November 2022
Bending the ARKK
Michael Burry’s hindsight
Elon’s Twitter
No, I do not want to talk about Twitter.
Okay, fine, I will talk about Twitter.
What can investors learn from Elon’s Twitter acquisition?
Investors can get away with a poor process… for a while... but that luck will eventually run out. Knowing only what has been made public, it is difficult to make the case that Elon’s due diligence with Twitter, and the due diligence of private investors in the new Twitter, can be described as a good process. Elon Musk’s Texts Shatter the Myth of the Tech Genius. Anything can happen in the short run, but a bad process will catch up to you in the long run.
Overconfidence is a killer. It is extremely common to see what others are doing and think “I could do better!”. It is even more extremely common to think this when you are the richest person on the planet with multiple other highly successful businesses. Overestimating one’s own abilities, particularly when one is not privy to privileged inside information, is a recipe for humble pie.
Paper towels and Stocks
When paper towels go on sale, our hearts flutter a bit then we buy more paper towels. Should investors do the same with stocks?
Well, 65% of Americans are doing 'the exact opposite of what they're supposed to,':
“When the market is doing well, people are throwing their money at it. When it’s doing poorly, they’re keeping their money out,” [LaVigne] says. “It’s doing the exact opposite of what you’re supposed to be doing.”
…
Here’s the problem: In order to earn long-term gains, you need to be invested on the market’s best days. And those often come right after the worst ones.
Over the 20-year period ending December 31, 2021, the S&P 500 returned an annualized 9.52%. Remove the 10 best days from that period, and the return drops to 5.33%, according to analysis from J.P. Morgan. Over that period, seven of the market’s best days occurred two weeks after one of the 10 worst days.
“We have no idea where the bottom of this downswing is, but we know almost for sure that if you’re keeping money out of the market you’re going to miss the uptick,” says LaVigne. “The worst thing you can possibly do is not be in the market when it starts to turn around.”
I also wrote about this market dynamic when this statistic made its rounds in early 2021.
Now inflation is leading some millennials and Gen Zers to close their investing accounts. The first basic message is that if investors cannot handle inflation-induced volatility in their portfolios, they should not be investing those monies. The second basic message is that what appears to be short-term pains will evolve into long-term gains - if you trust the process.
Here’s a nice animated graph from J.P Morgan Asset Management correlating Consumer Sentiment Index readings to subsequent 12-month S&P 500 returns. Not surprisingly, the best opportunities exist when sentiment is lowest.
Lesser Known Statistics: Bayesian Statistics
Continuing my recent riff on statistics, I recently wrote a segment aptly named Specious Statistics that highlighted a senate hearing in which Ted Cruz and other climate change deniers cherry-picked a short and carefully-selected interval of time to give the impression that global temperatures are not actually rising, check it out if interested. Inferential Statistics pulls back the curtain on misinformation like this to ask questions like “How Likely is the null hypothesis of “no global warming” given the long term trend of increasing temperatures?” and “Can random variation explain the fluctuations we see in global temperatures?”
As we delve into probability theory, two of the most common interpretations are the Frequentist and Bayesian interpretations.
Frequentists think of probabilities as tracking the frequencies of outcomes in a well-defined experiment that is repeated an infinite number of times. To find the probability that a coin will land on tails, we continually flip the coin, divide the number of tails by the total number of flips, and evaluate the limit as the number of flips approaches infinity - which for a fair coin is 50% of course. The hypothesis testing for coin flipping in the previous section is a Frequentist approach.
Bayesians, on the other hand, incorporate a “degree of personal belief” into an event - that belief is based on a “prior” probability and then is updated based on newly obtained information into a “posterior” probability. If “personal belief” sounds a bit squishy, that’s because it is - but that squishiness also helps extend probability theory beyond the limitations of Frequentists’ interpretations and into more complex real life situations. If a particular situation is novel and lacks history from which we can analyze data, Frequentists have very little to work with, but Bayesians can get to work.
Consider the following well-known context for introducing Bayes’ Theorem:
You test positive for a rare, very serious disease that afflicts 0.1% of the population. That test correctly identifies 99% of people who have the disease, and only 1% of people who test positive do not have the disease. What are the chances that you actually have this disease?
Most people, including many doctors, would answer 99%. However, the correct probability that you actually have this disease is only 9%. Only 9%(!!!) I pulled this particular problem and values from Veritasium’s phenomenal How To Update Your Beliefs Systematically - Bayes’ Theorem.
At a high level, what matters is the proportion of true positives to false positives. With rare diseases in particular, the large difference in populations of those with the disease and those without the disease have a significant impact on the true positive and false positive cases. Importantly, in this problem we have learned that we tested positive for the disease, so we now need to rigorously update the probability that we actually have the disease. Certainly a positive test result will increase this probability, but by how much? That’s where Bayesian analysis comes to the rescue.
The key phrase of course is “given that” - this means we know or assume that one event happened and want to update the probability of the other event then happening. What is the Likelyhood that you have the disease given that you tested positive? What is the Likelyhood of a 20 percent drop in the S&P 500 Index given that the Index has already dropped 10 percent? What is the Likelyhood that a company files for bankruptcy given that the CEO has been charged with embezzlement?
In The Signal and the Noise, Nate Silver dedicates multiple chapters to Bayesian analysis as applied to investing, climate change, and other predictive contexts. Here is my humble attempt at summarizing Silver’s much more complete discussion:
Bayesian analysis is about systematically updating one’s probabilities when presented with new information. When someone comes along with a different probability for an event than yours, you are compelled to either come to a new agreement with that person on a more accurate probability, or else you must place an arbitraging bet with that person to profit from your correct (and their incorrect) probability assessment. Markets serve this precise purpose for investors - in this way, Bayesian analysis is cut from the same cloth as Adam Smith’s “Invisible Hand” and the efficient markets hypothesis. A reasonable consensus view is that markets are mostly efficient but occasionally are inefficient, and it is these inefficiencies that create opportunities for investors to “beat the market”. Fischer Black (of the well-known Black-Scholes model) estimates that markets are efficient and rational 90% of the time, during which it is not possible to consistently outperform the market on a risk-adjusted basis. For this reason, active management during these times is merely a zero-sum game among market participants from which the frictions of transaction costs, psychological biases, and other effects create systematic underperformance. Identifying when markets are in the other 10% of inefficiency is perhaps possible but highly elusive, and is often identifiable only in hindsight. The prescription in making better predictions amid uncertainty is to think probabilistically - what I call Thinking in Likelyhoods: 1) keep a large number of hypotheses in mind, 2) think about those hypotheses probabilistically, and 3) update those probabilities rigorously as new information comes along.
For further learning on Bayes’ Theorem, check out the following resources, in order of mathematical rigor:
Veritasium: How To Update Your Beliefs Systematically - Bayes’ Theorem
3Blue1Brown: The medical test paradox, and redesigning Bayes' rule
3Blue1Brown: Bayes theorem, the geometry of changing beliefs
I will close with this quote:
“A healthy respect for uncertainty and focus on probability drives you never to be satisfied with your conclusions. It keeps you moving forward to seek out more information, to question conventional thinking and to continually refine your judgments and understanding that difference between certainty and likelihood can make all the difference.”
-- Robert Rubin
ONE MORE THING…
Bending the ARKK. On its 8th birthday, lifetime gains at Cathie Wood’s ARKK have fallen behind the S&P 500
Michael Burry’s hindsight. 'Big Short' investor Michael Burry points to the steep drop in ARKK stock - and says Cathie Wood's flagship fund was always bound to collapse in value
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