Stock markets and coronavirus – an endogenous perspective

Markets collapse when they’re in a vulnerable state. Coronavirus might be the straw that broke the camel’s back – this time – but there’s no clear pandemic to stock price causality.

The predominant explanation for this week’s steep decline in the stock market is coronavirus. I take this as evidence that the pandemic of open-loop, event-based thinking is as strong as ever.

First, let’s look at some data. Here’s interest in coronavirus:

It was already pretty high at the end of January. Why didn’t the market collapse then? (In fact, it rose a little over February). Is there a magic threshold of disease, beyond which markets collapse?

How about other pandemics? Interest in pandemics was apparently higher in 2009, with the H1N1 outbreak:

Did the market collapse then? No. In fact, that was the start of the long climb out of the 2007 financial crisis. The same was true for SARS, in spring 2003, in the aftermath of the dotcom bust.

There are also lots of examples of market crashes, like 1987, that aren’t associated with pandemic fears at all. Corrections of this magnitude are actually fairly common (especially if you consider the percentage drop, not the inflated absolute drop):

Wilshire Associates, Wilshire 5000 Full Cap Price Index [WILL5000PRFC], retrieved from FRED, Federal Reserve Bank of St. Louis;, February 28, 2020.
So clearly a pandemic is neither necessary nor sufficient for a market correction to occur.

I submit that the current attribution of the decline to coronavirus is primarily superstitious, and that the market is doing what it always does.

It’s hard to do it justice briefly, but the stock market is basically an overlay of a complicated allocation mechanism on top of the real economy. In the real economy (green positive loop) capital and technology accumulation increase output (thinking strictly of on-market effects). Growth in that loop proceeds steadily in the long run, but with bumps from business cycles. The stock market is sending signals to the real economy about how to invest, but that’s complicated, hence the dashed line.

In the stock market, prices reflect expectations about the future flow of dividends from the economy (blue negative loop). If that were the whole story, coronavirus (orange) would have to have induced fears of a drop in the NPV of future profits of about 10%. Hopefully that’s way outside any plausible scenario. So why the big drop? It’s due to the other half of the story. Expectations are formed partly on fundamentals, and partly on the expectation that the market will go up, because it’s been going up (red positive loop).

There are actually a number of mechanisms behind this: naive extrapolation, sophisticated exploitation of the greater fool, redirection of media attention to prognosticators of growth when they’re right, and so on. The specifics aren’t important; what matters is that they create a destabilizing reinforcing feedback loop that inflates bubbles. Then, when some shock sufficient to overcome the expectations of appreciation arrives, the red loop runs the other way, as a vicious cycle. Diminished expected returns spark selling, lowering prices, and further diminishing expectations of appreciation. If things get bad enough, liquidity problems and feedback to the real economy accentuate the problem, as in 1929.

Importantly, this structure makes the response of the market to bad news nonlinear and state-dependent. When SARS and H1N1 arrived, the market was already bottomed out. At such a point, the red loop is weak, because there’s not much speculative activity or enthusiasm. The fact that this pandemic is having a large impact, even while it’s still hypothetical, suggests that market expectations were already in a fragile state. If SARS-Cov-2 hadn’t come along to pop the bubble, some other sharp object would have done it soon: a bank bust, a crop failure, or maybe just a bad hot dog for an influential trader.

Coronavirus may indeed be the proximate cause of this week’s decline, in the same sense as the straw that broke the camel’s back. However, the magnitude of the decline is indicative of the fragility of the market state when the shock came along, and not necessarily of the magnitude of the shock itself. The root cause of the decline is that the structure of markets is prone to abrupt losses.

For a nice exploration of these dynamics, from the complexity/nonlinear dynamics thread of systems science, see Didier Sornette’s Why Stock Markets Crash: Critical Events in Complex Financial Systems.

Coronavirus Roundup

I’ve been looking at early model-based projections for the coronavirus outbreak (SARS-CoV-2, COVID-19). The following post collects some things I’ve found informative. I’m eager to hear of new links in the comments.

This article has a nice summary, and some

Disease modelers gaze into their computers to see the future of Covid-19, and it isn’t good

The original SIR epidemic model, by Kermack and McKendrick. Very interesting to see how they thought about it in the pre-computer era, and how durable their analysis has been:

A data dashboard at Johns Hopkins:

A Lancet article that may give some hope for lower mortality:

The CDC’s flu forecasting activity:

Some literature, mostly “gray” preprints from MedRxiv, all open access:

A podcast with some background on transmission from Richard Larson, MIT (intestine alert – not for the squeamish!):

This blog post by Josh at Cassandra Capital collects quite a bit more interesting literature, and fits a simple SIR model to the data. I can’t vouch for the analysis because I haven’t looked into it in detail, but the links are definitely useful. One thing I note is that his fatality rate (12%) is much higher than in other sources I’ve seen (.5-3%) so hopefully things are less dire than shown here.

I had high hopes that social media might provide early links to breaking literature, but unfortunately the signal is swamped by rumors and conspiracy theories. The problem is made more difficult by naming – coronavirus, COVID19, SARS-CoV-2, etc. If you don’t include “mathematical model” or similar terms in your search, it’s really hopeless.

If your interested in exploring this yourself, the samples in the standard Ventity distribution include a family of infection models. I plan to update some of these and report back.

S-shaped Functions

A question about sigmoid functions prompted me to collect a lot of small models that I’ve used over the years.

A sigmoid function is just a function with a characteristic S shape. (OK, you have to use your imagination a bit to get the S.) These tend to arise in two different ways:

  • As a nonlinear response, where increasing the input initially has little effect, then considerable effect, then saturates with little effect. Neurons, and transfer functions in neural networks, behave this way. Advertising is also thought to work like this: too little, and people don’t notice. Too much, and they become immune. Somewhere in the middle, they’re responsive.
  • Dynamically, as the behavior over time of a system with shifting dominance from growth to saturation. Examples include populations approaching carrying capacity and the Bass diffusion model.

Correspondingly, there are (at least) two modeling situations that commonly require the use of some kind of sigmoid function:

  • You want to represent the kind of saturating nonlinear effect described above, with some parameters to control the minimum and maximum values, the slope around the central point, and maybe symmetry features.
  • You want to create a simple scenario generator for some driver of your model that has logistic behavior, but you don’t want to bother with an explicit dynamic structure.

The examples in this model address both needs. They include:

I’m sure there are still a lot of alternatives I omitted. Cubic splines and Bezier curves come to mind. I’d be interested to hear of any others of interest, or just alternative parameterizations of things already here.

The model:

Vensim: sigmoids 1.mdl (works in PLE, Pro, DSS)

Ventity: Sigmoids