CNBC The Exchange With Kelly Evans, SIR Models, and the Epidemiology of Financial Crises
By Dr. John Rutledge
Summary: Recently, on CNBC’s The Exchange, Kelly Evans and I discussed the impact of the coronavirus on global markets and how investors can manage their way through the crisis. You can see a video clip of our conversation by clicking this link. Also, if you are the nerdy type like me, you can read about the contagion models that epidemiologists use in the second part of the post.
(As a side note, my old friend Bill Griffith, who anchored Closing Bell with Kelly, once told me she is the smartest journalist he has ever worked with. After working with her many times, I can’t disagree. Check out Kelly’s show at 1PM ET – you won’t be disappointed.)
To be honest, the coronavirus parameters are not good. It started in China, one of the most densely populated countries on earth. And the incubation period of approximately two weeks is long enough to allow an infected person to travel great distances and infect many people before they even know they have it. But there are two important additional control variables in play with the coronavirus:
- Chinese politics- Local officials initially covered up information about the disease, allowing it to infect more people.
- The authoritarian nature of the Chinese government allows Xi Jinping to take more aggressive actions to contain the spread of the disease than would not be possible in most other places. Locking down economic activity in a province of 59 million people and quarantining people in their homes wouldn’t work in many places but definitely slows the spread of the disease.
My first point to Kelly was that we are all scared by the coronavirus because nobody knows what’s going to happen and that when we are scared, we make dumb mistakes. So, let’s take a minute to separate the fear and ignorance from the fundamentals.
Fundamentals. Stocks are Long-Duration Assets
As for the fundamentals, we know that the closed stores and shut-down factories in China are going to have a huge impact on economic activity in China in the coming weeks, months, and quarters. And it will have a meaningful impact in other countries on the performance of companies that are directly impacted by the virus inside China, (e.g., Starbucks’ store closings) and companies affected by supply chain blockages (AirPod parts for Apple, components for Honda and Nissan). But we also know that the epidemic will most likely be a short-term headline, that the Chinese central bank has backed a truck up to Chinese banks so they can shovel liquidity where it is needed, and that the Chinese government is one of the few in the world that can quarantine a city of 6 million people with a phone call.
There’s one more fundamental that we need to remember. When you buy a stock, the price buys its entire stream of future free cash flow. Unlike a bond, there is no maturity date when you get your money back. And unlike a bond, a stock’s cash distributions, either as dividends or stock buybacks, grow over time (at least you hope they will). That means the duration of a stock–roughly the number of years of a company’s free cash flow you would have to collect in order for the present value of those collections to equal half of today’s stock price–is much larger than the duration of a long-term Treasury bond. Duration is a good measure of the sensitivity of an asset’s price to a change in the interest rate used to discount future cash flow. For example, the price of an asset with a duration equal to ten will fall by ten percent if interest rates rise by one percentage point. The duration of a typical bond fund is 5-7 years; the duration of the 10-year Treasury bond is a little over 9 years. But the duration of the S&P 400 non-financials is more than 40 years, which explains the extreme sensitivity of stock prices to changes in interest rates.
More simply, stocks last a long time so whatever happens to this year’s profits doesn’t matter much as long as it doesn’t affect expected profits in later years. The caveat, of course, is that this is only true if whatever happens this year doesn’t push the company into insolvency and kill it, which is especially relevant for highly-levered companies. But for strong companies it gives investors an opportunity to buy shares in great companies at a discount when others freak out about what is likely to be a short-term problem.
Examples include Starbucks (SBK) and its Chinese competitor, Luckin Coffee (LK). Both companies closed all their stores; both will survive and thrive. Another is Alibaba (BABA), perhaps China’s top world-class company. Another is Tyson Foods (TSN), a direct benefactor of both the other flu that has killed half of China’s pigs and the Phase 1 trade deal. All were hit hard by the coronavirus fears; all will still be around after the flu has been tamed.
Of course, all of this assumes that you either have nerves of steel or a medicine cabinet full of beta blockers, so you don’t freak out too.
Pandemics are Cascading Network Failures
Now for a few of the thoughts that didn’t make it into the show. I know I will be accused of being morbid but as much as the coronavirus is terrifying, depressing, confusing, devastating, and all the other ‘ings’, it is also interest-‘ing.’ I have an entire shelf of books on the history of plagues and disease to prove it.
I have no more idea how fast and how far the virus will spread before it runs out of steam than anyone else, but I do know some interesting things about contagion. I teach a graduate course in far-from-equilibrium systems dynamics where we spend a lot of time reviewing scientific papers on the mathematics of contagion, which boils down to how fast a signal (in this case, the virus) can be communicated across an information network (a population), by studying the behavior of a class of mathematical epidemiology models known as (SIR).
Plagues, SIR Models, and the Epidemiological Threshold
The SIR model has been around since Kermack and McKendrick proposed it in a (1927) paper, “A Contribution to the Mathematical Theory of Epidemics,” to study the rapid rise and fall in the number of infected patients in historical plagues and epidemics such as the London plague (1665-1666), the Bombay plague (1906) and cholera in London (1865). You can read about SIR models by clicking here. And you can play with your own models using the free agent-based modeling software called NetLogo by clicking here.
Since then, SIR models have been refined and used to study many historical epidemics. Examples include: the Antonine Plague (5-10M deaths, 165-180 AD); the Plague of Justinian (45-50M deaths, 541-542 AD); the Black Death (75-200M deaths, 30-60% of population, 1331-1353, which caused a labor shortage so severe that some credit it as the cause of the end of the feudal order and rise of the British middle class, collapsed the price of rags–when people die they leave their clothing behind–in the Rotterdam rag market, and drove down paper prices, accelerating book publishing in Europe); the Mexican epidemics (7-17M deaths in two waves, 1545-1548, and 1576-1580); the Great Plague of London (“only” 100K deaths, 1865-1856, but famous because, while holed up in his home, Isaac Newton invented both differential calculus and the theory of optics); the Spanish flu (up to 100M deaths, 1918-1920); the Asian flu (2M deaths, 1957-58); the Hong Kong flu (1M deaths, 1968-1969); SARS (500 deaths, 2002-2004, a coronavirus); MERS (500 deaths, 2012, a coronavirus); and of course HIV/AIDS (30M deaths, 1960-present).
In an SIR model, a specified number of agents wander around a closed landscape and occasionally bump into each other (think Brownian motion.) Each agent (person) is in one of three states: 1) Susceptible, meaning the agent doesn’t have the virus but is susceptible to catching it if it comes into contact with an infected agent; 2) Infected, meaning the agent has been infected with the virus and is capable of passing it on to a susceptible agent; and 3) Recovered, one who has been infected and recovered or is now immune. A probability is assigned to each encounter. For example, there is a probability that an “S” will become infected should they bump into an “I” and a probability that an “I” will recover from the disease and become an “R”.
The most important control variables for contagion are 1) density, the number of agents occupying a given unit of landscape, 2) activity, how quickly a given agent moves around the space, and 3) incubation period, the length of time an infected agent can wander around infecting other people without them being able to know the person is infected. Jointly, these metrics determine the critical metric for all epidemics known as the epidemiological threshold (ET), which measures the expected number of people that a single infected person will in turn infect before he/she either recovers or dies. If the ET is less than one, the epidemic will eventually die out. If it is greater than one, the epidemic will spread across the landscape.
SIR models have also been used to study contagion across networks in all kinds of situations, from ecological collapse, to forest fires, to financial crises. Their properties are very well known.
Financial Crises are Plagues Too
My main interest is applying them to the study of financial crises. In two brilliant papers in 1939 and 1945, Friedrich von Hayek described a market economy as a communications network that efficiently transmits information about changing wants and scarcities to just the people who need it so they can make decisions and do their jobs. (von Hayek was also the inventor, in an unpublished manuscript written when he was 19 years old, of what we today refer to as “neural networks.” It was published in 1952 as “The Sensory Order.”)
Network theory has blossomed into a rich area of research since von Hayek’s papers, especially in physics where thousands of papers were written about two related subjects, 1) how networks form and occasionally experience cascading failures (associated with Barabasi), and 2) how seemingly stable systems experience sudden, radical change, such as avalanches, earthquakes, and tsunamis (associated with Bak). Like financial panics, these system events, known as phase transitions, occur when individual particles, or agents, lose their ability to behave independently and lock into orchestrated behavior.
Per Bak and Power Laws. All Changes are Small. All Change is big.
Sadly, most macroeconomic models, including the ones used by the Fed and other central banks, presume that emotionless, identical people are quietly making independent decisions and not paying attention to each other. That explains why economists have so little to say about financial panics, situations where the interactions among people dominate their behavior and the entire system locks into hyper-correlation. A great deal of research is going on today to try and isolate statistical markers that show the probability that an impending phase transition or system collapse is about to happen. Early results have isolated half a dozen markets that show promise, including autocorrelated movements, variance spikes, and slowing recovery time. There is also compelling evidence that statistical distributions of returns are not even close to the normal distributions we see in textbooks. Instead, they
appear to be “power law” distributions that have extremely long tails, i.e., positive but low probabilities of extremely large positive and negative changes.
We can paraphrase physicist Per Bak’s summary (in “How Nature Works”) of the scientific literature on complex behavior as saying that in nature, to a first approximation, all changes are small, but all change is big. Catastrophic change (like financial crises) doesn’t happen all that often, but when it does it dominates all of the small changes that have ever happened.
Risk of Catastrophic Change Today
There are reasons to worry about the risk of catastrophic change today. On the positive side, US GDP is growing, unemployment is low, and the Fed appears ready to print money at the first whisper of trouble. But profits are flat, income growth is weak, and the coronavirus could bring growth to a temporary halt. Both bond and stock valuations are stretched by any rational measure. The odds are growing that later this year we will suffer through an extremely toxic and polarized election. And Trump vs. Sanders would not just be a clash of “isms”, it would be a clash of tax rates with enormous implications for the US and global stock prices.
But the epicenter of a global financial event, if one were to happen, would more likely be in China. The problem is not the risk of the “hard landing” everyone always worries about, the trade war, or even the coronavirus shutting down growth; it is the underdeveloped Chinese financial system. Anomalies include a banking system that was designed to lend money to giant state-run companies (SOEs), not the small, private companies (SMEs) that are the engine of almost all output and employment growth. Over the past two years, the government has clamped down on “shadow” lending, which has killed peer-to-peer lending and pushed shadow banks into calling loans instead of making them. Chinese corporate debt is extremely high, and some $2 trillion of it is dollar-denominated, which becomes more burdensome when the RMB falls against the dollar. And a large percentage of the shares of SMEs are pledged as collateral for bank loans. I believe proximate cause of the next global financial crisis, when it happens, will likely be triggered by financial events in China that are transmitted, like a virus, to the global banking system.
Conclusion for Investors
In summary, I believe this is a time when investors should be cautious, not aggressive. That means holding an abnormally large cash position and not letting FOMO—fear of missing out—make them chase rising stock prices. Of course, a large cash position also lets you pick off the shares of world-class companies at a discount when investors freak out over headlines, as I mentioned above.
For private companies and private equity investors, the risks are a little different. The objective there is to make sure that temporary air pockets don’t kill the company, trigger debt covenants and shut down the company’s access to working capital, or make the company unable to refinance short-term debt during a temporary credit squeeze. That means being a little less aggressive in bidding for deals, more cautious on exit multiple assumptions when underwriting deals, taking less leverage than you could get from banks, and longer maturities when you do borrow.
For real estate investors, the same advice applies with one additional wrinkle. Once upon a time (when inflation was 10-15% per year) it was standard practice to index rental rates by tying them to the CPI or some other measure of inflation. Inflation has been 2% for so long that almost every real estate deal I see specifies that rents will rise by 2% per year, or 10% every 5 years, over the entire term of the lease. Given the likelihood that the Fed and other central banks will continue to respond to financial crisis by buying bonds and printing money, I would much rather have the protection of indexed rents than the promise of a 2% increase.
The views and opinions expressed in this article are those of Dr. John Rutledge. Assumptions made in the analysis are not reflective of the position of any entity other than Dr. Rutledge’s. The information contained in this document does not constitute a solicitation, offer or recommendation to purchase or sell any particular security or investment product, or to engage in any particular strategy or in any transaction. You should not rely on any information contained herein in making a decision with respect to an investment. You should not construe the contents of this document as legal, business or tax advice and should consult with your own attorney, business advisor and tax advisor as to the legal, business, tax and related matters related hereto.
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