When I was still in academia, I worked in an epidemiological modeling lab. I worked on pure measure theory and combinatorics and hadn’t yet exposed myself to the capital markets yet.

When I was talking to my PI recently and discussing Coronavirus with them, we discussed how it is categorized rarely. It is known as a fomite-mediated pathogen. A fomite is an inanimate object and a potential transmission agent for some pathogens. My AirPods case on my desk is a fomite, for instance.  I might cough on it, leave, someone else comes in the room and touches it. The case has potential for transmission of my pathogen to the other person.

This markovian approach of looking at our environment suggests fomite mediated behavior is basically irrelevant when it comes to the potential for pandemic. There are some exceptions like noravirus and other interior pathogens, which can live on fomites for an extended period of time. That‘s why transmission rates are higher and significantly harder to contain.

But, in a case of the flu, if I cough on an inanimate object––and it’s not a water body––the pathogen will typically die within minutes. Whereas if it’s noravirus on puke or feces, and it comes into contact with the fomite, it could live on that fomite for weeks. 

Coronavirus is in the latter camp, living nine to 10 days on a fomite without intermediary intervention or without liquid suspension, which is highly unusual. The Chinese have destroyed currency over this property of the pathogen. 

Back when I was in academia, we used a model known as the SIWR model, which was actually the focus of my research. I don’t know if this model is still in use. It’s a set of partial differential equations that people use to model disease expansion and outbreak potential. The model was adding fomite-mediated behavior to it. Because the SIWR ignores fomites, because for most pathogens they’re not relevant. 

And that’s what people are starting to use now to look at coronavirus. Funds using certain models to predict disasters and how those disasters could potentially disrupt cash flows along supply chains could use this to develop macrostrategies. If you developed a strategy that revolved around modeling the impact of the fomite-mediated pathogen,  such as coronavirus on markets in some way, that would probably be a tenable strategy.

One of the reasons why people are really worried about coronavirus. It’s not necessarily that fatalities are high or that it’s killed. The flu today (or dengue or malaria) is significantly more dangerous, if you’re just looking at total fatality rates. But, the fact that it is capable of living on an inanimate object for so long is why a lot of people, including the WHO and the CDC, are worried about it crossing borders, because it is very hard to contain a format mediated pathogen unless you take extreme measures.

Many people in China, and the entire country, in certain sections, are basically under lockdown. You can’t leave the house, you can’t go out and get groceries, you can’t go to work. You can sometimes work remotely, if the possibility exists, but in many cases, it doesn’t.

In certain sectors, companies are failing. Their employees can’t work, which means they can’t sell their product, they can’t provide their services. The Chinese government, meanwhile, is struggling to find some sort of mitigating policy arrangements that could allow the banks to bankroll these organizations.

And these aren’t middle market cap companies. These are large companies beginning to go under due to a lack of employee support. It’s really, really bad there. The Chinese government is good at keeping things stable, because they have autocratic control over everything. They don’t have to worry about things like political gridlock and such, but Coronavirus is hitting them really hard. 

I don’t know how it will impact things in the long term. But, as a market maker, you want to engineer a strategy where that’s irrelevant. You don’t want to create a strategy that yields macro-exposure to fluctuations. And, in fact, if the spreads are higher than your alpha, you will increase in your utility maximization will be higher, so that, theoretically should help you. For the average Joe, it’s not good. 

Market Play

What’s the doomsday action plan? Find safe harbor in an asset that co-integrates inversely with fiat. When people start to lose trust in central banks, they gravitate towards whatever asset that may be. That asset would serve as my hedge against fiat calamity. 

A far safer bet, which happens all the time, and which is what I recently did, whenever there is a global pandemic deals, PE deals and specifically mergers and acquisitions of these mid market cap to small biotech incubators jump through the roof. You have companies like Pfizer and Merck, who might not necessarily have the resources or the brain pool even to synthesize a vaccine for Coronavirus.  

But,  a small incubator that’s affiliated with Harvard or somewhere in Boston––or you could have something in Texas or California or New York or anywhere––producing  the research required to synthesize a cure. And, as soon as they do, they get acquired at a premium. If you have investments in their equity, then that’s extraordinarily profitable. You’ll get 10 x 20 x return sometimes––that’s unmatched anywhere else in terms of profit. 

If coronavirus gets worse than it already is now, private equity in these types of incubators might be a safe bet, because, at the end of the day, these are the guys who actually produce the research. It is the model that tech has started to adopt these days, Big Tech in that they outsource their research and development to small shops.

And then big pharma acquires these small, PhD-driven shops when they produce something that works. GSK, Merck, Pfizer, Allergan sitting around waiting and seeing which companies they can eat for the research that they can then industrialize and sell on a global scale.

Typically, if the market is more complex, so in areas where volume is really high, in general, the dynamics are not something that can be correlated usually, whereas in places like Vietnam or Thailand or Indonesia or post-modernizing industrial economies, on their way to becoming a powerhouse, are typically much more easier to model, but, in saturated economies with very high volume, we tend to find that the regime-switching mechanism of the market microstructure tends to work best. 

There is no one defining reason why this is so, but one of the reasons is that, with so many dynamics at play, you can’t use a single model to look at anything. Even if you’re looking at a single asset; say you have equity A that’s trading as a non-CBOE or an option, say, a call and equity a trading on CBOE. And, it’s the dynamics could change at any given time, and an existing model that you have, that you’ve back tested 10 million times, might not work in the next trades. There is value in evaluating things on an incremental basis. Today we have the computational power to do so. 

In the 90s or the 2000s, that wasn’t feasible. The volume was so high that we would have to use continuous time approximations to do any sort of estimation. But, now computers are so powerful that you can actually do discrete time analysis of everything that comes in and out of the order book. That wasn’t a possibility before computers got so strong.

In more straightforward markets, that’s a more tenable strategy, but I certainly wouldn’t do that in places like the states or Shanghai, Shenzhen, London, Singapore. Overarching, macro level analysis on complex economies can be dangerous to do. 

This text has been adapted from comments on our weekly Genius Wednesdays. Be sure to join the CoinGenius Discord channel today for a constant stream of market analysis and economic insights.

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