Dr. John Rutledge: Far from Equilibrium Economics, Finance and Investing

Summary: My last post discussed the conversation I had with Kelly Evans, anchor of CNBC’s The Exchange, on the impact of the coronavirus on global markets. In this post, I lay out the basics of the mathematical contagion models that epidemiologists use to understand how a pathogen spreads across a vulnerable population. I conclude with investment strategies that make sense in a world where financial crises happen more often than many like to admit.

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; you can see a few of them are below. I will write more about them in the next post.

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, 1665-1656, but famous because, while holed up in his country 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.

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.

JR

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. For a more technical version, you can read about the contagion models that epidemiologists use in my next post.

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:

• On the negative side, local officials clearly covered up information about the outbreak during the crucial first days, allowing it to infect many more people.
• On the (medically) positive side, the authoritarian nature of the Chinese government allowed Xi Jinping to take more aggressive actions to contain the spread of the disease than would be possible in many places. At one point more than 150 millions people were under geographical lockdown. These actions, while draconian, likely bought those of us outside China valuable time to prepare for our turn in the barrel.
• Note: Now that the flow of new cases in China has leveled off and been overtaken by new cases elsewhere, there is a pointless blame game raging in the media being played by both Chinese and American authorities. I find it a waste of time and will not be commenting on it. We have more serious issues to handle today.

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.

Remember the Fundamentals

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), companies affected by supply chain blockages (AirPod parts for Apple, components for Honda and Nissan), and companies in other countries where the coronavirus will spread. 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.

Stocks are Long-Duration Assets

When you buy a stock, the price you pay 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. Technically, it is the fulcrum–the point on the teeter-totter where is just balances–of the stream of future cash flows, translated into present values. For example, the price of an asset with a duration equal to ten (say, a ten year zero-coupon Treasury bond) will fall by ten percent if interest rates rise by one percentage point. The duration of a typical bond fund today 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. For a decent estimate of how much you can use the dividend yield. If a company pays a $2 annual dividend and its stock is selling for $100 per share (2% dividend yield) you are only going to get back $2 of your $100 in the first year, so the other $98 much be the value of everything that happens after year 1.

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 at a discount when other people 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. The same goes for Apple, Microsoft and Amazon. All were hit 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. This is a time to be cautious, not aggressive.

JR