Moontower #144

[I’m touching down in Maui this morning with my family for Spring Break. I won’t open the laptop this week so Moontower will be off next Sunday.]


It’s been a crazy “Q1” as the suits say (seasonal references that favor fiscal orbits over solar ones still can’t escape the “time is a flat circle” vibe. A semantic loss all around, well-played all of us). In keeping with my Spring Break, I’ll re-post links to the more popular articles I wrote in the past few months in case any new or old readers want to catch up. There won’t be new content in the next 2 weeks.

Drawing Better Outcomes From Fat-Tailed Distributions

✍️There’s Gold In Them Thar Tails: Part 1 (13 min read)

✍️There’s Gold In Them Thar Tails: Part 2 (24 min read)

A meta-comment about the process of writing these. The thinking behind the posts was heavily inspired by Rohit Krishnan’s Spot The Outlier. When I first read his article, I knew it was deeply insightful but I struggled to fully grok it. I saved it in my task dashboard so I would re-visit it occasionally. By keeping it top of mind, I was more primed to “see” it in the wild. There was a back and forth between exposing myself to the post, following his references, and trying to reason about it in the context of what I already knew. This brings me to an encouraging point (I think). Understanding an idea you don’t get fully get is often just a matter of repetition broken up by rests and just enough space in your RAM to give your attention filter a chance to see it around you. It’s a mix of focused and diffuse thinking.

I imagine some readers are thinking “Kris, that post was not hard to understand…you’re supposed to be an options trader?!” I found it hard, what can I say. The journey to comprehend it (at least enough to write a few thousand words on it) is more encouraging than the distress of being dense in the first place. Which is a roundabout way of saying to understand something just keep trying from different angles. Give yourself rest. And trust in repeated exposure. I hope that advice helps next time you try to bang a concept into your skull. Fluid intelligence peaks in your 20s so knowing how to learn requires believing that you can. I’m 100% sure you can.

If you enjoyed this ensemble of concepts (finding outliers, Berkson’s Paradox, correlation breakdown in the extremes), I encourage you to read another treatment that adds to and reinforces the conversation:

✍️ Searching for outliers (22 min read)
by @benskuhn

The post is about better decision-making in fat-tailed distributions. Since they exist in many real-world matters, you should care. The end of the post has good recommendations while the beginning helps you differentiate between thin and fat-tailed distributions.

Some highlights:

  • As the dating example shows, most people have some intuition for this already, but even so, it’s easy to underrate this and not meet enough people. That’s because the difference between, say, a 90th and 99th-percentile relationship is relatively easy to observe: it only requires considering 100 candidates, many of whom you can immediately rule out. What’s harder to observe is the difference between the 99th and 99.9th, or 99.9th and 99.99th percentile, but these are likely to be equally large. Given the stakes involved, it’s probably a bad idea to stop at the 99th percentile of compatibility. This means that sampling from a heavy-tailed distribution can be extremely demotivating, because it requires doing the same thing, and watching it fail, over and over again: going on lots of bad dates, getting pitched by lots of low-quality startups, etc. An important thing to remember in this case is to trust the process and not take individual failures, or even large numbers of failures, as strong evidence that your overall process is bad.
  • Often, you’ll have a choice between spending time on optimizing one sample or drawing a second sample—for instance, editing a blog post you’ve already written vs. writing a second post, or polishing a message on a dating app vs. messaging a second person. Some amount of optimization is worth it, but in my experience, most people are way over-indexed on optimization and under-indexed on drawing more samples.
  • This is similar to how venture capitalists are often willing to invest in the best companies at absurd-seeming valuations. The logic goes that if the company is a “winner,” the most important thing is to have invested at all and the valuation won’t really matter. So it’s not worth it to the VC to try very hard to optimize the valuation at which they invest.

Finally, I can offer an example sitting right under everyone’s nose: choosing which books to read. In How to Read: Lots of Inputs and a Strong Filter, Morgan Housel writes:

The conflict between these two – most books don’t need to be read to the end, but some books can change your life – means you need two things to get a lot out of reading: Lots of inputs and a strong filter…A good reading filter is more art than science. You’ll have to find one that works for you. The bigger point is that the highest odds of finding the right piece of information comes from inundating yourself with information but very quickly being able to say, “that ain’t it.”

The Moloch Series

You cannot unsee the god of unhealthy competition.

✍️Don’t Look Up, It’s Moloch (10 min read)

Once you feel sufficiently Moloch-pilled you need the serum:

✍️Putting Moloch To Rest (7 min read)

To reinforce the cure (again, repetition folks) see this quirky and enjoyable post:

✍️ Slack (4 min read)
by Zvi Mowshowitz

Zvi’s writing has an almost poetic cadence and sticky phrasing. His blog is a minimalist rabbit hole. He’s in the Magic: The Gathering Hall of Fame and a former market maker so I’m probably biased towards his kind of geekery.


✍️Lessons From Susquehanna (5 min read)

Todd Simkin’s interview re-hashed a collection of deeply influential ideas regarding learning and communication from my professional career

✍️Being A Pro And Permission To Be Serious (12 min read)

Discipline and earnestness feel quaint in the theater of memes modernity hyper-manufactures. Don’t fall for it.


✍️From CAPM To Hedging (16 min read)

Ideas in this post:

  • Variance is a measure of dispersion for a single distribution. Covariance is a measure of dispersion for a joint distribution.
  • Just as we take the square root of variance to normalize it to something useful (standard deviation, or in a finance context — volatility), we normalize covariance into correlation.
  • Intuition for a positive(negative) correlation: if X is N standard deviations above its mean, Y is r * N standard deviations above(below) its mean.
  • Beta is r * the vol ratio of Y to X. In a finance context, it allows it allows us to convert a correlation from a standard deviation comparison to a simple elasticity. If beta = 1.5, then if X is up 2%, I expect Y to be up 3%
  • Correlation is symmetrical. Beta is not.
  • R2 is the variance explained by the independent variable. Risk remaining is the volatility that remains unexplained. It is equal to sqrt(1-R2).
  • There is a surprising amount of risk remaining even if correlations are strong. At a correlation of .86, there is 50% unexplained variance!
  • Don’t compute robotically. Reason > formulas.

✍️If You Make Money Every Day, You’re Not Maximizing (28 min read)

Part stories and part technical discussion of how to think about reducing risk.

Money Angle

Finance Guilt

I’ve said several times that finance is really just code. Like software, it’s an abstraction skin pulled over physical features. One can feel a bit disembodied if their formulation of the world for 8-12 hours a day are prices. Prices that collapse all of human enterprise, from the dirt under its fingernails to the sunrises and sunsets between now and some expiration date, into some Excel number format.

Just as software intermediates for less, financial innovation lowers the cost of go-betweens. In finance, the things went-between are people paying to offload risk to people looking to get paid for warehousing risk. In software and finance, skimming a tiny bit of rent on those transactions is lucrative.

How good or bad we can feel about the degree of skimming depends on how much surplus is created versus the higher friction model. The value of information liquidity is fairly obvious so Google enjoyed a positive reputation for at least its first decade in business. Meanwhile, finance feels like a constant barrage of “what did Wells Fargo do now?” or words that rhyme with Fonzi. People outside finance can be excused for having a dim, albeit biased, view of the profession since nobody reports on people doing an honest job.

With that in mind, I leave you with Mitchell’s understandable question:

Here’s my quick response:

Agustin’s response:

I’ll wrap with a footnote from a recent post:

The slicing and dicing of risk is finance’s salutary arrow of progress. Real economic growth is human progress in its battle against entropy. By farming, we can specialize. By pooling risk, we can underwrite giant human endeavors with the risk spread out tolerably. People might not sink the bulk of their net worth into a home if it wasn’t insurable. Financial innovation is matching a hedger with the most efficient holder of the risk. It’s matching risk-takers who need capital, with savers who are willing to earn a risk premium. Finance gets a bad rap for being a large part of the economy, and there are many headlines that enflame that view. I, myself, have a dim view of many financial practices. I have likened asset management to the vitamin industry — it sells noise as signal. But the story of finance broadly goes hand in hand with human progress. It might not be “God’s work” as Goldman’s boss once cringe-blurted, but its most extreme detractors as well as the legions of “I wish I was doing something more meaningful with my life” soldiers are discounting the value of its function which is buried in abstraction. Finance is code, so if software is eating the world, financialization is its dinner date.

Last Call

December 1984:

✍️The Day Los Angeles’ Bubble Burst (4 min read)


✍️Is the Housing Market Broken? (4 min read)
by Ben Carlson

See y’all in 2 weeks!


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