back-of-the-envelope hedging cost

a little exercise in proportion

Here’s a way to think about options that has nothing to do with vol.

Consider a strategy that:

  1. Buys a 1-year 15% OTM put on SPX for 3% on Jan 1.

  2. Puts the remaining capital in SPX

  3. Every Jan 1, rebalances back into that position. So if your put expires in-the-money, you reinvest the proceeds back into SPX, if it doesn’t you sell enough SPX to buy the next year’s protection.

Ask Claude to backtest it for, say, 25 years. It only needs the SPX prices on every Jan 1 because we are not using market-based pricing for the put. We’re just saying it costs 3%

Then compare the performance to just owning SPY unhedged.

🛟Modern survival skill

The point of doing this isn’t to mine for a strategy, but to develop some numeracy around market returns and prices. This is a recurring theme for me, but to get better at making snap judgments, you train your brain on strands of data and comparisons. You’re creating context to recognize when something sounds unreasonable. This will be a survival skill in slop-world where we are doing less work at the bit-level but find ourselves buried in verification.

The exercise provides a confidence interval for: what premium would you be happy to pay for the put so that you could skip diversifying and just hold 100% SPY plus the insurance policy?

If you run this exercise, you’ll find for anyone with a typical risk preference*:

At 1% it’s a no-brainer.

At 5% it’s meh.

*It’s a bit chaotic to throw the phrase “risk preference” into that sentence so freely, but you can, in the spirit of upgrading your context, learn a lot from Elm Wealth’s whitepaper How We Invest

I posted that thought exercise on X and received a reply I really liked:

The Option Premium
@theoptpremium
@KrisAbdelmessih You don’t even need the backtest to see where it lands. 3% a year for 25 years is 75% of your starting capital spent on insurance. In that stretch, a 15% out-of-the-money put paid off meaningfully in 2008, a little in 2002 and 2022, and nothing in the other 22 years. Add it up…

It’s a great example of the type of on-the-fly reasoning one develops when they don’t have the luxury of time.

I’ll rephrase the tweet’s approach in my own way, showing my logic at every step.

1. Which years had a point-to-point return of -18% (the put’s breakeven) or more?

  • 2001 or 2002. One of those years, I can’t remember, but if the put paid, it was very small.

  • 2008. I’d guess the market was down ~40%, so the put paid (40-18)/3 or about 7x

  • 2020. Oh wait, the market didn’t actually finish lower iirc. The put didn’t pay out.

  • 2022, it might have paid a small amount

2. Estimating net losses on the put

Let’s be conservative and say the put broke even in the wake of the dot-com era and 2022. So the put loses its full value in 22 years, pushes in 2 years, and makes 7x one-time.

Net net, you lost 15 out of 25 bets or 60% of your premium. If the put costs 3%, the hedge pulls down your arithmetic return by 1.8% per year.

3. What does this mean for the strategy?

Back of the envelope, I’d guess your annual arithmetic return on a naked SPX position for the past generation has been close to 10% and lop off about 1.3% for vol drag* to get a CAGR ~8.7%

*Vol drag

=½ * variance

=½ * volatility2

=½ * .16 2

=1.28%

This put cost you another 1.8% per year, leaving you with about 8.2% arithmetic but a touch less vol drag because it lowered your volatility. It’s a bit crunchy to import the savings from a 2008 tail event into volatility, which is more descriptive of a distribution’s meat, but a 40% move in variance space is nearly 10x bigger than a typical 15% move, so if you think of 2008 as 10 years, then the GFC represents about ⅓ of the sample in variance time.

Notice, the put doesn’t erase 2008, but it makes the loss much smaller. Without the put, 2008 was down 40%, but with the put, your loss caps at -18%.

Now measure each version as a miss from an average year:

  • Unhedged, the average year is about +10%, so −40% is a 50-point miss.

  • Hedged, the average year is about +8%, so −18% is a 26-point miss.

Variance cares about miss squared.

50² = 2500.

26² ≈ 676.

676 is about ¼ of 2500.

So the hedged 2008 carries about ¼ of the variance the unhedged 2008 did.

In other words, the put removed ¾ of 2008’s variance!

Since 2008, a single year, carried about ⅓ of the whole sample’s variance, the put removed about ¼ (¾ of ⅓) of the total variance.

I’m going to repeat this for tedious emphasis:

Removing ¾ of the variance for a single giant year out of 25 years removed ¼ of the total variance

¾ of the total variance remains, but since vol is the square root of variance, we need to compute √0.75 ≈ 0.87.

87% of the volatility remains, so vol goes from 16% to about 14%.

Vol drag is half of vol squared: ½ × 0.14² ≈ 1%.

So we get 8.2% arithmetic return with the put minus ~1% of vol drag and we end up with a CAGR of about 7.2%

The put cost 1.8% per year, but because it reduced drag as well, it only lops ~1.5% from CAGR. This represents somewhere between a third and a half of historical equity risk premium. Whether this is worth it or not is in the eye of the beholder, but it doesn’t sound like puts, which we expect to have a premium, are egregiously high.

4. What if the put only cost 1%?

We’ll stay conservative and say you push in 2002 and 2022. But in 2008 you make (40-16)/1 or 24x on the puts. You basically break even on owning the puts and cut off your left tail!

You get better at this the more you practice, but moving in between risk, return, and payoff scenarios like this should not only be fun but inoculate you from the viral disease of fuzzy thinking

no such thing as a stock market bubble?

There’s No Such Thing as a Stock-Market Bubble | 4 min read

Last week, Jeff Yass, founder of Susquehanna, published an op-ed There’s No Such Thing as a Stock-Market Bubble in the WSJ. It’s the same talk he gave our office in the early 2000s.

In 1999, just as today, when share prices fall, it isn’t a delusion correcting. It’s uncertainty resolving.*

My resolution for why the resolution of uncertainty resolves downwards from my post Why Investing Feels Like Astrology:

“The intrinsic value increases as it makes profits, but its extrinsic value declines. The number of forked paths ahead of it evaporates faster than its fundamentals could ever make up for. It’s the same feeling you’d have if you bought a put option on a stock and lost money even if the stock fell. You overpaid for the extrinsic which receded more than the stock’s decline.”

A stock price is the market’s probability-weighted guess under uncertainty. It is perfectly sensible for stock prices to soar in the context of a paradigm shift in technology, and for the most likely outcome to be a negative return or even a zero. The stock’s price is concerned with the expected value, which is probability times outcome. A tiny probability of massive upside will rationally assign a hefty valuation to the stock, but both can be true at once: a sensible expected value and a most likely outcome of zero.

But as Jeff says in the article and I’ve covered repeatedly, the options market will tell you what possible scenarios are driving the valuation. A stock price is a grainy snapshot, but an option surface is a high-definition movie with all of its strikes and expiry dates.

One of the cleaner examples of this comes from commodities pricing. Natural gas prices are vulnerable to winter squeezes. The so-called “widowmaker,” the March-April futures spread, trades as a referendum on winter supply/demand balances. From What The Widowmaker Can Teach Us About Trade Prospecting And Fool’s Gold:

The truth is the gas market is very smart. The options are priced in such a way that the path is highly respected. The OTM calls are jacked, because if we see H gas trade $10, the straddle will go nuclear.

Why? Because it has to balance 2 opposing forces.

  1. It’s not clear how high the price can go in a true squeeze or shortage

  2. The MOST likely scenario is the price collapses back to $3 or $4.

Let me repeat how gnarly this is.

The price has an unbounded upside, but it will most likely end up in the $3-$4 range.

Try to think of a strategy to trade that.

Good luck.

Wanna trade verticals? You will find they all point right back to the $3 to $4 range.

Upside butterflies, which are the spread of call spreads (that’s not a typo…that’s what a fly is…a spread of spreads. Prove it to yourself with a pencil and paper), are zeros.

The market places very little probability density at high prices, but this is very jarring to people who see the jacked call premiums.

That’s not an opportunity. It’s a sucker bet.

I kept that last line in the block quote because it made me laugh. It sounds so AI, but this article was written years before ChatGPT was born, and I think Sam Altman owes me shares of OAI.

Anyway, it was comforting to see Jeff affirm 25 years later what I consider to be evergreen knowledge. So much of the value of options comes from being able to understand the shape of risk, which a 2-D stock price fails to convey.

If interested in learning more:

Moontower #336

In this issue:

  • More AI uses to learn

  • The resolution of uncertainty is…bearish?

  • Pricing put protection without vol

Friends,

2 quick AI uses before we get to Money Angle.

Socratic Tutor | agent skill

Jane Street open-sourced a single SKILL.md that turns Claude or ChatGPT into a strict tutor. It opens with a diagnostic, scores you 0-5 on each subskill, asks one question at a time, while withholding the answer.

If you get it wrong, it asks progressively narrower questions until you find the hole in your own reasoning. Levels only go up after you prove it twice, once from cold recall, and it ambushes you with review of old material.

Regular Moontower readers will recognize traces of the methods Math Academy uses in its product from “generation” effects, to reinforcement and a nod to spaced repetition.

🔗To learn more about the pedagogical foundation of these methods, see The Principles of Learning Fast

I beat Jane Street to publishing when I wrote this summer’s Socrates 2026 which, mother of ironies, starts with me flubbing a Jane Street-style interview question! This is the only thing I’m sure I’ve ever beaten them in, and the satisfaction of that moment is already long gone, and to make it even more pathetic, unprofitable.

More in the spirit of open-sourcing:

Every Jane Street resource on the internet | hft.md

If you hate board game rulebooks…

then we can get along. Complex eurostyle board games are no fun to learn or teach. So I recruited ChatGPT to make a custom explainer video for the rules of Brass:Birmingham with some prompts and a PDF of the rulebook.

It took a few iterations to get its “camera” to move without jerking around (but now I have the proper prompt/skill file going forward) and to sync the narration with motion. The iterating process involves having it produce a segment of the explainer to so I don’t obliterate too many tokens without helping it course-correct with my feedback.

This video is just a 30 second segment of the game’s setup phase that preceded building the full tutorial.

Money Angle

There’s No Such Thing as a Stock-Market Bubble | 4 min read

Last week, Jeff Yass, founder of Susquehanna, published an op-ed There’s No Such Thing as a Stock-Market Bubble in the WSJ. It’s the same talk he gave our office in the early 2000s.

In 1999, just as today, when share prices fall, it isn’t a delusion correcting. It’s uncertainty resolving.*

My resolution for why the resolution of uncertainty resolves downwards from my post Why Investing Feels Like Astrology:

“The intrinsic value increases as it makes profits, but its extrinsic value declines. The number of forked paths ahead of it evaporates faster than its fundamentals could ever make up for. It’s the same feeling you’d have if you bought a put option on a stock and lost money even if the stock fell. You overpaid for the extrinsic which receded more than the stock’s decline.”

A stock price is the market’s probability-weighted guess under uncertainty. It is perfectly sensible for stock prices to soar in the context of a paradigm shift in technology, and for the most likely outcome to be a negative return or even a zero. The stock’s price is concerned with the expected value, which is probability times outcome. A tiny probability of massive upside will rationally assign a hefty valuation to the stock, but both can be true at once: a sensible expected value and a most likely outcome of zero.

But as Jeff says in the article and I’ve covered repeatedly, the options market will tell you what possible scenarios are driving the valuation. A stock price is a grainy snapshot, but an option surface is a high-definition movie with all of its strikes and expiry dates.

One of the cleaner examples of this comes from commodities pricing. Natural gas prices are vulnerable to winter squeezes. The so-called “widowmaker,” the March-April futures spread, trades as a referendum on winter supply/demand balances. From What The Widowmaker Can Teach Us About Trade Prospecting And Fool’s Gold:

The truth is the gas market is very smart. The options are priced in such a way that the path is highly respected. The OTM calls are jacked, because if we see H gas trade $10, the straddle will go nuclear.

Why? Because it has to balance 2 opposing forces.

  1. It’s not clear how high the price can go in a true squeeze or shortage

  2. The MOST likely scenario is the price collapses back to $3 or $4.

Let me repeat how gnarly this is.

The price has an unbounded upside, but it will most likely end up in the $3-$4 range.

Try to think of a strategy to trade that.

Good luck.

Wanna trade verticals? You will find they all point right back to the $3 to $4 range.

Upside butterflies, which are the spread of call spreads (that’s not a typo…that’s what a fly is…a spread of spreads. Prove it to yourself with a pencil and paper), are zeros.

The market places very little probability density at high prices, but this is very jarring to people who see the jacked call premiums.

That’s not an opportunity. It’s a sucker bet.

I kept that last line in the block quote because it made me laugh. It sounds so AI, but this article was written years before ChatGPT was born, and I think Sam Altman owes me shares of OAI.

Anyway, it was comforting to see Jeff affirm 25 years later what I consider to be evergreen knowledge. So much of the value of options comes from being able to understand the shape of risk, which a 2-D stock price fails to convey.

If interested in learning more:

Money Angle For Masochists

a little exercise in proportion

Here’s a way to think about options that has nothing to do with vol.

Consider a strategy that:

  1. Buys a 1-year 15% OTM put on SPX for 3% on Jan 1.

  2. Puts the remaining capital in SPX

  3. Every Jan 1, rebalances back into that position. So if your put expires in-the-money, you reinvest the proceeds back into SPX, if it doesn’t you sell enough SPX to buy the next year’s protection.

Ask Claude to backtest it for, say, 25 years. It only needs the SPX prices on every Jan 1 because we are not using market-based pricing for the put. We’re just saying it costs 3%

Then compare the performance to just owning SPY unhedged.

🛟Modern survival skill

The point of doing this isn’t to mine for a strategy, but to develop some numeracy around market returns and prices. This is a recurring theme for me, but to get better at making snap judgments, you train your brain on strands of data and comparisons. You’re creating context to recognize when something sounds unreasonable. This will be a survival skill in slop-world where we are doing less work at the bit-level but find ourselves buried in verification.

The exercise provides a confidence interval for: what premium would you be happy to pay for the put so that you could skip diversifying and just hold 100% SPY plus the insurance policy?

If you run this exercise, you’ll find for anyone with a typical risk preference*:

At 1% it’s a no-brainer.

At 5% it’s meh.

*It’s a bit chaotic to throw the phrase “risk preference” into that sentence so freely, but you can, in the spirit of upgrading your context, learn a lot from Elm Wealth’s whitepaper How We Invest

I posted that thought exercise on X and received a reply I really liked:

The Option Premium
@theoptpremium
@KrisAbdelmessih You don’t even need the backtest to see where it lands. 3% a year for 25 years is 75% of your starting capital spent on insurance. In that stretch, a 15% out-of-the-money put paid off meaningfully in 2008, a little in 2002 and 2022, and nothing in the other 22 years. Add it up…

It’s a great example of the type of on-the-fly reasoning one develops when they don’t have the luxury of time.

I’ll rephrase the tweet’s approach in my own way, showing my logic at every step.

1. Which years had a point-to-point return of -18% (the put’s breakeven) or more?

  • 2001 or 2002. One of those years, I can’t remember, but if the put paid, it was very small.

  • 2008. I’d guess the market was down ~40%, so the put paid (40-18)/3 or about 7x

  • 2020. Oh wait, the market didn’t actually finish lower iirc. The put didn’t pay out.

  • 2022, it might have paid a small amount

2. Estimating net losses on the put

Let’s be conservative and say the put broke even in the wake of the dot-com era and 2022. So the put loses its full value in 22 years, pushes in 2 years, and makes 7x one-time.

Net net, you lost 15 out of 25 bets or 60% of your premium. If the put costs 3%, the hedge pulls down your arithmetic return by 1.8% per year.

3. What does this mean for the strategy?

Back of the envelope, I’d guess your annual arithmetic return on a naked SPX position for the past generation has been close to 10% and lop off about 1.3% for vol drag* to get a CAGR ~8.7%

*Vol drag

=½ * variance

=½ * volatility2

=½ * .16 2

=1.28%

This put cost you another 1.8% per year, leaving you with about 8.2% arithmetic but a touch less vol drag because it lowered your volatility. It’s a bit crunchy to import the savings from a 2008 tail event into volatility, which is more descriptive of a distribution’s meat, but a 40% move in variance space is nearly 10x bigger than a typical 15% move, so if you think of 2008 as 10 years, then the GFC represents about ⅓ of the sample in variance time.

Notice, the put doesn’t erase 2008, but it makes the loss much smaller. Without the put, 2008 was down 40%, but with the put, your loss caps at -18%.

Now measure each version as a miss from an average year:

  • Unhedged, the average year is about +10%, so −40% is a 50-point miss.

  • Hedged, the average year is about +8%, so −18% is a 26-point miss.

Variance cares about miss squared.

50² = 2500.

26² ≈ 676.

676 is about ¼ of 2500.

So the hedged 2008 carries about ¼ of the variance the unhedged 2008 did.

In other words, the put removed ¾ of 2008’s variance!

Since 2008, a single year, carried about ⅓ of the whole sample’s variance, the put removed about ¼ (¾ of ⅓) of the total variance.

I’m going to repeat this for tedious emphasis:

Removing ¾ of the variance for a single giant year out of 25 years removed ¼ of the total variance

¾ of the total variance remains, but since vol is the square root of variance, we need to compute √0.75 ≈ 0.87.

87% of the volatility remains, so vol goes from 16% to about 14%.

Vol drag is half of vol squared: ½ × 0.14² ≈ 1%.

So we get 8.2% arithmetic return with the put minus ~1% of vol drag and we end up with a CAGR of about 7.2%

The put cost 1.8% per year, but because it reduced drag as well, it only lops ~1.5% from CAGR. This represents somewhere between a third and a half of historical equity risk premium. Whether this is worth it or not is in the eye of the beholder, but it doesn’t sound like puts, which we expect to have a premium, are egregiously high.

4. What if the put only cost 1%?

We’ll stay conservative and say you push in 2002 and 2022. But in 2008 you make (40-16)/1 or 24x on the puts. You basically break even on owning the puts and cut off your left tail!

You get better at this the more you practice, but moving in between risk, return, and payoff scenarios like this should not only be fun but inoculate you from the viral disease of fuzzy thinking

Stay groovy

☮️

“Compounding Through Risk, Not Return”

🎙️Philip and Alan Maymin, co-heads of global asset allocation at Janus Henderson, joined Corey Hoffstein on Flirting with Models (S7E35, 65 min).

This is an outstanding interview. I’ll let the quotes carry, but I add my own comments and emphasis to direct your attention to what I found especially important from the parts of the transcript I curated.

It’s fitting to add that another great dork gift this season would be my favorite finance book, which is by Philip Maymin, Financial Hacking.

Corey’s intro on the guests

Corey: In this episode, I speak with Philip and Alan Maymin, co heads of global asset allocation at Janus Henderson. Philip and Alan run the asset allocation group originally founded at Janus Henderson by Nobel Laureate Myron Scholes more than a decade ago, And they’ve arrived there by wonderfully different routes. Philip through LTCM, academia and behavioral analytics. Alan through high frequency trading derivatives and prop desks. At the heart of their process is a simple but subversive idea. As Philip put it on our pre call quote, people live in an average return world and they ought not. What matters to investors is compound growth. And the brothers believe that the surest way to improve compounding isn’t chasing higher returns, but managing risk. Drawdowns destroy compounding and avoiding them is the more tractable lever. They connect theory to practice through the options market calls and puts embed the markets collective forward looking views of risk, making them one of the richest sources of information available to an asset allocator. We discuss how they extract those signals and propagate them across more than 4,000 assets to build portfolios around client objectives rather than benchmarks.

why risk is the easier lever

Corey: I know this idea of compounding is really critical to the way that you all think about portfolio construction and our pre call, you sort of laid out the chain of logic as being, well, everyone wants more money, more money comes from better compounding. And while you could try to improve expected returns, I think your quote was risk is easier. You can talk a little bit about why risk is more tractable as a lever for improving compounding versus expected returns.

Philip: This is awesome because there’s quite a few reasons. First is this anomaly or puzzle. If you have a positive average return scenario, right, you have some alpha, that’s your average return. What you really want is to make more money later. So you want the compound return to be higher, and the compound return differs from the average by the risk drag…Fine. If you think about that equation, compound return is average return minus risk. Average return, you know, I don’t use this word a lot, and I’ve never heard this word used a lot. Whenever I hear it, it’s always related to alpha. Alpha is fickle. You never hear about fickle. Right? It’s not a very common word, but it’s perfectly encapsulates what alpha is. You could spend your entire career, and it could be a short career, it could be a long career, and suddenly you wake up one day and what you thought was your source of alpha is gone. The world has changed. It’s moved on. Your contacts are gone. The sort of information or knowledge you thought you were extracting just suddenly gone. That’s not the case for risk management. If you’re managing risk, you’re expecting things to change and you can build not a fickle source of knowledge, but a compound base of knowledge that grows on itself. You know how to manage in different environments. That’s not gonna go away. We’re gonna suddenly live in a world where there’s only one environment. No. Environments will always change. The other reason is Bob Merton in 1980. He wrote a paper and one of the things most memorable… It was a great paper, but in… Even in the appendix, he had a really important insight. The insight was this, purely on a statistical basis. How confident, how far back do you have to be just looking at data to distinguish an alpha, an average return, an average from being a zero? And you have to go pretty far back. All it depends on is the length of time. It doesn’t matter if you slice it up into weekly or daily returns. It doesn’t matter. Just the starting and ending and how long a period you have. So you have to go back pretty far, sometimes decades, to be sure that what you’re measuring just statistically is different from zero. So at some point, you’re one is IBM, does it have a positive alpha? And you’re comparing returns from the nineteen seventies. Like that’s not the IBM of today. What is the point? So alpha is fickle, it’s hard to measure. Fine. But risk? Risk as he shows in that appendix and as you well know, the finer you cut the returns, the better your measurement of risk. It’s a stronger, more secure. You don’t have to go further back in time. You just cut it from monthly to weekly to daily to hourly and you have a better and better estimates of risk. So those things combined, risk is easier to measure, it’s reflecting the environment that things can change, you’re able to compound your knowledge, you’re able to build things for the long run.

In short, alpha is unreliable, but the principles of risk management are stable.

This point deserves an extra comment: All it depends on is the length of time. It doesn’t matter if you slice it up into weekly or daily returns. It doesn’t matter. Just the starting and ending and how long a period you have.

This idea is echoed in Benoit Mandelbrot’s Misbehavior of Markets, where the scientist explains that price behavior is fractal and non-Gaussian. Market shocks are power-law distributed. Since power-law functions are less steep than exponential functions (the normal distribution curves are exponentials), the tail probabilities do not decay as quickly as bell curves predict. Mandelbrot shows that cotton future price charts are statistically self-similar. If you zoom in on a daily chart of cotton prices, the jagged pattern looks similar to a monthly or yearly chart despite the difference in scale.

My post Know Nothing Sizing discusses how risk is the sturdier foundation for sizing because volatility is far more predictable than returns.

Go here if you’re interested in following up on power laws.

reading the options market

Corey: I know one of the key inputs for your process is looking at options markets, information embedded in puts and calls to inform your risk estimates. And this is across not just major asset classes, but correct me if I’m wrong, I think it’s thousands of assets and securities that you’re grabbing this data for to help inform your portfolio construction. Can you talk a little bit about how you utilize the options chain and the information embedded in there to help you measure both upside and downside risk and how it informs your process?

Alan: The best thing about the options market is that they provide a forward looking estimate of the upside and downside of a particular asset. Further, they require an exchange of premium in order for a trade to occur. So truly, people need to put their money where their mouth is in order for the options market to clear. As a result, by reading the information, by reading the trades, by reading the prices, we can see where people are expecting the asset to move over a certain period. This information is extremely rich. It transcends multiple different time horizons and multiple different strikes. In other words, you can see across an entire distribution of possible outcomes over a variety of time periods, what the market is saying is the probability of the underlying to achieve that particular outcome. For us, we can take that information and we can translate it into what is the probability or what is the likelihood of this asset moving up or what is the upside risk? Similarly, what is the downside risk or the left tail risk of a particular asset? By keeping track of that information over time and importantly, their ratio, we can see how a asset changes its upside versus downside over time.

the next best thing to an oracle

Corey: One of the potential constraints of looking at something like the options market is that the options prices can only reflect what market participants can inherently imagine. So how do you think about the unknown unknowns? The risks that by definition aren’t being priced in anywhere?

Philip: The options don’t reflect, like Alan’s example before, just fire or just earthquake. If you’re buying insurance on a house, it’s anything can happen. Similarly with option markets, it’s not that I’m buying a call option in case their earnings are up or in case of something else. It’s in any of the possible cases. So in a sense, a lot of the unknown unknowns are already in the options as much as they possibly can be. Even if you don’t know what might drive it, you can have a sense of foreboding. That’s what investors will draw on to set their prices and put their money where their mouth is. Of course, there’s always unknown about the future. Right? But what is the alternative? We don’t have a perfect oracle where we’re saying the options are not as good. Yes. They’re not as good as a perfect oracle. What’s the next best thing? Right? Just look at past performance. Look at the rearview mirror. Look at earnings or other news items that come out that’s already in the past as well. What other alternatives are there? One way that Myron puts it, you know, sometimes people ask us, are we fundamental or quantitative? And it feels like we should say we’re quantitative, like everything we’ve been talking about. Right? We’re not, like, visiting companies and going undercover and checking their stockrooms or anything. On the other hand, Myron puts it this way, we have thousands of the best fundamental analysts working for us. Suppose you really were, you had an infinite budget or maybe you’re using OpenAI’s agents or whatever. You have hundreds, thousands of human and AI agents investigating everything they can. How would you incorporate all of that information into a single signal that you could then manage your portfolio off of? Wouldn’t it be nice to use something like prediction markets or option markets where they could express their confidence through some sort of reward mechanism like the market? That’s what the options are doing. That’s what they provide us as a way of unifying information signals confidence from thousands of the smartest people on earth, tracking what they believe to be the most honest true thing, whether it’s known knowns, known unknowns, whatever. And that provides a signal. So it’s not gonna be the Oracle of Delphi, but it is something that you can look at and use to your benefit not to necessarily trade against them. That’s another important caveat… We’re not thinking like there’s a mistake in the option pricing in the market and we could take advantage of it. On the contrary, there’s just all this free information that people are shouting from the rooftops. Here’s what we see. Here’s where we’re putting our money. And almost everyone on Earth is covering their ears, their mouth, their throat, their eyes, everything and ignoring it. Why? Why not at least take in that information? There may be times when you deviate from it. It’s like using GPS. Right? Traffic enabled GPS. Every time I deviate, I regret it. I’m sure this happened to you too. It knows better than you almost all the time because it has forward looking information. It has traffic. It has people reporting stuff. There may be a few exceptions. When I’m really close to my house or a place I’m very familiar with and it tells me to go some way, I know I can go a different way. I can cut through the neighbor’s yard or whatever the heck I do. So, similarly, if you’re taking the options information overall, but you have some specific view on a particular thing and you like to deviate from it, so be it.

Philip is preaching to the choir (me). This idea of taking the options market’s collective intelligence, parameterizing its features, calling the liquid ones “fair”, then looking for departures from that is the foundation of moontower.ai’s approach.

risk-neutral vs real world

Corey: Anyone who’s classically trained as a quant or certainly in derivatives is gonna immediately jump on the fact that options are priced under what we call the risk neutral measure and you’re trying to use them to make statements about real world risk. The P measure, those are different measures and they’re not necessarily always in alignment with each other. So I’m curious how you think about sort of this p verse q problem when using option implied signals.

Philip: What is the biggest difference in the two measures? It’s of course the drift, right? And that’s what Fischer and Myron showed in 1973. The drift is completely different. It’s risk neutral in one versus whatever arbitrary thing it is in reality. The rest of the distribution can also be different. However, you can try simulation of assuming what the real is and then figuring out what the risk neutral is. Almost all of that difference is coming from the change in the drift. The shape of the distribution is fairly robust across the changes in measure. Like Alan was saying, we have the upside risk. We have the downside risk. It’s not a coincidence that we’re ignoring the middle. The entire philosophy is built on the idea that middles don’t matter. It’s the tails that matter. You can throw away… We have a paper showing this. You could throw away the middle 80% of a distribution, and your risk is basically the same. You’re gonna have an 80 to 90% correlation, and 80, 90 of the risk is still there. The tails are what matter, and the tails, that’s what the options price very, very well. And that’s the shapes that we take as signals. And bear in mind, like Alan was saying, it’s not that we’re taking one number on one day and that’s some God given truth. We’re looking at the changes in that signal relative to itself, relative to others. Things don’t always work 100%. But the risk we’re not so much worried about is the p versus q, the risk neutral versus real world because it would have to differ in a way that goes against all of these different parts of our process that translated into a reasonable signal. And at the end of the day, it’s a very reasonable thing. Look. What is Alan saying that we’re doing? Ultimately, boiling it down. If suddenly and for an extended period of time, people are buying a lot more calls and a lot less puts, that’s interesting information. P versus q notwithstanding.

The emphasized parts were amazing to hear after I recently published are the options market’s odds any good? which showed that insofar as the market’s pricing of option spreads is “wrong” compared to what was realized, the bulk of the edge is coming from the risk-free rate drift assumption that is required to ensure “no-arbitrage”. If you use a more empirical, markets-tend-to-go-up drift assumption, the mispricing goes away, but my claim is that someone trying to exploit options because of the risk-neutral assumption is just underwriting equity risk premia and conflating an option edge with harvesting beta.

correlations in the tails

Philip: Correlations are tricky. When? And where? In the middles. Like equity and fixed income for the past few years and decades. It’s tricky. Right? It can change from minus point three to plus point three, and that ruins people. So estimating that is really hard. It’s really slippery. But what about in the tails? In the tails, it’s much easier, isn’t it? We have a much simpler challenge in the tails. In the tails, we know the correlations are one. The only real challenge is it gonna be plus one or minus one. We can talk about some tools we’ve developed for that kind of thing using regime based correlation and stuff like that. But already, we’ve simplified it because we know that the correlations will be very, very high, and we’re managing to the tails. It does rhyme with expected return over variance. The way we look at it is skewness or expected tail gain divided by expected tail loss, and then that whole thing divided by expected tail loss again. So you get exactly what you were talking about where you have expected tail gain divided by expected tail loss square, just like you have mean variance optimization instead of mean standard deviation. So the downside plays a much more oversized role. That’s the most important thing is on the downside. So if we can get estimates of correlation for… On the tails, the downside 10% tails on the downside, that essentially answers the questions that we need. We don’t have to worry about the middle in correlation. We don’t even have to worry about the upside correlations.

Corey: I would love to hear you talk a little bit about that regime dependence because I can certainly think of regimes where certain assets might have positive correlations and other regimes where they would almost certainly have negative correlations. Can you talk a little bit about how that flows through the optimization process?

Philip: Again, remember that we’ve simplified it to just thinking about tails. So one of the things we can do is look at the current levels of volatility, let’s say equities and fixed income, relative to their own history. If they are both in the top quartile, the top 25% of their history, equities are elevated and fixed income is elevated. What do you think that tells you about the tail correlation in the current environment? If there’s a problem, they’re probably positively correlated. It’s probably a plus one correlation. They’re both very elevated. People are worried about both things. What if one of them is above… It’s in the top quartile and the other one’s in the bottom quartile? So people are terrified in one asset class and not at all in the other. Then maybe we’re thinking about, like, a minus one tail correlation environment. And if neither of those extremes are in the situation, we’re both kind of around fifty fifty or something, then we’re closer to a zero tail correlation. That’s a simple way of doing it. We have more complicated things where we would also blend it with recent or very modern, like high frequency correlation measures just to ground it towards something more dynamic. But that’s the essential approach is first simplify the problem and then use simple proxies to get at what we’re looking for.

You should realize by now just how rich in substance this interview is. Philip is telling you that in a market shock, the correlation of the vols themselves as assets move around is a clue as to what the tail correlations are in the tails. This is why those macro risk radar charts are interesting…you have events where all assets get smoked (gold, equities, bonds, etc) and some where the correlations diverge sharply (during covid bonds ripped higher as equities fell). Vol surfaces gauge stress, so by considering not just the asset moves but how the vols change given those moves you can learn what the market expects if the stress becomes more acute (ie in the tails).

It looks like Philip gave you enough to point an LLM at the transcript to work out the math to initiate your own monitoring framework.

what risk means

Corey: Alan, the process that you and the team have built is around what you guys call your dynamic risk management process. When most of the industry hears risk, when we talk about academic papers being published around risk, they’re traditionally talking about volatility. What does risk mean to you when you think about it both conceptually and from an actual measurement perspective?

Alan: For us, what we believe is that risk is the chance of falling short of the client’s objective, not necessarily just how much the portfolio moves. For us, if we take an objective based approach, and we can define various scenarios for a particular client or a mandate. What we like to look at in risk spaces, the chance of not delivering what the client is looking to achieve. When people think of risk and they think of volatility, the other problem that they will experience is that volatility treats gains and losses alike. What we do is we try to separate out the upside risk, which for all intents and purposes, could be something that you want. Downside risk, on the other hand, is something that we look to tilt away from. From our perspective, we think of risk not necessarily as a volatility measure, but more so a probability measure, something along the lines of what are the various outcomes available, and how can we tilt those outcomes in the direction upside of what the client wants and away from the downside of what the client would not want.

when dynamic risk goes mainstream

Corey: One of my favorite questions to ask people who are in the portfolio manager seat is, if I’m doing due diligence, what questions should I be asking you? Because no one knows where the skeletons are buried better than the people who are actually running the money. And when I asked you that question on the pre call, you said, well, the question naturally that should be asked every time is, well, if this is so great, if everybody starts doing it, can the opportunity still continue to exist? … If all of a sudden every asset allocator everywhere said, oh, you know what, you’re right, we should be taking these ideas and these measures from the options market. These are great implied return and risk measures and we’re going to build our portfolio that way. Can this still work for you as a means of managing money?

Philip: I think there’s three ways of answering that. One is it’s not the current situation. Why is that not the current situation? Many of the biggest investors are still very constrained. They’re benchmark relative. They can’t deviate too much. When things go up, have to mechanically rebalance. So we live in a world of constraints that prevents that from happening. Imagine you’re driving on the street, you have GPS traffic enabled, but nobody else is allowed to use it. There’s a law. You cannot use GPS. You can’t use any traffic enabled navigation system. What would happen if suddenly more people did? Well, traffic would work better. Right? There’d be fewer traffic jams. We would live in a better world. I remember this is a question I asked Myron early when I first joined because I was as amazed as you are. It’s a reasonable thing to focus on dynamic risk management, take information from option market, reallocate. Why doesn’t everybody do that? The real question is, how can we know when it’s done? … How would we see in the world when this has become so commonly accepted that it probably has gone away? And he gave one of the most amazing answers I could imagine. … He said, we will know that this approach has come to be accepted, and this is his vision. He wants this to happen. I agree. Wouldn’t you? It’s wonderful to be the only person with GPS traffic enabled navigation when everyone else doesn’t have it. But, actually, your life overall would be better if everybody had it. You would be less stuck in traffic. You don’t have to go into side roads all the time. The world would be a better place. That’s Myron’s vision. It’s a very inspiring one. How would we know? One of the ways we would know is look at any fund, any investor. They all have benchmarks, don’t they? And those benchmarks are static. It’s S&P 500 or sixty forty or something else. It’s almost unimaginable now, but what he imagines, and I believe it will happen some day, people should have risk managed dynamic benchmarks. They should have benchmarks say down 60%, down 30%, drawdown or whatever it is, right, not just a sixty forty equity. When that happens, it’ll be obvious just as it would be obvious when everybody suddenly has nav enabled on their cars. The institutions would evaluate how they’re doing business, not to beat a static benchmark, but to manage their risk. And one of the problems with a static benchmark is it feels passive. Passive is a beautiful word. It sounds gorgeous. I’m passive. I’m not doing anything. I believe in the market. But you’re not actually passive. If you have a passive dollar investment and we know that the risks fluctuate just like they did this year, there’s some months that are just far more volatile than other months, then your risk exposure in those months is far greater component of your overall risk than the other months. Just like you don’t want to have too much of your cross sectional risk in one particular asset class, you don’t wanna have too much of your time series risk in one particular period of time. And we know that goes in regimes. We know that risks change. So a passive dollar investment like into the S&P or a 60/40 will actually have wildly changing risks that you’re not controlling. Same as when you’re driving. You suddenly have lots and lots of traffic. You’re not doing anything at all. I’m just being passive. I’m just following the guy in front of me. I’m not banging into him. Great. Good. You’re beating the benchmark by not dying, but it could be a much better world if we all manage our risk in the same way dynamically with this vision that Myron has. That’s when we’ll know that it’s taken over and we’ll all celebrate it.

I won’t belabor the point, but you can appreciate Philip’s answer by remebering that there is such a thing as wall time (named for the clock on the wall) and vol time. Not all 15 minutes are created equal. Just like we acknowledge the difference between time-weighted returns and asset-weighted returns, we acknowledge that how and when returns happen change our interpretation of our exposures.

your thoughts are gerrymandered

In light of midterms, or really the midterm avalanche on social media from all sides, I feel like I can say some stuff that will just be forgotten in the inevitable refractory period of “take” fatigue.

Politics are as old as Cain, the world’s 3rd person.

The game and tactics evolve alongside our technologies and ideological fashions. I’ve never cared about politics despite it having these game-like qualities. I think my disinterest in it is related to why I also don’t consider myself a businessman.

In when quitting is ambitious, I said:

I don’t really see myself as a businessman. As someone who would spin something up just because they see an opportunity… I don’t care about solving a problem just because it exists. There are infinite problems, and I have one attention span. To a businessman, profit helps them filter. But more money than I actually need is not motivating enough to do work I don’t care about.

The bold bit sums up my apathy about politics. I vote. Otherwise I’m an NPC and don’t care to put the effort into not being an NPC in this realm of life.

So I’m not particularly informed about politics. I don’t follow news on it. The job is fundamentally about persuasion, but on matters that are always composed of challenging trade-offs. Like any sales gig, there’s no obligation to present those trade-offs in a way that weakens your case. I don’t find this offensive because I’m a grown-up. But I’m also not drawn to listening to motivated arguments any more than I care to listen to timeshare pitches.

Unless I’m strongly affected by a matter, I will not prioritize becoming sufficiently educated on the topic to the standard of competent debate with a worthy adversary. And if I’m not to that standard, who cares what I have to say beyond entertainment value anyway. [I’m not discounting entertainment value as frivolous…I enjoy hearing takes from people good at making them even if I suspect gaps in their knowledge.]

I’m restraining myself from giving advice on this because I can’t do it credibly. Maybe when you sit on the toilet poasting about what so-and-so said while your legs fall asleep makes a difference in the same way that voting makes a difference in aggregate, even though your singular vote makes no mathematical difference. I can only share what I think, and that is to be taken with you or left behind without prejudice as to what I would prefer.

So here are a smattering of thoughts that you will make what you want of:

  • When I look at political discourse, I see games as well as their necessity. It is serious business. Life and death, joy and sorrow depend on it. I don’t hold my non-participation with pride just as I don’t justify not exercising as much as I should. I recognize that personal failings are themselves trades. Every choice has an opportunity cost.

  • Much of our thinking is gerrymandered, not coherent. Your beliefs are darts that you then draw a bullseye around. I’m ok with this because it’s reality. But I warn you that to internalize it means many chains of logic you encounter, both earnest and propaganda, are not wrong because of how they stick together, but because they fundamentally don’t map to anyone’s reality. There is no normative. It’s just what’s allowed or what’s not. Which brings us to power…

  • In the film Nuremberg, Russell Crowe, playing Nazi leader Hermann Göring, delivers this ditty during his interrogation: “You have your freedom and I am a prisoner because you’ve won and we lost. Not because you are morally superior!” The great tension society must balance is that he’s right, but to maintain order we must all aspire to prove him wrong, which we knowingly do in vain.

  • You want what you want and only then do you retrofit coherence. A trader would notice this before a commentator because a trader’s greatest battles are with p/l. P/L is an agent of Reality over enough trials.

  • The relationship of logic and conclusion is flipped everywhere where reality isn’t the judge. If you need a bridge to stand, you need logic. If you want to cut taxes, logic is a luxury, a tool at best. Remember, the conclusion was born before there was a logic to it, and it will survive once the logic has been refuted, rehabilitated, repackaged, and refuted again.

Anyway, this is what I think our nature is. It’s not evil, it’s just a neutral adaptation. It survives because it works. I don’t care about, nor recommend, your adoption of this perspective. For all I know, it’s a form of self-protection, while I tell myself thinking it comes from observation. Either way, it does explain why political disputes just sound like people arguing over their favorite color.

Figuratively. But you never know these days.

Moontower #335

In this issue:

  • gerrymandered thoughts

  • Levine brain

  • commodity index

  • “stock pickers” market?

Friends,

In light of midterms, or really the midterm avalanche on social media from all sides, I feel like I can say some stuff that will just be forgotten in the inevitable refractory period of “take” fatigue.

Politics are as old as Cain, the world’s 3rd person.

The game and tactics evolve alongside our technologies and ideological fashions. I’ve never cared about politics despite it having these game-like qualities. I think my disinterest in it is related to why I also don’t consider myself a businessman.

In when quitting is ambitious, I said:

I don’t really see myself as a businessman. As someone who would spin something up just because they see an opportunity… I don’t care about solving a problem just because it exists. There are infinite problems, and I have one attention span. To a businessman, profit helps them filter. But more money than I actually need is not motivating enough to do work I don’t care about.

The bold bit sums up my apathy about politics. I vote. Otherwise I’m an NPC and don’t care to put the effort into not being an NPC in this realm of life.

So I’m not particularly informed about politics. I don’t follow news on it. The job is fundamentally about persuasion, but on matters that are always composed of challenging trade-offs. Like any sales gig, there’s no obligation to present those trade-offs in a way that weakens your case. I don’t find this offensive because I’m a grown-up. But I’m also not drawn to listening to motivated arguments any more than I care to listen to timeshare pitches.

Unless I’m strongly affected by a matter, I will not prioritize becoming sufficiently educated on the topic to the standard of competent debate with a worthy adversary. And if I’m not to that standard, who cares what I have to say beyond entertainment value anyway. [I’m not discounting entertainment value as frivolous…I enjoy hearing takes from people good at making them even if I suspect gaps in their knowledge.]

I’m restraining myself from giving advice on this because I can’t do it credibly. Maybe when you sit on the toilet poasting about what so-and-so said while your legs fall asleep makes a difference in the same way that voting makes a difference in aggregate, even though your singular vote makes no mathematical difference. I can only share what I think, and that is to be taken with you or left behind without prejudice as to what I would prefer.

So here are a smattering of thoughts that you will make what you want of:

  • When I look at political discourse, I see games as well as their necessity. It is serious business. Life and death, joy and sorrow depend on it. I don’t hold my non-participation with pride just as I don’t justify not exercising as much as I should. I recognize that personal failings are themselves trades. Every choice has an opportunity cost.

  • Much of our thinking is gerrymandered, not coherent. Your beliefs are darts that you then draw a bullseye around. I’m ok with this because it’s reality. But I warn you that to internalize it means many chains of logic you encounter, both earnest and propaganda, are not wrong because of how they stick together, but because they fundamentally don’t map to anyone’s reality. There is no normative. It’s just what’s allowed or what’s not. Which brings us to power…

  • In the film Nuremberg, Russell Crowe, playing Nazi leader Hermann Göring, delivers this ditty during his interrogation: “You have your freedom and I am a prisoner because you’ve won and we lost. Not because you are morally superior!” The great tension society must balance is that he’s right, but to maintain order we must all aspire to prove him wrong, which we knowingly do in vain.

  • You want what you want and only then do you retrofit coherence. A trader would notice this before a commentator because a trader’s greatest battles are with p/l. P/L is an agent of Reality over enough trials.

  • The relationship of logic and conclusion is flipped everywhere where reality isn’t the judge. If you need a bridge to stand, you need logic. If you want to cut taxes, logic is a luxury, a tool at best. Remember, the conclusion was born before there was a logic to it, and it will survive once the logic has been refuted, rehabilitated, repackaged, and refuted again.

Anyway, this is what I think our nature is. It’s not evil, it’s just a neutral adaptation. It survives because it works. I don’t care about, nor recommend, your adoption of this perspective. For all I know, it’s a form of self-protection, while I tell myself thinking it comes from observation. Either way, it does explain why political disputes just sound like people arguing over their favorite color.

Figuratively. But you never know these days.

Money Angle

a Levine index for commodity nerds

Thought I’d share an idea that you can recycle for your own custom needs.

If I’m drafting a commodity piece and vaguely remember (or I’m not even sure if he’s covered the topic or something adjacent to it) Matt Levine’s take, I don’t want the friction of searching years of email to find it.

I had agents (Grok + Claude) build an index from my Gmail archive of the newsletter. They went through 1,533 daily columns section by section, 8,845 sections in all, read 277 candidates in full, and kept 160. A section makes the cut when a market, a mechanic, or a company story has commodities at its core.

Each entry has the date, the column it ran in, theme tags, a short summary, and one verbatim passage. It lives as flat files in git, one file per entry, and Claude adds new columns every Monday.

When I’m working in my Writing Projects folder, I can then call upon Matt Levine’s brain when needed.

Breakdown:

The problem with commodity investing | 15 min read

Bruno Schwalbach’s points out a flaw commodity traders understand well. The big commodity indices are oil bets. Energy was over half of the S&P GSCI’s weight in 2026 and has carried about 85% of its risk over history. BCOM caps any sector at 33% but still gets roughly half its risk from energy.

He also points out the brutal variance drag: the GSCI averaged 5.1% a year but compounded at 2.7% and how it feels like wasted diversification since across 26 commodity futures, correlation averages 0.14 across sectors s 0.53 within a sector.

His fix, Balanced Commodity, splits risk equally across the six sectors, tilts toward markets that are trending up, and goes to cash when everything trends down. Long-only, unlevered. Backtest from 1996, after costs: 8.2% a year vs 2.8% for the GSCI, max drawdown 35% vs 88.5%, 0.29 correlation to global equities. It made 1.1% in 2008 and 7.5% in 2022.

As you might expect, it will underperform when energy rips. From January to mid-October 2026 the GSCI returned 60% and Balanced Commodity 16%. But I think this is not so much disappointing as a misguided prior on the role of an allocation. If you’re looking for broad exposure to commodities, you’re looking for a well-constructed index. It’s also reasonable, given oil’s chokepoint status in the global economy, to acutely care about a a strategic allocation to that much like you would with gold. You don’t buy gold via its meager weight in an index but consider it separately. There’s no shortage of vehicles for concentrated oil exposure, so there’s no reason to mix your motivation towards it with the broad commodity index motivation.

Bruno’s article is both interesting and educational. It also reminded me of an interview RCM did with Jim Rogers back in 2021. If you traded commodities, you know Jim. He co-founded Quantum Fund with Soros and foresaw the rise of China in the early 2000s. Making a bet not just financially, he moved there with his family to make sure his kids learned Mandarin and is just an adventurous fellow, having been the first person to ever make some notable motorcycle journeys off-beaten paths across various continents.

Before his move and knowing he wanted to get long commodities in a set-it-and-forget-it way, he also found the GS and BCOM indices to be lacking. He fashioned the Rogers index (RICI), but to be blunt, it feels merely incremental with respect to the indices he tried to get around. It’s still 40% energy by weight, better than the GSCI on oil weight, worse than BCOM. But it does have a little wrinkle that always appealed to us floor trader types.

Its roll dates intentionally front-run the much larger GSCI rolls.

Money Angle For Masochists

Dispersion Is No Excuse | 7 min read

If you want to play Where’s Waldo, you can search all my letters for backhanded parentheticals about how the relentless low correlation regime must be a boon to “stock pickers”. It’s backhanded because, of course, it hasn’t. As a cohort, active managers aren’t doing any better than they did in higher correlation regimes where they were always sulking that there was no opportunity for differentiation.

Elm Wealth, instead of being snarks like me, decided to write about this puzzle. I love learning new shit, this is just very enlightening and a neat example of applying portfolio construction knowledge to form predictions. If I was great at that, I might have realized this isn’t quite the puzzle I originally thought.

I’ll let Victor’s quotes carry:

Active managers are conventionally judged against a benchmark, and they manage their tracking error, which is the volatility of their return relative to that benchmark. Clients, consultants and in-house risk managers expect tracking error to stay within a fairly narrow band. When dispersion rises, the same positions generate more tracking error, so managers tend to shrink their bets.

Our LSE friend Dimitri Vayanos has written about this behavior, and its impact, in several excellent papers. If tracking error stays constant, the share of managers who underperform is approximately:

Share underperforming ≈ N(Costs / Tracking Error)

where “costs” include fees and trading costs and N is the cumulative standard normal distribution function.

With a cost disadvantage of 1.5% (fees of 1% and trading costs of 0.5%) and tracking error of 3%, about 69% of managers should underperform. That is close to what we observe. As tracking error shrinks toward zero, the formula gives 100%, the lockstep case from Sharpe’s arithmetic. Dispersion does not appear in the formula because managers holding their tracking error steady make it irrelevant.

Active management is also a negative-sum game on risk, even before costs. Because active portfolios add up to the market, the average active portfolio is riskier than the market. That extra risk is uncompensated, idiosyncratic risk. Even a manager who matched the index return would, on average, deliver a worse risk-adjusted result.

High dispersion sharpens the dilemma. A manager who keeps position sizes unchanged takes on more uncompensated risk. A manager who cuts them gives up whatever help dispersion offered, and pays trading costs to do it. Either way, the investor loses.

Basically, the urge to keep tracking error low is a constraint that makes you unable to take advantage of the increased dispersion between stocks. So long as asset management is fixated on benchmarks like the SP500, disappointment is not actually a puzzle at all.

My timing could not have been (accidentally) better on the topic of dispersion. I scheduled what if the “singularity” trade cracks? to go out Thursday am which happened to be after a giant rally and before a giant bloodbath which was accompanied by:

Image

From My Actual Life

A thought I shared on Instagram (follow) now that my mother is taking an online options course.

[VIDEO PLACEHOLDER: Substack-hosted clip, re-upload or embed from Instagram]

Here are the questions for the first homework I gave here:

Image

And finally, we finally got our chance to see Gary Gulman live as he performed his “Grandiloquent” theater show in San Rafael Saturday night.

Gary’s style combines a unique pacing, a performative vocabulary, with rare mix of high-brow literary references + pop culture-infused storytelling.

This show had a long bit about early 90s rock which is immediately one of my favorite comedy sketches.

There are still lots of dates on his current tour, but the tour is actually 2 different shows. Grandiloquent is only being performed once more. We got lucky because when I booked the show, I didn’t realize he had 2 different shows, and this one felt uniquely special. Yinh and I love to be inspired by live performances, and as we walked out of this, we were just filled with respect for the talent and execution on display. Inner voice admonishing me: “work harder, be better”.

Stay groovy

☮️

Moontower Weekly Recap

“Conversational AI custom media eats traditional media” thesis

I’d let this thread seep into your cells before we continue.

Ok, now you are ready.

[Only do this hands-free via Bluetooth. Safety first.]

While driving to pick up my kids, I interrupted the podcast that was playing to launch Grokbot’s voice mode to ask about TLT volatility (a random market idea popped into my head). The bot is connected to moontower.ai data via MCP and, of course, the internet and its own training data. Before I know it, I’m at the destination and never switched back to the podcast.

This felt weirdly profound.

Podcasts have an infovore following. But can anything compete with an infovore discussing topics they care about and getting to ask questions and guide their own learning? AI + voice feels like the seed of a trend that is not totally visible to everyone. A ”conversational AI custom media eats traditional media” thesis.

If this is a thing, it would leave less attention for the businesses and individuals that monetize attention. Publishers sell page views, minutes, subscriptions, which are all quantities that are produced once, then delivered at zero marginal cost. But custom, conversational media runs on tokens and compute.

Attention is zero-sum bounded by hours and my guess is individualized media means more properties are sucked into the black hole of the “middle”. The “hollowing of the middle” has been the ruthless evisceration of saccharine mass market media. You either want to own a hyper niche or be broadcasting the Super Bowl. The middle is the failed Vice magazine which outgrew an edgy audience but was not large enough to scale its fixed costs.

I’m not planting any new flags here. It’s just part of my gradual awakening to how these powerful technologies can reshape behavior, in turn, altering and replacing business models. The investing question is not “can” but “will”.

An obvious counterargument against this is that most people are consumers and lurkers. They want to scroll and channel surf. They don’t actually want to steer if entertainment is an escape as opposed to an outlet.

That’s all valid and fair. But it’s also consensus (for good reason). But weird things happen when we remove friction. I’m old enough to remember ordering a pizza on the phone before DoorDash existed.

Games are an interesting category in that they’re a hybrid of active and passive. We don’t generally make our own games, but we enjoy active participation in our couch leisure, whether it’s Catan, GTA, or D&D. In Games: Agency as Art, the philosopher C. Thi Nguyen argues that games are the art form whose medium is agency itself. The designer shapes what you can do and what you’re trying to do, but the specific experience depends heavily on what you bring to it. More examples in the same vein:

  • User-made games: Minecraft and Roblox, where players build the content.

  • Fan fiction and modding: people taking a finished story and making their own version.

  • Musical hobbies: Everything from performing an instrument or dance to curating playlists

Choose-your-own-adventure shows like Netflix’s Bandersnatch never caught on. Perhaps people like agency when it’s the point and not when it interrupts a story. But again, this is consensus.

On the other hand, if the friction to self-expression is reduced to the ease by which a thought crosses your mind, like it did when I was driving, this is a glimpse into an unimagined, non-consensus future replete with multi-order cascades that can fuel a substantial engine of investing and business opportunity.

if it feels good you’re too late

I was chatting with a friend this week who I’m so happy for it makes me almost teary-eyed. He turned down an unbelievable job to take a chance on building an HFT startup a few years ago. It’s going VERY well. And it’s fun. And they’re hiring and all the things. I couldn’t help but say, “Man, you’re just in one of those sweet parts of the curve now”. The work fits like a glove and to quote Eddie Murphy “the ends are meeting like a muthfckr”.

It’s HFT with Sharpe so high you don’t bother with numbers, I just call it Charizard. I have to waste my breath because this is as cheap as an option gets, but “are you raising money?”

Of course not.

They raised once at the start and that’s it. Self-financing from here. Interestingly, they raised from VCs, not allocators. This unusual arrangement combined with the mathematical visibility of their edge, which doesn’t exist in long-term stock-picking, just had me thinking again about the nature of investing.

From The Paradox Of Provable Alpha, we know that the only place to mine for alpha among managers is among those with longer-term horizons. The regular investment firms are not as capacity constrained, are an underdog to have any edge at all, and happy to work for a small take on other people’s money. The managers that look like traders, with high turnover and sample sizes, who consistently crush, don’t want your money.

An advantage in long-run investing on the basis of pure analysis is very hard and even if you’re good you there’s no stats that will ever affirm you to be as good as you think. Active managers either think they charge too little, but since they can’t flaunt HFT stats, settle for what the market will bear, or the managers know they are way overpaid. Nobody thinks the fee is exactly right because the industry is some mix of a economic “lemon” problem and a circus exhibition that has little to do with serious investing and more with spectacle. (Pick your favorite negative alpha manager that finds a way to stay relevant and maintain assets).

Alpha in long-term investing is scarce for even more fundamental reasons. The nature of advantage in a zero-sum game (the warm drift of beta is a public good, but alpha is fixed…no winners without losers).

You have probably seen some version of the right/wrong vs consensus/contrarian 2×2 before. If you haven’t, you should sit under a tree with it for a while. It’s liberating in its proscriptiveness.

A looming annunciation of this problem comes from an interview VC Josh Wolfe had nearly a decade ago on Invest Like the Best.

He noticed that every one of his fund’s investments that became a giant winner was highly contentious at the start. Typically, the stake had just one passionate champion for it on the investment committee.

[Do not confuse this with all highly contentious ideas will be winners. It’s a statement conditional on the investment paying off huge already.]

In light of the 2×2, this is not surprising. If the investment makes obvious sense, it will be consensus and even overbid.

I’ve seen an analogous phenomenon in trading, but even in mock trading. I’ve talked about it a lot before, but when I watch a room of people mock trade, I can quickly tell quite a bit about the person from their action or lack of action (in the case of lack of action, I have to probe a bit, but the lack of action comes from a fairly small number of common reasons which are as telling as taking action).

[If I ever host an event like that again, maybe I’ll consider report cards, where I inform the individual what I learned about them and they can tell me if it matched their horoscope.]

But, one consistent trait that I think is good out of the box is the ability to act before all the info is obvious.

If you need to wait until the evidence is convincing, you’re too late. In fact, at that time you may want to be doing the opposite.

Why trading decisions are different from many routine decisions

This is a ruthless aspect of markets because it defies the requirements of regular decision-making. In your daily life, you’re not in a race to buy the best phone or lunch before it’s gone. You can usually hold out for a bit more info, improve decision quality, and not have your patience used against you.

The ability to act before you have all the info is useful in other zero-sum games as well. There’s an unsavory connotation to say someone has great political instincts because the object is usually someone who seems impure. The left-coded VC who moved right sharply after Covid. Perhaps they were honestly disillusioned with their team. Perhaps it was just good for business. Either way, the begrudging respect one might hold for a winning political trade echoes the lauded financial agnosticism of great traders. Stan has no problem flipping a yard from long to short at a speed that obviously outpaces any kind of formal research process.

VC to me is an interesting solution to long-term investing because its very structure acknowledges the futility of analysis on forecasting what is going to pay-off. Instead, create a marketing machine that both stabilizes assets and makes founders, the lifeblood of your upside, want to come to you. Then diversify the lotto tickets. As long as you’re drawing from a sample that truly has no mean, then surviving practically ensures thriving.

Yes, the surviving is hard, but has surprisingly little to do with returns.

Related

The Efficient Venture Markets Hypothesis (9 min read)

Abraham Thomas doesn’t publish too often but I am always sure to read them. This one opens:

Venture capital is deeply weird these days. Consider these phenomena…

Thomas’ description of VC circa 2026 strikes me as a triumph of venture creating demand for its services by becoming a part of the “market portfolio” (in the CAPM sense of the word). It is now as much beta as SPX members, which elevates it above the bucket of crabs that is zero-sum competition for outperformance. VC funds are essential allocations to those trapped in the asset-managment-industrial complex should seem paradoxical if their scale ensures beta-level returns. But this article makes it make sense.

hacking hope

Jared Dillian has this great essay, The Reason We Do Everything, from earlier in the year that opens:

Norm MacDonald, one of the funniest people to ever walk the planet, was also a degenerate gambler, busting out a number of times and going to zero. His therapist told him that he gambled in order to escape reality. Norm said, “That’s why everyone does everything.”

You should read the essay, but today I just want to zoom in on something Norm said. I watched the recent documentary Judd Apatow did on him (Netflix). Apparently, Norm was a heavy and eventually reformed gambler. Judd pulls the following quote from Norm’s 2016 memoir Based on a True Story:

Most people would think it’s the wins that keep the gambler going, but any gambler knows this is not true… When the red dice hit the green felt… and you’re declared the winner… you feel relief. Relief is all, and relief is fine, but hardly what a man would give the whole rest of his life to gain. It has to be something else… It is a particular moment, a magic moment that occurs after the placing of a bet and before the result of that bet. It is after the red dice are thrown but before they lie still on the green felt… As long as the red dice are in the air, the gambler has hope. And hope is a wonderful thing to be addicted to.

At the seat of any good comedian’s observational power is a rare balance of sensitivity with cold logic. Norm is able to feel and diagnose simultaneously (his struggle to quit is a notch for philosopher David Hume’s idea that we are slaves to passion).

As you gaze at the horizon in 2026, dotted with inescapable metaphorical gambling billboards, I urge you to remember that their pitches are hacking an otherwise adaptive impulse just like Frosted Flakes hacks our predisposition to the muted sweetness of pre-industrial fruit. The impulse that’s being hacked is our urge to grow.

Celebrating short-term wins when you don’t have an advantage is like falling in love with the good cop during an interrogation. You are being manipulated. In the gambling sense, by chance herself. She’s an abusive partner who gives up the milkshake just enough for you to stay.

We need growth so nature wraps it in hope. Take the motion to gamble as a sign that you crave risk. Risk is good because it precedes growth. Risk involves failure. Failure leads to learning. Learning changes behavior.

Don’t waste the urge towards risk on a loop where the lessons of failure fail to compound towards durable knowledge that you can actually build upon.

Gambling addiction has always seemed to me like the “insanity of doing the same thing over and over again and expecting different results”. Norm humanizes why we are susceptible to it without taking the stupid low road that it has to do with innumeracy or anything like that. But as it increasingly gets hawked as something more noble (ahem, prediction), they are trying to bring it even closer to what constitutes growth and learning. I’m not against gambling any more than I’m not against freedom to indulge in most conventional “vices”. But big tobacco doesn’t even try to put Vitamin C in their cigarettes.

[Ha, ha I’m lying. They actually did.]

Moontower #334

In this issue:

  • hacking hope

  • if it feels good you’re too late

  • the geometry of volatility scaling

Friends,

Jared Dillian has this great essay, The Reason We Do Everything, from earlier in the year that opens:

Norm MacDonald, one of the funniest people to ever walk the planet, was also a degenerate gambler, busting out a number of times and going to zero. His therapist told him that he gambled in order to escape reality. Norm said, “That’s why everyone does everything.”

You should read the essay, but today I just want to zoom in on something Norm said. I watched the recent documentary Judd Apatow did on him (Netflix). Apparently, Norm was a heavy and eventually reformed gambler. Judd pulls the following quote from Norm’s 2016 memoir Based on a True Story:

Most people would think it’s the wins that keep the gambler going, but any gambler knows this is not true… When the red dice hit the green felt… and you’re declared the winner… you feel relief. Relief is all, and relief is fine, but hardly what a man would give the whole rest of his life to gain. It has to be something else… It is a particular moment, a magic moment that occurs after the placing of a bet and before the result of that bet. It is after the red dice are thrown but before they lie still on the green felt… As long as the red dice are in the air, the gambler has hope. And hope is a wonderful thing to be addicted to.

At the seat of any good comedian’s observational power is a rare balance of sensitivity with cold logic. Norm is able to feel and diagnose simultaneously (his struggle to quit is a notch for philosopher David Hume’s idea that we are slaves to passion).

As you gaze at the horizon in 2026, dotted with inescapable metaphorical gambling billboards, I urge you to remember that their pitches are hacking an otherwise adaptive impulse just like Frosted Flakes hacks our predisposition to the muted sweetness of pre-industrial fruit. The impulse that’s being hacked is our urge to grow.

Celebrating short-term wins when you don’t have an advantage is like falling in love with the good cop during an interrogation. You are being manipulated. In the gambling sense, by chance herself. She’s an abusive partner who gives up the milkshake just enough for you to stay.

We need growth so nature wraps it in hope. Take the motion to gamble as a sign that you crave risk. Risk is good because it precedes growth. Risk involves failure. Failure leads to learning. Learning changes behavior.

Don’t waste the urge towards risk on a loop where the lessons of failure fail to compound towards durable knowledge that you can actually build upon.

Gambling addiction has always seemed to me like the “insanity of doing the same thing over and over again and expecting different results”. Norm humanizes why we are susceptible to it without taking the stupid low road that it has to do with innumeracy or anything like that. But as it increasingly gets hawked as something more noble (ahem, prediction), they are trying to bring it even closer to what constitutes growth and learning. I’m not against gambling any more than I’m not against freedom to indulge in most conventional “vices”. But big tobacco doesn’t even try to put Vitamin C in their cigarettes.

[Ha, ha I’m lying. They actually did.]

Money Angle

I was chatting with a friend this week who I’m so happy for it makes me almost teary-eyed. He turned down an unbelievable job to take a chance on building an HFT startup a few years ago. It’s going VERY well. And it’s fun. And they’re hiring and all the things. I couldn’t help but say, “Man, you’re just in one of those sweet parts of the curve now”. The work fits like a glove and to quote Eddie Murphy “the ends are meeting like a muthfckr”.

It’s HFT with Sharpe so high you don’t bother with numbers, I just call it Charizard. I have to waste my breath because this is as cheap as an option gets, but “are you raising money?”

Of course not.

They raised once at the start and that’s it. Self-financing from here. Interestingly, they raised from VCs, not allocators. This unusual arrangement combined with the mathematical visibility of their edge, which doesn’t exist in long-term stock-picking, just had me thinking again about the nature of investing.

From The Paradox Of Provable Alpha, we know that the only place to mine for alpha among managers is among those with longer-term horizons. The regular investment firms are not as capacity constrained, are an underdog to have any edge at all, and happy to work for a small take on other people’s money. The managers that look like traders, with high turnover and sample sizes, who consistently crush, don’t want your money.

An advantage in long-run investing on the basis of pure analysis is very hard and even if you’re good you there’s no stats that will ever affirm you to be as good as you think. Active managers either think they charge too little, but since they can’t flaunt HFT stats, settle for what the market will bear, or the managers know they are way overpaid. Nobody thinks the fee is exactly right because the industry is some mix of a economic “lemon” problem and a circus exhibition that has little to do with serious investing and more with spectacle. (Pick your favorite negative alpha manager that finds a way to stay relevant and maintain assets).

Alpha in long-term investing is scarce for even more fundamental reasons. The nature of advantage in a zero-sum game (the warm drift of beta is a public good, but alpha is fixed…no winners without losers).

You have probably seen some version of the right/wrong vs consensus/contrarian 2×2 before. If you haven’t, you should sit under a tree with it for a while. It’s liberating in its proscriptiveness.

A looming annunciation of this problem comes from an interview VC Josh Wolfe had nearly a decade ago on Invest Like the Best.

He noticed that every one of his fund’s investments that became a giant winner was highly contentious at the start. Typically, the stake had just one passionate champion for it on the investment committee.

[Do not confuse this with all highly contentious ideas will be winners. It’s a statement conditional on the investment paying off huge already.]

In light of the 2×2, this is not surprising. If the investment makes obvious sense, it will be consensus and even overbid.

I’ve seen an analogous phenomenon in trading, but even in mock trading. I’ve talked about it a lot before, but when I watch a room of people mock trade, I can quickly tell quite a bit about the person from their action or lack of action (in the case of lack of action, I have to probe a bit, but the lack of action comes from a fairly small number of common reasons which are as telling as taking action).

[If I ever host an event like that again, maybe I’ll consider report cards, where I inform the individual what I learned about them and they can tell me if it matched their horoscope.]

But, one consistent trait that I think is good out of the box is the ability to act before all the info is obvious.

If you need to wait until the evidence is convincing, you’re too late. In fact, at that time you may want to be doing the opposite.

This is a ruthless aspect of markets because it defies the requirements of regular decision-making. In your daily life, you’re not in a race to buy the best phone or lunch before it’s gone. You can usually hold out for a bit more info, improve decision quality, and not have your patience used against you.

The ability to act before you have all the info is useful in other zero-sum games as well. There’s an unsavory connotation to say someone has great political instincts because the object is usually someone who seems impure. The left-coded VC who moved right sharply after Covid. Perhaps they were honestly disillusioned with their team. Perhaps it was just good for business. Either way, the begrudging respect one might hold for a winning political trade echoes the lauded financial agnosticism of great traders. Stan has no problem flipping a yard from long to short at a speed that obviously outpaces any kind of formal research process.

VC to me is an interesting solution to long-term investing because its very structure acknowledges the futility of analysis on forecasting what is going to pay-off. Instead, create a marketing machine that both stabilizes assets and makes founders, the lifeblood of your upside, want to come to you. Then diversify the lotto tickets. As long as you’re drawing from a sample that truly has no mean, then surviving practically ensures thriving.

Yes, the surviving is hard, but has surprisingly little to do with returns.

Related

The Efficient Venture Markets Hypothesis (9 min read)

Abraham Thomas doesn’t publish too often but I am always sure to read them. This one opens:

Venture capital is deeply weird these days. Consider these phenomena…

Thomas’ description of VC circa 2026 strikes me as a triumph of venture creating demand for its services by becoming a part of the “market portfolio” (in the CAPM sense of the word). It is now as much beta as SPX members, which elevates it above the bucket of crabs that is zero-sum competition for outperformance. VC funds are essential allocations to those trapped in the asset-managment-industrial complex should seem paradoxical if their scale ensures beta-level returns. But this article makes it make sense.

Money Angle For Masochists

Trigonometry and Vol

I had Claude make me a custom chalkboard app for making math and investing tutorials Khan Academy-style. For fun, I asked Claude to use it to teach me something. Claude knows me better than my mother, so naturally it chose to talk about volatility.

It created this geometric intuition for volatility. Watch the video and then if you feel like visiting trigonometry, below you can read a full treatment of what cosines have to do with correlation and volatility estimates! → Using Geometry to See Why Volatility Scales With √t

I’ve mentioned before that I’m playtesting a game called Market-Maker that I started making just over a year ago. The current form of it didn’t come together until a) I attended the Arbor Bootcamp last November and got inspiration for one of the core mechanics and b) vibe-coding got so good that it could build a fully digital version of the game that can be played on the internet.

I’m still iterating on it (slowly, this is a hobby project), but when I was playing with a trader friend he understood immediately how to price a straddle that expired in round 5 (it’s the lesson of the video).

Although sometimes that sqrt(round) trick isn’t quite right. The reason, to put it in the context of the geometry article, is that there are card interactions that would make the “angle something other than 90 degrees” and for sigma itself to change. Your cards are private information. Looking forward to getting to play this with you. I think it will be quite the mix of fun and educational (but the goal for this is really fun).

So succinct it’s genius

Corey’s comment is outstanding framing.

From My Actual Life

Self-care = Metallica at the Sphere

Stay groovy

☮️

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