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The Double-Edged Sword

Take a deep breath. The air filling your lungs is 78% nitrogen and 21% oxygen. A Swedish pharmaceutical chemist by the name of Carl Wilhelm Scheele first discovered both of these elements in the mid 1770s, but he didn’t get any credit for it. However, this never stopped Scheele, because he was obsessed with chemicals. Scheele’s obsession drove him to discover 6 additional elements on the periodic table all without using advanced scientific equipment given his lack of money.

However, his love of chemicals had a dark side. Scheele was known to have a habit of tasting whatever chemical he was working with. Bill Bryson’s A Short History of Nearly Everything tells the unfortunate side effect of Scheele’s peculiar “tastes”:

Scheele’s rashness eventually caught up with him. In 1786, aged just forty-three, he was found dead at his workbench surrounded by an array of toxic chemicals, any of which could have accounted for the stunned and terminal look on his face.

Scheele’s insatiable drive to understand the mysteries of chemistry led to his ultimate demise. His strength became his greatest weakness. It was his double-edged sword.

This idea is most relevant for “investors” (I actually mean speculators) who take extremely risky bets and keep winning. Their initial success boosts their confidence and they continue their risky investment behavior. While some of them will wise up and diversify after getting rich, many won’t and will pay the price as a result. The very thing that made them rich (i.e. taking big risks) leads to their downfall.

History is riddled with examples of this phenomenon. Consider the famous speculator Jacob Little, who made and lost his fortunes a record nine times, dying without a cent to his name in 1865. Or the story of Jesse Livermore, who got rich and then saw his wealth disappear on at least four occasions while trading on Wall Street in the first few decades of the 1900s. The point is that speculation is an inherently risky game that usually ruins most people financially…if they don’t stop playing.

To imagine this visually, I have run 20 simulations of a strategy with an 80% chance of gaining ~20% and a 20% chance of losing ~90% in each period. This strategy has an expected return of -2%, so it should lose money in the long run. However, it can perform well in the short run by chance alone (i.e. some of the simulations haven’t lost any money even after 10 periods):

As you can see, most of the simulations experience a 90% loss within the first 10 periods. In expectation, a 90% loss should occur once every five periods, however, three of the simulations didn’t experience a loss within 10 periods! This is akin to a gambler going on a hot streak. However, the streak doesn’t last forever. If we extend out the number of periods, the double-edged sword will present itself and slice away the gains:

The three “surviving” lines from the first chart finally realize losses, but only after the 10th period. This is because, on average, a strategy will trend toward its mean return over time. Or as I like to think about it: luck can only disguise skill for so long.

From this idea, there are a few key takeaways for you as an investor:


Is Crypto the New Double-Edged Sword?

I see many parallels today between historical speculators and the overnight millionaires in the cryptocurrency market. I am not saying that those people who got rich in crypto are guaranteed to lose their wealth in crypto, but that there will be some individuals whose crypto success makes them keep pushing their luck elsewhere. The crypto market reminds me of a lottery where there are a few big winners (i.e. 40% of all Bitcoin is owned by less than 1,000 people). Yes, some of these individuals will stay rich, but some of them will go broke because of their double-edged sword.

Either way, this is one of the most exciting times to be following the investing world and I am excited to see how it will all play out. Until next week, thank you for reading!

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This is post 50. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data


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