Of Interest, December 2020
Happy New Year. It’s already 2021 in much of the world. It’s been a strange year – a global pandemic prompted government restrictions not seen in generations, at least in the US. The way we work and shop has shifted. Social interactions have been affected. Jobless numbers jumped by unprecedented amounts (as did usage of the word “unprecedented.”) The US Government Debt grew by trillions of dollars. Social unrest was rampant. Oh, and the S&P 500 rose 16.3% and the NASDAQ 100 47.6%.
Please enjoy a compilation of things I found of interest recently. As always, if you found something interesting or informative, please send to me.
Small Business Armageddon and a Zombie Apocalypse?
It’s tough out there. 48% of small businesses risk closing for good, including 62% and 61% in two of my favorite categories – travel/hospitality and gyms/fitness. Meanwhile, the percentage of firms that are “zombies” climbs inexorably higher. By John Mauldin Link
Initial Public Offering (IPO) Frenzy?
This article looks at IPOs as a measure of market euphoria. In recent months, IPO activity has reached tech bubble levels.
This is only part of the picture. Many companies are skipping the IPO process and going public through a Special Purpose Acquisition Company (SPAC). This article is a great look at the trend and why it is concerning.
John Hussman uses data to look at forward market returns. His model has been bearish for a long time, but never this much. It predicts a -1.7% negative nominal 12-year return for a 60/30/10 (S&P 500/bonds/cash) portfolio. This is even worse than what the model predicted in 1928, before the stock market crash that ushered in the Great Depression. Jeremy Grantham, of GMO, predicts an annualized real 7-year return to US large cap stocks of -5.2%. That means stocks would lose 31% of their value over the next 7 years after adjusting for inflation. These projections are based on current valuation. The rest of the word is not nearly as richly valued as the US, and GMO predicts modest positive returns for International small and Emerging Markets and strong returns for Emerging Value (the only asset class for which GMO see good returns.)
John Mauldin is one my favorites. In this piece he gives some historical perspective of where this market is. This is a long read, but worthwhile.
Some thoughts on COVID:
If we are to ultimately eradicate COVID 19, we will need to reach herd immunity. Generally, this happens when a significant number of people are immune due to vaccination, developing antibodies by having the disease, or having a natural immune system that is highly resistant to the disease. As far as I know, there is no data on natural immunity. Vaccines are supposed to be 95% effective, according to company claims from the trials. If they prove to be that effective in the real world, we should see a rapid decline in infection once significant numbers of people are vaccinated. The 5% chance of still contracting the virus should not be worrisome. If 100% of the population was inoculated, the rate of new cases should drop by 95% quickly. Once current cases were resolved, there would be 95% fewer carriers, with only 5% of people susceptible. Even people in the 5% susceptible group would be much less likely to be exposed to the virus. Currently, there is not enough data to know how long the vaccine will last. If it lasts one year, I would expect cases to get close to zero. The problem is that a small number of cases can grow rapidly, requiring annual vaccines.
While there is some controversy around the vaccine, I am struck by the controversy around immunity from recovering from the disease. An internet search of COVID reinfection reveals misleading articles on reinfection, calling for vigilance from those who have already recovered. A big theme seems to be around what immunity means, and lack of data. If your chances of reinfection are 0.003%, for instance, and not 0.000%, you are much safer than having received the vaccine (something like 1500x safer) but still not completely safe. I acknowledge that there is much we do not yet know and some early theories on the novel coronavirus have been replaced with others. Caution is a good thing, but so is reasonableness. There have been about 58,000,000 people who have had COVID-19 and recovered. If 1% of those were reinfected, we would have 580,000 cases of reinfection. Instead, there are 26 confirmed cases. Different sources report different numbers of suspected cases, but every number I have seen is less than 10,000. Given the false positive rate of tests, that tests often see cured cases as active and that some people may think they are recovered and then have symptoms intensify again, we should expect quite a few apparent reinfections that are not truly new cases. A great example of media malfeasance is this article, whose headline doesn’t match the facts in the story itself. From the article:
“What we understand is 90[%] to 100% of people who are infected with the coronavirus do develop an antibody response, whether you have mild infection, asymptomatic infection, all the way to severe infection,” she said.
Ongoing research indicates an immune response may last for six months or longer, she said. In a recent Oxford study, researchers found that people who have contracted the coronavirus are “highly unlikely” to contract the disease again for at least six months.
The study, conducted between April and November with 12,180 health-care workers employed at Oxford University Hospitals, found that 89 of 11,052 staff without antibodies developed a new infection with symptoms. None of the 1,246 staff with antibodies developed a symptomatic infection.
“In some people, it may wane after a few months, but we do get a good indication that natural infection immune response is lasting for some months,” Van Kerkhove said. “We’re about a year into this pandemic, and so we still have a lot to learn.”
The percentage of people who develop antibodies is somewhere between 90-100% (the midpoint being comparable to the vaccine). Obviously, if antibodies wane, so would resistance. Why is “at least six months” cited as the time frame for defensive antibodies? Probably because that is how long transpired from the start of the study.
My problem is not with urging caution. Vigilance is important and I am a big fan of handwashing, hand sanitizer, and keeping distance from sick people. My problem is with journalists using complicit experts to push a narrative not supported by and often contradicted by the facts. We should be cautious to protect ourselves from bad diseases and from bad journalism.
This article is the best I’ve seen at explaining the impact of the false positive rate from the PCR. I started thinking about this when Justin Turner tested positive during the last game of the World Series. Most likely, somewhere around 0.5% of tests show a positive result when the test is negative. One might think that getting a positive test means there is a 99.5% chance that you have the disease. Not so. Bayes Theorem looks at the probability of an event if another event occurs. In this case, the probability of getting a false positive before we get the test is 0.5%, and requires 1) the test result to be positive, and 2) the patient to actually be negative. If the test comes back negative, there is a 0% chance of false positive. If 90% of tests are negative, only the 10% positive tests should be considered in determining the probability of a false positive. If we know false positives are 0.5% (we don’t, but the data I’ve seen suggest this is pretty close), the probability of a positive result being false is 5% in this hypothetical example. When the test positivity rate is high, this does not skew the data much, but when the positivity rate gets low, 50-60% of positive results could be false positives. In MLB’s World Series bubble, there were around 100 people (players, coaches, trainers, etc.) being tested daily. On average a false positive test should be expected ever couple days. If the players were staying in the bubble, the probability of infection was very low. Because they were together in close proximity for a large part of each day, any actual cases likely would have resulted in many cases, which never happened. In this case, the single positive test was best explained as a false positive.
On the lighter side:
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 As of mid-day 12/31/2020. This number grows constantly.
 It is probably not possible to nail down precise numbers here. I am just trying to frame the numbers we have. The sample size will keep growing and confidence can improve, but there are already a lot of data, with relatively tiny numbers of suspected reinfection cases, which can be explained by reasons other than actual reinfection.