February 2022
Good for market transparency, bad for meme stock short sellers
Jaydyn Carr, a 5th grader from San Antonio, struck gold. In 2019 his mom, Nina Carr, bought him 10 shares of GameStop at $6/share. Prudently, amid the recent meme stock mania, he cashed out a $3,200 gain on his $60 investment. How should we think about this outcome?
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Then there are the more dangerous self-reflections among meme stock shareholders as they make sense of their overnight riches. “I’m a natural at stock picking!” they might conclude, or “I knew GME had a bright future!”, or countless other outputs of the Fundamental Attribution Error. Natural next beliefs include that their stock picking decision-making process is replicable in the long run. Non-rigorous decision-making processes have a well-documented negative expected value in the long run.
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The worst possible outcome for a new investor is to get lucky on their first trade.
In statistics, sample size is an important parameter in determining whether sample results are statistically significant. It is too expensive and usually impractical to survey every voter in a population, but mathematics and a reliable design allows us to be quite confident in a conclusion - not certain, but confident - while sampling a much smaller number of voters. Go too small, however, and the inclusion of just one or a few outliers can greatly skew the conclusions implied by the sample statistics.
Statisticians sometimes refer to unusual one-time events as “an n of one”. Extrapolating from one small case can produce wildly unreliable conclusions of which numerate minds should be skeptical.
But gosh, “an n of one” sure is a good way to get you headlines like this: One hedge fund notched $700 million from its winning GameStop trade. They got the timing just about perfect - this time:
One hedge fund got the GameStop trade just about perfectly right last year — buying it under $10 and selling when the meme stock peaked.
The sell signal it used? An Elon Musk tweet.
That’s how 2021′s top-performing hedge fund, Senvest Management, was able to notch $700 million in profit from GameStop and bring its annual return to more than 85%. The trade was the firm’s single best in its 25 years in existence.
The single best trade in the firm’s 25 year existence, that’s “an n of one”. They cannot expect the process behind that trade - using Elon Musk tweets as sell signals(!) - to replicate similar outcomes when repeated in future trades. To his credit, it sounds like Richard Mashaal, Senvest Management’s founder, CEO and co-CIO, knows this well.
Jaydyn Carr, that 5th grader who bought (well, Mom bought him) GameStop at $6 and cashed out at $320, also reflects “an n of one”. I am happy for Jaydyn (and Mom)! But I also hope that the mathematical lessons he learned includes that this one-time outcome is not typical and should not be extrapolated into expected outcomes for future trades with a similar process.
Again, “The worst possible outcome for a new investor is to get lucky on their first trade.”
Not all volatility is created equal
Risk is complex and subjective, so well-meaning mathematicians (and economists and money managers and others) create formulas to do the impossible task of quantifying risk. Volatility is perhaps the most common calculable but nevertheless flawed proxy for authentic risk. In some ways that makes sense - if volatile price movements induce forced liquidations that lock in extreme losses, volatility can indeed be risky.
But also, in some important ways the volatility-equals-risk assumption makes no sense at all. In July 2020, I intentionally chose to write about this concept for the very first issue of Likelyhoods because risk is so important for investors to understand. I offered the following challenge and analysis:
Quiz time: Three charts appear below, each with two securities on a recent one-year scaled price chart. Decide whether the Red security is more risky, less risky, or equally risky as the Blue security.
Chart 1:
Chart 2:
Chart 3:
Importantly, both the red and blue securities start and end at basically the same price in all three charts, so the question lies in how each security gets from start to finish. Virtually everyone thinks the red security is more risky in both Charts 1 and 2, and think that the red and blue securities are essentially equally risky in Chart 3.
The punchline is that, in Chart 3, Red is Accenture Plc (ACN), and Blue is SPDR S&P 500 ETF Trust (SPY). If it’s discomforting to think that a broad-based index ETF is just as risky as a bankruptable individual company, it should be! Volatility does not measure authentic risk.
Don’t just take my word for it either. Here’s Warren Buffett when asked about volatility and risk at the 2007 Berkshire Hathaway annual meeting:
“Volatility does not measure risk. And, the problem is, that the people who have written and taught about volatility do not know how to measure, or taught about risk do not know how to measure risk. And the nice thing about beta, which is a measure of volatility, is that it’s nice, and mathematical, and wrong, in terms of measuring risk. It’s a measure of volatility, but past volatility does not determine the risk of investing.”
When investors seek the volatility of crypto and hyper-growth stocks as sources for potential outperformance, that’s truly risky volatility.
The different levels of losses right now:
— Ben Carlson (@awealthofcs) February 3, 2022
If you own an S&P 500 index fund (minor correction)
If you own the Nasdaq 100 (correction)
If you own small caps (bear market)
If you own Facebook (1987)
If you own crypto (crash)
If you own hyper-growth stocks (Great Depression)
Crypto owners transact in much more authentically risky products than broad-based indexes. The Likelyhood of the latest cryptocurrency going to zero is far higher than the Likelyhood of an S&P 500 Index fund going to zero, even if their standard deviations were identical. That’s why volatility does not equal risk.
Pamphlets on Probabilistic Thinking
Here’s The Onion’s definitely-not-true reporting that the CDC Announces Plan To Send Every US Household Pamphlet On Probabilistic Thinking:
ATLANTA—Stressing that the effort represented the best chance of ensuring American make responsible choices around the pandemic, the Centers for Disease Control and Prevention announced Thursday that it planned to send every U.S. household a pamphlet on probabilistic thinking and decision-making. “What we’re hoping to do is give every American a quick refresher on how to use statistical analysis to assess their priors and make Bayesian inferences, thereby ensuring they overcome their innate psychological biases—simple stuff, but important nonetheless,” said CDC director Rochelle Walensky, estimating that the pamphlets’ lessons on the baseline fallacy alone would save far more lives than mask-wearing, handwashing, and the Covid-19 vaccine combined.
For investors, I absolutely approve of the SEC writing such a pamphlet.
ONE MORE THING…
Good for market transparency, bad for meme stock short sellers. New SEC short sale rules would force investors to submit updates each month
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