Within the Gavekal 4 Quadrants’ framework there is no discussion today that the world economy has moved up the chart from the disinflationary lower quadrants to the inflationary upper ones.
The next question thus becomes: right or left? In other words, boom or bust? The answer to this question has historically paved the way for reasonable hopes or disasters.
Occasionally, Gavekal-IS publishes a seminal paper when deep financial instabilities light up from our quantitative research. For instance, February 2021’s “US PE Expansion: Game Over!” based on the statistical evidence that US inflation was about to kill equity multiples, as well as July 2021’s “Risk Off” on the statistical evidence that equity value for risk was shrinking. Today’s “World Crash Ahead” is the third of this trilogy.
Five conditions are met for a global market collapse.
You take this route in your car every morning. It is second nature to you, controlling the risk of any collision. And then one day, a bike suddenly steers too far to the right. Your adrenaline kicks in, you brake, you swerve, and just barely escape an accident.
Here is my first bet: You will drive very carefully the rest of your route and the next day and even the days following that, until, gradually, you return to your usual habits.
And my second bet: The same thing occurs on the VIX and all volatility indices. The way our memory of stress amortizes gives the price of the VIX – and gives it better than the consensus.
US CPI at 7.5% YoY and 5% in Europe, oil at more than $100 a barrel, Ukrainian refugees by the millions, wheat prices up 30% this year revealing possible food shortages to come in the Maghreb, European governments over-indebted by the management of the Covid crisis which, therefore, can only mean minimalist support measures in the face of the energy shock – a shock that will lead to stagnation in the next six months (likely recession), and FED raising rates by 0.25% this month – the first in an expected series of ten successive hikes – which cannot contain inflation as real interest rates will remain deeply negative… Despite the warlike rhetoric of Mr. Powell, central banks have lost the upper hand.
We are back to the real world.
At 15% drawdown in equity markets compared to a market high, the probability of an extension of the drawdown by an additional 20% in the coming year is multiplied by a factor of 4 to reach 20%.
At 30% drawdown, by a factor of 10 to reach 50% (reference “The Snake That Bites Its Own Tail,” published in September 2021).
Risk begets risk.
Here are some crash risk indicators to follow daily in the highly troubled period we are currently experiencing.
Two macro factors – one economic and the other monetary – have each historically weighed on stocks multiples. These are inflation and the contraction of liquidity. The combination of the two factors today puts equity markets in serious danger.
What should central bankers do, stop contracting at the risk of runaway inflation? Continue contracting at the risk of a market crash?
So hard to start a new year! The counters are reset to zero. Nothing to preserve. Everything to gain – or lose. And how does one manage the investment risk when there is no accumulation of profits yet?
Here is the simplest of principles.
The S&P 500 can be danced like a 3-count waltz: 1 (corporate profits), 2 (price of primary energy), and 3 (interest rates).
Last week, we analyzed the market sensitivity to rising interest rates, all other things being equal. However, rarely are all other things equal.
This week, we integrate the three rhythms, with the conclusion being the cost of primary energy is growing too fast.
You can only purchase two things in the markets: a contract (for example a 10-year bond), or a title of property (like a share). I draw this observation from our trusted source, Charles Gave.
However, you cannot value both in the same way, simply because the first has a finite duration (here 10 years), and the second is a perpetual.
The consequence is of upmost importance when rates are very low – as is the case today – and when they begin to rise (for example from 1.5% to 2.5%), which could be the case for the United States in 2022.
In this scenario – all other things being equal – the contract loses less than 9%, but the title deed loses 40%.
Nobel laureate Harry Markowitz’s Modern Portfolio Theory (MPT) will celebrate its 70th anniversary this year. It has revolutionized the finance industry by formalizing the principle of diversifying an investment portfolio and taken up much of the computing time of the world’s powerful financial computers and the minds of managers for decades. It’s the free lunch of finance.
Today, we tackle this mountain by proposing a new slope in which to climb it: The Intelligence Portfolio Theory (IPT).
The difference between MPT and IPT is ontological. The first focuses on the statistical effects of randomness; the second focuses on the self-organizing intelligence of interacting systems.
Randomness, as we shall see, is a mathematical convenience of ignorance, and this convenience presents many false noses.
Cette lettre est la première d’une série qui prolonge, étape par étape, la Théorie Moderne du Portefeuille. Elle s’adresse aux gérants de portefeuilles financiers et aux risk managers.
Nous commençons par un morceau de choix : la corrélation interne d’un portefeuille. C’est la variable clé qui détermine le risque de crash financier.
Depuis Harry Markowitz, les gérants de portefeuille pensent connaître la solution pour leurs investissements. Choisir des titres les plus diversifiés possibles. En d’autres termes, zéro coopération, zéro corrélation.
Mais comment mesurer la corrélation ? Et surtout, comment la mesurer en temps réel ?
This letter is the first in a series extending step-by-step the Modern Portfolio Theory. It is aimed at financial portfolio managers and risk managers.
We start with a choice piece: the internal correlation of a portfolio. This is the key variable determining the risk of financial crash.
Since Harry Markowitz, portfolio managers think they know the solution to their investments: choose the most diversified assets possible. In other words, zero cooperation, zero correlation.
But how do we measure the correlation? And furthermore, how do we measure it in real time?