This relation between supply and demand can be tested by conducting a regression analysis across key variables for token prices — while controlling for trend. Intuitively for QTM to apply, a regression analysis should return a negative coefficient for liquidity.
Sha Wang and Jean Philippe Vergne conducted such an experiment across five leading cryptocurrencies. They came to the conclusion that instead of being negative as predicted by QTM, the coefficient for liquidity was positive.
For the sake of our discussion, I’ve taken the liberty to reproduce the results of some of their findings in the table below. I strongly recommend anyone interested in the subject to read the full paper.
Sha Wang and Jean Philippe Vergne observed that in crypto markets, an increase in token supply is strongly associated with an increase in prices — all else being equal.
In short, Quantity Theory of Money it seems, isn’t applicable to crypto markets.
- Efficient Market Hypothesis
The Efficient Market Hypothesis (EMH) states that asset prices always incorporate and reflect all relevant information. For EMH to apply to crypto markets, new relevant information should always lead to a corresponding price update.
This mechanism for information pricing can be tested by observing randomness in token returns. For EMH to apply, return distribution should be standard normal — which is equivalent to exhibiting randomness.
There is enough publicly available information to conduct such an experiment at the market and Bitcoin level.
Conducting such a study shows that instead of being normally distributed as EMH would require, crypto markets returns exhibit high kurtosis and fat tails.
In other words, empirical data shows that in crypto markets, changes in token value are not random. Like the Quantity Theory of Money, the Efficient Market Hypothesis and its corresponding set of rules don’t apply.
Complex Adaptive Systems
The high kurtosis and fat tails that we’ve observed in the above distribution study are characteristic of Complex Adaptive Systems (Complex systems for short).
Michael Mauboussin, who was Chief Investment Strategist at Legg Mason before rejoining Credit Suisse and now with Blue Mountain, has been covering the subject extensively. In Revisiting Market Efficiency, he lists a few practical implications for investors in such systems. These implications are highly valuable investment rules to keep in mind and highly relevant to crypto markets. See below for a transcript of some of Michael Mauboussin’s work edited and adapted to crypto markets.
(1) Keep an eye on volatility and adjust accordingly :
Be careful with leverage, seek ways to hedge your portfolio. In Complex systems, even the most brilliant models can be undermined by extreme outcomes. There are more tangible risks for crypto investors then Beta. Think permanent loss of capital. Also, make Nassim Taleb proud, try to build your portfolio in an Antifragile way.
(2) Take a step back, use a telescope rather then a microscope when looking at markets:
Complex systems exhibit aggregation properties where the whole acquires properties that can’t be tied to any of the constituents. Put simply, the sum exceeds the parts.
Crypto investor should spend more time studying crypto markets as a whole and pay less attention to constituents. Think more in terms of baskets and less in terms of units.
By the way, aggregation also holds for market participants. A Complex system could be entirely made of below average investors and still function superbly. Overall performance of the past year makes everyone look and feel like a genius investor. Beware of false positive. Over the long run, most of the time you’d be better off investing in an index.
(3) Pay attention for diversity breakdowns and prepare to act upon them:
Complex systems are subject to feedback loops mechanisms. In other words, trends are self reinforcing.
For instance, Nic Carter identified possible reflexivity between token prices and onchain volume. We did the math and while the signal is weak, it `can’t be discarded all together. Once these signal become strong enough. Be ready to act upon them.
(4) Cause and effect thinking is futile :
Complex systems exhibit nonlinear properties. Meaning the smallest inputs can lead to large scale events. The recent drop in prices in crypto markets and the lack of unified explanation proposed by the market is a perfect exemple.
In such a light, beware of anchoring and availability heuristics. Odds are that you’re wrong.
Parting thoughts
The main goal here was to introduce the idea that crypto markets are in flux. Like modern physics, crypto markets are undeterministic in nature.
Which means that we have to forego the crisp and appealing deterministic narrative that comes with equilibrium economic models such as QTM and EMH. At least for now.
That being said the INET framework proposed by Chris Burniske and recently perfected by Alex Evans remains highly relevant. It does an exceptional job at articulating key issues that anyone designing or investing should keep in mind.
There is a lot more that should be done to further prove that crypto markets are Complex Adaptive Systems. There are also many more practical conclusions that can be drawn from it. Please consider this publication as a conversation starter only.
I hope that you’ve enjoyed the reading. And welcome all feedbacks!
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Of course, I’d also be happy with a small donation
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Disclaimer:
This document is intended for informational purposes only. The views expressed in this document are not, and should not be construed as, investment advice or recommendations.
