Moontower #293

Friends,

The largest firms in the world’s capex doesn’t lie. They’re all-in on AGI. Since the so-called hyperscalers are also a historically disproportionate share of the SPUs, they are dragging the entire economy into that bet.

The “dog with a mouthful of bumper” outcome is they are right and actually eat the whole economy, only to be murdered by their own bunker guards. The irony of using Sama’s ChatGPT to query this:

OpenAI’s fundraising appetite and valuation requires AGI to be a thing, whereas a company like META will survive as a going concern and may even get stronger despite the capex blowing a big hole in their balance sheet temporarily if AGI is not a thing. Yann LeCun, META’s chief scientist and Turing award recipient, is leaving the company. He’s critical of the “scaling hypothesis” which claims that if you just keep throwing compute and data at the models, they will become “intelligent”. He doesn’t see how causal reasoning would ever emerge from pattern-matching even if it’s the most superior pattern-matching the world has ever seen.

This debate feels like mommy and daddy are fighting while the family’s future hangs in the balance. The naughtier kids are using the distraction to raid the cookie jar, while the market’s spirits are a thermometer on who the rest of the kids want to side with.

In a Carlota Perez framework, the prospect of AGI would suggest we are still early in the installation phase, but if the common knowledge surrenders to” AGI is never spawning from the current soil” then we are probably already in the deployment phase but not every middle-manager has taken Prompting 101 yet. In the first case, the calls aren’t hot enough, and in the second, you want your stocking stuffed with puts. Either way, we’ll take the electricity. But Santa, gas not coal, please.

Below the sci-fi (is that science fiction or science finance?) theatrics, we have the mundane business of actually using these things. Last week I shared the listicle voting app thing with some resources for those who want to make stuff with LLMs. This week Khe had a detailed post describing a CRM he built:

Vibe coding? More like “whack-a-mole” coding: The uncomfortable truth around my Pebblr CRM app (link)

Here’s another fantastic article that I think captures nuance between AGI breathlessness and very real capabilities of LLMs today.

Is Vibe Coding the Future of Skilled Work? (Scott H Young)

It’s written from a relatable perspective since I’m using these things so much, while easily admitting they are enablers rather than substitutes. And isn’t that a sweet spot? Like you can do more but still matter? Well for now I guess. We’ll see what happens. If the puts pay off maybe that’s bullish for humanity.

I’ll share some vibe-coding projects here and there.

Here’s my Car Lease Embedded Option Calculator.

Some background for the uninitiated:

Money Angle

A traditional way to think of a stock price is the expected value of its future prices weighted by their probability and discounted to the present. Ignoring the cost of money, in a binary world a $100 stock could be fairly priced if it was 50/50 to be worth $200 or $0. It is also fairly priced if there’s a 20% chance of it going to $500 and an 80% chance of $0.

There’s this vocal VC named Keith Rabois who aggressively cheerleads his companies. I don’t know the guy. His online persona exudes many standard deviations of F-U confidence. Sounds par for the uber-rich these days, but he’s extra fun because he’s pugnacious. And got baited into a silly pride bet.

Here’s a tweet from investor @compound248:

We wouldn’t talk about this in the Masochists section because this is fairly basic financial reasoning. The type that really needs to obvious to everyone in a society is flirting with a simulcrum of the movie Rat Race. But it’s appropriate to spell out the opportunity here in gambling terms.

Keith is offering an even-money bet, his $100k to Compound248’s $100k on the stock multiplying by 31.5

If you think in odds:

Keith is offering even money on a 30.5-1 odds proposition

That might be more clear when you think of Keith buying the stock. If he buys $OPEN he risks losing the stock price or 1 bet and if the stock goes up 31.5x he wins 30.5 (because the 1 bet or amount of cash he spent for the stock is not part of his win or return).

It’s similar to how a stock 2x’ing is 100% return, or 10x’ing is a 900% return. A stock that 10x’s paid 9-1. You risked S, you won 9S if S is the stock price.

Normally when you buy a stock, you get paid dollar for dollar as it moves times the number of shares you have. If the stock doubles you make S in profits which is how much you risked when you bought it. You are paid in proportion to the move.

Keith needs a heroic move to simply get paid even money. His proposition is an arbitrageable violation of how return works. I don’t know anything about Compound248’s outlook on $OPEN by him taking the bet. He could be bullish or bearish. When you hear the proposition, your mind shouldn’t go to “Is $OPEN a good or bad investment?” because what you should do doesn’t depend on this assessment.

Keith’s offer is free money regardless of your outlook.

He’s laying 30.5-1 odds where the max loss is $100k.

So you solve for “How much OPEN do I need to buy to make a $100k profit if it pays 30.5-1?”

It’s simply:

1/odds * bet size

1/30.5 * $100k = $3,280.21

I need to buy $3,280.21 worth of shares. Since the stock was $8.48 that’s just about 387 shares.

If the stock goes to 0, you lose the $3,280.21, but Keith hopefully pays $100k. If the stock does go up 31.5x, you break even.

You could also structure the hedge so that at a stock price of 0, you break even. You buy $100,000/$8.48 or about 11,792 shares. If the stock hits Keith’s bogey you get paid 30.5 on your $100k and you happily peel off 1 bet size to him as a tip. Any share quantity you buy between 387 and 11,792 is a guaranteed win.

An amusing post-script to this story is HF manager Benn Eifert requesting $10mm of action on this proposition. Of course, Keith said no — he’s confident not stupid. Keith said he did the bet with Compound248 just to shut the “troll” up or something.

I don’t understand how rewarding a troll with the easiest money I’ve ever seen is anything but encouraging future trolls, but maybe this is why Keith is rich and I’m writing on the weekend.

Money Angle For Masochists

Tomorrow, we are going to launch the annual Black Friday/Cyber Monday discount for moontower.ai. It’s the only sale we offer during the year (current subscribers will be able to extend at the discounted price as well).

The app’s selling point, what makes it different, is its “point of view” on vol. It really starts from “what’s normal in the options market now” vs what sticks out. The analytics are geared towards answering that question because that’s how you find contradiction.

If sunscreen and umbrellas are simultaneously expensive, it might be because there’s a sunshower expected — but do the odds make sense? Before you could even reason about that you needed to know that sunscreen and umbrellas were both expensive in the first place, otherwise you wouldn’t even consider the question. Questions are where opportunities live.

The price of options is summarized by properties of vol surfaces, which in turn, can be compared to each other. We do the measuring and comparing to point out where the questions are. Options are not as simple as point spreads. Strikes themselves are fixed but the stock price changes, time passes. That same contract’s properties morph like natural landscape seen through a time-lapse camera. We are your guide in this wilderness.

We surface what types of trades look relatively attractive from the vol trader’s vantage point. You can think of that as a solid hypothesis based on the data, but from there, you can adjust based on your knowledge or opinion of what is going on in the name.

We don’t make guesses about the future. We don’t say do X and you will make money. It’s obvious to anyone who has ever taken risk that handicapping the future is not enough to make money. Think about it. How confident are you that the current price of SPY is not the all-time high? Probably 100% and rightfully so. The “SPY to be up 1 cent” one-touch option would be priced at 99.99999% percent. The knowledge is replacement-level not value-add.

You don’t sign up for Bloomberg because it tells you how to make money. You sign up because it helps you see*. And seeing in the correct terms is a prerequisite to profitable decisions. It’s the base of the pyramid upon which you layer the rest of your process.

That’s what we’re solving for in the options niche. The vol trader’s lens.

I made these vids this past week to offer a concise description of some of the key tools for seeing like an option trader:

*When you sign up for analytics, you usually do so knowing what you want to see. But also, there’s a built-in education. You learn what matters to others as well. Digital real estate is not scarce but deciding how to fill it is still constrained by taste and demand. The option pricing software that I used in market-making was also full of clues about best practices because professional users drove the features. What’s interesting when you have an analytics product that serves both retail and enterprise is the features can be a weaker signal about what matters. B-to-B vs B-to-C.

Stay groovy

☮️

Moontower Weekly Recap

Posts:

the art of paranoia

This is a fun one.

A good friend and mentor from the pit sent me this (lightly edited and hyperlinked):

I was catching up on moontowers and reading your cotton story. Made me think of one that you can use if you ever cover interest rates.

I started in trading with silver options. I was quite meek as I had never clerked. I was just backed and thrown in the pit at age 22. Anyway, a couple of years in, silver had rallied from around $4 to $7. The whole pit was short long dated $4.50 calls to a spec who was holding the long. These things were easily exercisable as interest rates were high. Great short to have.

Broker [badge redacted] (you may remember him) offered the synthetic put at zero. No one knew what he was doing. I bid 2 ticks under and he said sold.

So I said “1000”. I think the biggest trade in that pit was like 200.

Anyway I took 1000, exercised them and made 10 grand before commissions. The carry was probably north of 50 ticks and he increased his shorts by around 800 as all the other locals got hit with my exercises. The other locals were pissed.

So I came in through a broker and bid 2 ticks under the next day. Locals hit me. I got lucky because I was clearing [redacted prime broker] and they neglected to put in my exercise. They delayed it by mistake (and gave me the interest as I recall.)

Anyway the locals thought they found someone who would hold these things and just wanted a synthetic put for a credit. I kept doing the trade for a week.

[Redacted broker] got it right by the second day. I think I made another 5 or 10 grand before the locals figured it out and stopped doing it.

Anyway. I hope if you relay this no former silver option traders subscribe. They still don’t know it was me!

There’s a lot going on here!

Option pricing mechanics, pit dynamics, deduction.

Let’s start with the option pricing.

I don’t want to deny you the opportunity to figure that part out for yourself.

In the following list, I’ll start with important clarifications but as the list unfolds the material is more of a hint than just reference information.

Like a quiz show, buzz when you understand why “locals” [ie the other traders] were willing to” sell 2 cents under” and why my friend was willing to buy that level.

Clarification and hints…

1 silver option references 1 silver future. This is typical in commodities whereas equity traders are used to an option having a multiplier of 100
1 silver future references 5,000 troy ounces of silver. So if silver is $10 an oz, it’s a $50,000 contract
The minimum increment, or “tick”, in silver options is .001 or 1/10th of penny. Since it references a 5,000 oz contract, a penny is worth $50 and a tick is worth $5.
This story happened many years ago. Look at those silver prices: “Silver had rallied from around $4 to $7. The whole pit was short long dated $4.50 calls to a spec who was holding the long.”
Silver options are American-style meaning you can exercise them anytime.
“Random assignment”: when an option is exercised, any contract in that series held short is equally likely to be assigned regardless of the clearing firm or account. See rules.
When a broker quotes a “synthetic put” (and yes synthetics are not just identities but directly traded orders!) the convention is to bid or offer as a “differential to intrinsic value”. When my friend bid “2 ticks under”, it’s understood that he’s willing to pay 2 ticks under “intrinsic value”. If the futures are $7 and the broker sells the $4.50 call 2 ticks under than the trade package is:
- broker sells the $4.50 call at $2.498 to my friend
- brokers buys the future for $7.00 from my friend
- Make sure you understand this: to buy a synthetic put it to buy call and sell the future “1-to-1” (meaning for every option you buy, you sell 1 future.) Another way to express that is you hedge on a 100 delta. A synthetic put is assumed by everyone to be a simultaneous package of “long call, short future” in the same way as a straddle is call + put on the same strike or strangle is call + put on different strikes. “Synthetic put” is a real tradeable thing not just the name for an option identity.
While futures are subject to initial and maintenance margin, option premiums are settled in full. In other words, the premium is not itself marginable. That is normal but worth stating because there are some option markets where the premium is marginable (ie WTI options on ICE)

At this point, you should be able to understand my friend’s side of the trade. If you don’t want to bother, stick around, I’ll spell it out soon enough but also that part should feel really obvious to anyone that’s ever owned an American option.

The harder question is:

Why were the broker and the other locals willing to sell him the synthetic put 2 ticks under?

Your last hint is a line already tucked into the story:

The whole pit was short long dated $4.50 calls to a spec who was holding the long. These things were easily exercisable as interest rates were high. Great short to have.

This is fun stuff. Let’s unpack it.

Why did my friend buy the synthetic put 2 ticks under?

It’s an American-style option. He buys the $4.50 call for $2.498 and sells the future at $7.00.

He exercises the call immediately, effectively paying $6.998 closing out the future he sold at $7.00. He makes 2 ticks or $10 actual dollars x 1,000 contracts. $10k profit (before exchange fees which probably claimed ~ 25% of that). Call it $7,500 for a minute’s work and no risk.

How could my friend’s buy be so good if he also says:

The whole pit was short long dated $4.50 calls to a spec who was holding the long. These things were easily exercisable as interest rates were high. Great short to have.

The key:

The carry was probably north of 50 ticks

50 ticks = 5 cents

In other words, the interest on $2.50 of intrinsic option premium was 5 cents or about 2%

💡While a market-maker will be hedged with futures, you only need to post margin to maintain that leg. You can satisfy the collateral requirement with T-Bills, so you really are capturing the the interest on the short option premium without having it offset by hedge funding which is close to zero.

💡In the story, there’s 5 cents of carry. If we knew the DTE, we could back out the interest rate prevailing back then. If we knew the interest rate, we could back out the DTE. But we have neither. We just have the recollection that the call had about 50 ticks of carry.

Think of the typical local’s position:

The whole pit was short long dated $4.50 calls to a spec who was holding the long.

The locals are hedged. They are short the deep in-the-money calls and long futures against them on a 100 delta. In other words, they are short synthetic puts. The puts on that strike are worthless however if the spec never exercises the calls, the locals will get about 50 ticks of interest. It’s like selling a worthless put for a nickel.

💡If you’re so Taleb-pilled you can’t imagine a put being worthless, just pretend you could buy the actual $4.50 put for a tick or the $4.75 put for 3 ticks. There’s such a thing as arbitrage bounds. Options 101 stuff.

Amateurs will say things like “that’s worthless” because they believe a price “can’t get there” but when I say it’s worthless I mean by arbitrage. Like the value of the put on that strike is dominated in such a way that if you could sell it at a positive value there’s free money on the board. An option trader thinks in a matrix of arbitrage relationships when they examine a chain.

You can see why the locals were willing to sell the put 2 ticks under. They didn’t think they would get assigned! They assumed they were going to collect 50 ticks on a riskless position.

The mistake was in thinking my friend wouldn’t exercise the calls. Perhaps they thought he needed to cover risk and if they were all short the calls he was just closing. But that doesn’t make sense to me since he was a “meek” trader before then and certainly wouldn’t have been short 1000 calls.

That’s the most puzzling part to me. I understand why they might believe a customer like the spec would not exercise the call optimally, but when a market-maker buys them you need to update. “This guy who trades options all day is buying an American-style option below intrinsic, I wonder what he’s going to do?”

I don’t want to be too snarky because “street smart” is probably one of the most salient features of a pit trader. I’ll assume I’m missing a detail that makes their decision justifiable.

The rude awakening

“The other locals got hit with my exercises. The other locals were pissed.”

This is the random assignment. When my friend exercised his calls the free interest gravy train slowed down as suddenly some of the locals got assigned which closed their positions (the short call goes away and the long future they were hedged with is liquidated to the exerciser).

💡In random assignment, you “expect” to be assigned on your pro-rata portion of the OI held by all shorts. How many you actually get assigned on can vary from this theoretical expectation because the sequential 1 by 1 assignment process is memoryless just like dealing from a deck of cards with replacement each time. Just because you got tagged on the last one doesn’t change your odds of getting tagged on the next one.

Let’s keep unpacking the story.

Poisoning the well

My friend masked his behavior to blend in with customers and used a broker to “bid 2 ticks under” the next day. The locals didn’t think it was a market-maker going through a broker. The locals were reasonable in not suspecting a local to be hiding themselves behind a broker order for 2 reasons:

Paying commission which would eat into the slim margin even further.
This behavior is known as “poisoning the well”. This type of pro vs pro crime was considered bad form. Just like in poker how the pros try to avoid each other and just eat the fish. Of course from the outside, the norm is considered anti-competitive.

Let’s address both of these.

Regarding #1:

My friend may have worked out a deal with the broker. After all, the broker stands to make a thousand bucks or so for no real effort and if the order is contingent on a reduced commission rate they’ll probably go for it.

[I used to do this all the time. Wet the brokers’ beak. We like to talk about nerd stuff and math here, but trading is a business like any other business. Giving out orders is currency. Traders who pay lots of commission magically get the first call. Whether it’s commodities or equities brokers can solicit the other side rather than bring an order to the pit and instead just cross it without worrying about the order being broken up (ie bettered) they can double-bill. Brokers are fiduciaries and their business is competitive, get too many bad fills and you risk losing a customer, but there’s a lot of leeway in discretion. I’m using a broad brush, the rules and details vary across asset classes, but the mechanics rhyme everywhere.]

Regarding #2:

Prisoner’s dilemma.

How much is it worth to defect?

Will we return to stasis and you got away with one or did you really poison the well?

Will anyone find out?

Does any of this change if you don’t really fit in with the club and can’t hope to be part of it?

And then there’s pure disagreeability mixed with self-confidence. I was at Max’s soccer game last weekend and the ref in a moment of deprecating humor said “They told me when I was a kid you need to either be smart or likeable. And I’m definitely not smart”. I don’t think this particular buddy ever cared about going along to get along. But he’s also super-smart.

I’ll add my own perspective to the game theory stuff. I did nothing but watch markets get tighter, more competitive and more ruthless over time. The edge you never would have settled for becomes something you’d kick grandma down the stairs for before you know it. Someone is going to defect.*

This is chained to another observation. In a landscape where there’s excess profit (ie too much reward per unit if risk) there is always someone sandbaggin’. They aren’t showing how smart or fast they are because they will harvest at this level before it tightens and not have to tip off to others what’s possible. I know this firsthand because of a friend that runs a brilliant trading operation in a niche space in which he never shows the market how fast he really is. Imagine my lack of surprise when I read about this “don’t show” tactic from HFT-er Liquidity Goblin’s Let’s Pretend We Have An Edge (paywalled).

A fortunate mistake

I got lucky because I was clearing [redacted prime broker] and they neglected to put in my exercise. They delayed it by mistake (and gave me the interest as I recall.)

Anyway the locals thought they found someone who would hold these things and just wanted a synthetic put for a credit. I kept doing the trade for a week.

[Redacted broker] got it right by the second day. I think I made another 5 or 10 grand before the locals figured it out and stopped doing it.

My friend was able to milk the ruse for a bit longer because of an error! The prime broker failed to process the exercise which reassured the locals briefly that they were not getting picked off. Sounds like it gave him at least another day to buy more synthetic puts for a credit before the locals got wise to the game.

Knowing is half the battle

Let’s put a bow on this like an 80s cartoon ending lesson.

Being short those synthetics to a single counterparty who doesn’t realize the calls should be exercised is good fortune. But once someone wants to buy those deep ITM calls opening, you know that they know what they’re doing.

I’ll tell you from personal experience that anytime someone wants to trade a deep option or a reversal/conversion where there is little or no open interest your guard goes up. You check your funding assumptions. You think harder about what your option model is saying about the value of an American vs European style rev/con. The difference between the 2 represents the value of the early exercise option for that strike. Those are modeled via trees simulations and difficult to debug as opposed to closed form equations for European Black-Scholes.

Only the paranoid survive.

*I refer you to this excerpt from A Former Market Maker’s Perception of PFOF:

SIG wasn’t know as the “evil empire” on the Amex just because of the black jackets we wore. They understood the risk-reward was completely outsized to what it should be 25 years ago. They were amongst the first to tighten markets to steal market share. They accepted slightly worse risk-reward per trade but for way more absolute dollars. They then used the cash to scale more broadly. This allowed them to “get a look on everything”. Which means you can price and hedge even tighter. Which means you can re-invest at a yet faster rate. Now you are blowing away less coordinated competitors who were quite content to earn their hundreds of percent a year and retire early once the markets got too tight for them to compete.

SIG was playing the long game. The parallels to big tech write themselves. A few firms who bet big on the right markets start printing cash. This kicks off the flywheel:

Provide better product –> increase market share –> harvest proprietary data. Circle back to start.

The lead over your competitors compounds. Competitors die off. They call you a monopoly.

deworsification?

Let’s start here…

Essential Wisdom from Twenty Personal Investing Classics (6 min read)

Elm Wealth distills investing lessons from 20 classics, cross-referencing them with James J. Choi’s paper Popular Personal Financial Advice versus the Professors (Journal of Economic Perspectives, Vol. 36 No. 4) that analyzed advice across 50 best-selling personal-finance books, revealing where popular wisdom aligns (or clashes!) with financial theory.

This stood out to me:

Bear in mind that the first twelve books listed above are written for the broad audience of all investors in developed market economies, with particular focus on US investors. It is well-known (and disturbing) that the financial literacy of this audience is, on average, quite low – as evidenced by a mean score of 56% (yes, that would be an “F” if not graded on a curve) on the below five-question quiz, known as the “Big Five” test by researchers. A survey conducted in 2021 found that less than one third of respondents answered at least four of them correctly, the threshold researchers define as “high financial literacy.” At least as concerning as the low test scores is the fact that the scores themselves have fallen dramatically between 2009 and 2021. If you decide to read some of these books, don’t be surprised to find a good deal of the advice proffered seems blindingly obvious if you come to them with above-average financial sophistication.

Umm, the quiz:

With the growing zoo of money distractions (crypto, gambling, prediction markets) and the results on that quiz, I’m guessing a large swath of society is gonna feel like there’s a financial tapeworm in their wallets.

I tweeted this a few days ago:

Let’s focus on evergreen financial hygeine. These were common themes I saw in the books Elm Wealth selected:

Diversify. It’s the rare free lunch: combine two assets with equal expected return and volatility, and your portfolio’s risk-reward improves.
Minimize frictions. Avoid fees, taxes, and excessive trading.
Reduce touchpoints. The fewer chances to act on emotion, the better your long-term results

Just to piggyback on the diversification bit. It sounds trite, but you’ll hear some people push back against it with the buzzword “deworsification”. When you’re as smart as Warren Buffett maybe you can use this word. But Buffet himself recognized Ed Thorp was a genius despite Thorp’s strong conviction in diversification. [And vice versa, by the way. Thorp’s recollection of hanging out with Buffet when they met in their 30s is pretty heart-warming. Game recognizing game. Apparently, after dinner Buffet showed Thorp a toy he really liked — non-transitive dice. Think of them like roshambo. A beats B which beats C which beats A.]

Here’s the cold-ass truth. Not diversifying is incinerating money. Looking back at your concentrated outcome and saying “see” is not proof of anything but survivorship. In fact, you can prove mathematically that diversifying is a free lunch.

What would you rather own?

Portfolio A: a single stock with an expected return of 10% and 30% vol

Portfolio B: an equal-weight portfolio of 2 stocks where each has 10% expected return and 30% vol but are 70% correlated

Stuff you can read if this is not clear:

The proof is sitting there in market prices too. A diversified portfolio of inferior credits will have a higher rating than the bonds in the basket. A higher rating means a higher price (lower yield).

The nuances of that are better understood today than they were in the heyday of CDO-squared.

See the GFC through a quant’s eyes

Of course, diversification always means you left something on the table in hindsight. You sign up for FOMO. But this is the nature of every decision. If you get crushed, you wished you traded zero and if you win, you wish you traded more. Results alone tell you nothing about the quality of your shot.

To complete the point:

“If you invest and don’t diversify, you’re literally throwing out money,” stated Jeff Yass. “People don’t realize that diversification is beneficial even if it reduces your return.”

Why is this the case? “Because it reduces your risk even more,” added Yass. “Therefore, if you diversify and then use margin to increase your leverage to a risk level equivalent to that of a nondiversified position, your return will probably be greater.”

The modern pod shops are another triumph of diversification, which takes us to the next section…

I am impressed by the multi-managers on the whole. They continue to generate positive returns with outstanding Sharpe ratios and thus far don’t capture the same downside as conventional 60/40 portfolios.

I think of them as the holy grail marriage of deep security research that you would have associated with a long/short fundamental manager plus the quantitative risk management and attribution metis that prop trading firms trading their own money have accumulated through the decades.

Many investors, usually from the cheap seats, want to hate on them because they don’t match the SP500 plus their pass-through fees are multiples of typical fees. Not to mention, hedge fund managers are just natural villains to normal people who maintain a Richard Scarry worldview about which jobs are valuable (eh, like any competitive profession, some of them are decent and some of them are vampires).

Regardless, this quote from Byrne Hobart conveyed something I never found the words for, so as soon as I read it, I had to clip it.

From Why Does Volatility Matter?:

If the portfolio you’re looking at is 100% net long conventional asset classes, and if you think it’s absurd to pay high fees in order to match the S&P with less liquidity—lucky you! You’re part of society’s financial shock absorber, a middle class or above saver in a rich country with functioning capital markets. But if you’re in that position, there’s a very real sense in which joking about how the S&P has outperformed complicated multi-manager setups year-to-date is a form of financial punching-down. They have a different benchmark, and a harder job. And they’re doing you the very generous service of ensuring that the next time you buy the S&P 500, the price of every single component reflects the collective attempt by thousands of professionals with massive data and analytics budgets who are all trying to push the price 1% closer to optimal.

I want to share another post from Andrew who I introduced last week.

So-Called “Bonds” in Prediction Markets

Great subtitle:

Rare events teach slowly

Moontower #292

Friends,

I really liked this listicle by Sasha Chapin:

50 things I know

I was gonna do my usual thing of sharing which items from the 50 resonate most for me.

I’ll give you one.

11. I know that talent doesn’t feel like you’re amazing. It feels like the difficulties that trouble others are mysteriously absent in your case. Don’t ask yourself where your true gifts lie. Ask what other people seem weirdly bad at.

But what I want to know is which of these land for YOU.

This was an excuse to do a project.

Here it is…go vote:

https://sasha.moontowermeta.com/

I have been doing a lot more vibe-coding recently. This project was an excuse to make something that required a database backend to record the likes and user sessions.

I used AI to then document the process which you can read about here:

General environment and stack:

👨🏽💻Setting up Git + Vercel + Terragon

Specific for the sasha voting page:

☕1-page guide

🔍Detailed tutorial

You can find a new portal here and in my signature down below:

🤖Resources to Get More Out of AI

Related and timely — this week Khe documented making a CRM which is the next-level up because it uses authentication (ie username/password to login):

I coded an entire CRM from scratch

If there are any resources you love as you explore, feel free to share!

Money Angle

Essential Wisdom from Twenty Personal Investing Classics (6 min read)

This stood out to me:

Umm, the quiz:

I tweeted this a few days ago:

Let’s focus on evergreen financial hygeine. These were common themes I saw in the books Elm Wealth selected:

Diversify. It’s the rare free lunch: combine two assets with equal expected return and volatility, and your portfolio’s risk-reward improves.
Minimize frictions. Avoid fees, taxes, and excessive trading.
Reduce touchpoints. The fewer chances to act on emotion, the better your long-term results

What would you rather own?

Portfolio A: a single stock with an expected return of 10% and 30% vol

Portfolio B: an equal-weight portfolio of 2 stocks where each has 10% expected return and 30% vol but are 70% correlated

💡Stuff you can read if this is not clear:

The nuances of that are better understood today than they were in the heyday of CDO-squared.

🔗See the GFC through a quant’s eyes

To complete the point:

“If you invest and don’t diversify, you’re literally throwing out money,” stated Jeff Yass. “People don’t realize that diversification is beneficial even if it reduces your return.”

The modern pod shops are another triumph of diversification, which takes us to the next section…

Money Angle For Masochists

Regardless, this quote from Byrne Hobart conveyed something I never found the words for, so as soon as I read it, I had to clip it.

From Why Does Volatility Matter?:

If the portfolio you’re looking at is 100% net long conventional asset classes, and if you think it’s absurd to pay high fees in order to match the S&P with less liquidity—lucky you! You’re part of society’s financial shock absorber, a middle class or above saver in a rich country with functioning capital markets. But if you’re in that position, there’s a very real sense in which joking about how the S&P has outperformed complicated multi-manager setups year-to-date is a form of financial punching-down. They have a different benchmark, and a harder job. And they’re doing you the very generous service of ensuring that the next time you buy the S&P 500, the price of every single component reflects the collective attempt by thousands of professionals with massive data and analytics budgets who are all trying to push the price 1% closer to optimal.

I want to share another post from Andrew who I introduced last week.

So-Called “Bonds” in Prediction Markets

Great subtitle:

Rare events teach slowly

Stay groovy

☮️

Moontower Weekly Recap

Posts:

why poker is used to train traders

This video is the best articulation of why poker is used to train traders.

From the description:

Jerrod Ankenman, professional poker player, co-author of The Mathematics of Poker, and Quantitative Researcher at Susquehanna, explores the connections between poker and trading. Jerrod, who began his career playing poker and went back to earn his PhD, explains how concepts like probability, expected value, risk management, and game theory apply in both the card room and financial markets. Poker and trading both demand strong mathematical thinking, disciplined decision-making, and the ability to manage uncertainty under pressure. Jerrod shares lessons from his poker career (which includes a WSOP bracelet win) that translate directly into quantitative finance and trading strategy.

If you are a trader this video despite not being technical is alpha. It tells you where to look if you pay attention.

A few ideas I’ll re-emphasize:

SIG treats poker as a structured way to train probabilistic thinking. Jerrod structures the flow of the video as a parallel between 3 concepts in poker and their analogs in trading.

Ante
Decision practice
Interpreting outcomes

You’ve heard this before — both poker and trading require making decisions with incomplete information. But a more subtle point is about speed. The goal in both is the same:

“Make the decision now that’s as close as possible to what you’d decide if you had infinite time and information.”

Both poker and trading have an information structure of what’s private and what’s public. But also what’s behavioral. Examples of the trading version of these ideas:

Private info: proprietary models, internal data, trader knowledge sharing.

Public info: news, filings, Fed releases.

Behavioral info: order flow and price action. In other words, what others are doing.

This last point gets a lot of emphasis and maps strongly to poker because at its core, it recognizes that trading is an adaptive, adversarial game not a physics problem. It’s an evolving pattern recognition and categorization problem.

You can’t model every opponent individually, or every trade uniquely, because you shrink the statistical power of your findings. You must intelligently group similar situations into profiles. Familiar poker examples: “tight-aggressive regulars,” “recreational loose players”. In trading, profiles can account for who (ie retail), periodicity (time of day, time of month, and so forth), or why (index rebalance, hedging mandates).

The art is balancing taxonomy and specificity to have enough data to be statistically meaningful, but close enough to be relevant.

A few other powerful ideas:

The big risk isn’t volatility.

It’s being wrong about your edge. The market wiggles are the nature of the game, that’s not a risk.

[I’ll take a moment to repeat myself — if you blow out because the market moved, you were committing malpractice. Being aware that the market will do things amounts to no more than a toddler understanding of risk. Volatility shouldn’t keep you awake at night. That the exchange might cancel your trades or that they may ban orders should.

From Investopedia on the response to the Hunt Brothers’ silver squeeze:

Federal commodities regulators introduced special rules to prevent any more long position contracts from being written or sold for silver futures. This move stopped the Hunts from increasing their positions by temporarily suspending the fundamental rules of the commodities market. With longs frozen and shorts free to pile in, the price of silver began to slide.

From my floor days, I can tell you there’s lore about who knew the valuable bit of info that you were only going to be allowed to do opening trades on the short side. Exchanges were run by the traders and brokers before they went public. This is the weird black or grey swan stuff that bosses worry about.

A company going bankrupt? That’s a line item in portfolio_shock_analysis.xls, not something that makes you cry in public to your investors.]

Back to Jerrod. A big risk is being wrong about your edge. It’s a risk because edge hides behind low signal-to-noise. This is one of the great teachings of poker. Short-term results are noise. He explains that in Limit Hold’em, even a high edge hand has only .02 big bets worth of expectancy vs a standard deviation of 2.5 bets.

[Kris: In investing language, a .008 Sharpe for one trial. The SP500 has a daily expectancy of about 3 bps and 100 bps standard deviation for a daily Sharpe of .03. The poker hand has almost 4x the noise of the daily SP500 return.]

Since poker teaches that you will make the right decision and still lose money, it trains you to emotionally decouple decision quality from result quality.

This is a ceaselessly profound concept. Not because it’s so clever, but because of how it resists internalization. It’s easy to understand, it’s hard to apply the understanding to how we receive the world. Fooled by randomness might be a tired title, but it’s never been stronger as we are bombarded with data.

The risk of being wrong about your edge is insidious because the relative efficiency of markets means it’s hard to make excess returns, but it’s also hard to lose too much doing sensible things. The problem is when sensible things aren’t adding value beyond randomness, but you think they are. You’re wasting your life tossing coins.

[Unless you like action for the sake of action. In that case, you’re understimulated. Go take a risk in the name of actual growth or something.]

The link between speed and skill

Jerrod notes that you don’t have time to “go to the lab” mid-hand or mid-trade. Edge requires building mental shortcuts and intuitions that perform well under time pressure.

This feels easiest to imagine in the world of sports. I’ve heard elite athletes talk about how the big jump from say college to pros is “just” the speed of the game. It’s not that they are doing new things, it’s that they must be able to do the same things faster without losing precision.

I remember reading a profile many moons ago about Jason Kidd who was known for his passing and court vision. I got the sense that he could see a split second into the future. Being able to make and execute a decision just a tiny bit faster compounds into outlier results over time. The long-term ROI on having your intuition slightly better tuned works to disproportionate effect.

This echoes. We play the mock trading games and some people are just a tad faster every time. Maybe when new info hits the game they refresh their market quickly, not necessarily making a perfect 2-way, but it’s biased in the direction of the asymmetry. Ricki Heicklen discussed this with Patrick McKenzie. If you are trading “the sum of the siblings in the room” and someone’s count is revealed, do you Bayesian update in the right direction and in a proportionally coherent way? When you see someone do this consistently, you know they’re clocking differently than the others (and I’m excluding the clueless whose updates are logically incoherent to how they processed a similar situation in the opposite direction).

In competitive scenarios, if you can debug your thinking in the moment, you’re too slow. When making a market for a broker, you need to hear the order, intention, what’s not said and how the trade looks vs the framing of the option chain in seconds (and this of course assumes your tools are already designed with this workflow in mind, showing you the info you need when you pull up the ticker). This is obviously not happening if you need to step through expected value trees. There’s no substitute for reps if you need to decide faster than the speed of system 2 axon to dendrite sex.

Feedback loops to build that intuition

Jerrod is blunt. The best way to learn in poker and trading is post-mortem discussion. Go over the tape with your team. Chat scenarios. A great feature of trading is if you love it, you want to talk about it, so this doesn’t feel like work.

When I was at Parallax, I used to carpool with 3 other traders. Shout out to Steve, Ben, and Rob — I still wish we livestreamed those rides. The commute in the morning was your typical sports or current events banter (ok fine, gossip). But the ride home was all play-by-play of trading scenarios from the day. What happened, what would you have done there, etc?

While Jerrod treats discussion as the primary way to learn (I agree — trading is an apprentice craft) he does acknowledge a role for books.

[Even though I recommend some books, they are more for describing the overall epistemic landscape or inspiration. 99.9% of what I know about trading comes from discussion or experience. I either learned how to price or think about something from someone else or after discovering first-hand a new way to lose].

He mentions that most poker books are wrong. He offers a strategy for figuring out which ones are good. But he also encourages reading the bad books because it reveals how your opponents might think. That bit reminded me of an old post I wrote:

Twitter Reminds Me Of The Trading Pits

[Random: I was hanging out with a trader from my cohort who now runs education for a big prop firm in Chicago. He was re-learning poker because a lot of the stuff we learned 25 years ago is now considered wrong. I’m not surprised, since 1 year of poker information in the online, poker-on-ESPN, poker-celebrity-giving-TED-talks era likely generated a decade worth of info from 20th-century poker.]

In sum, SIG is using poker to build the same mental circuitry that trading relies on in an enclosed, fast-feedback petri dish. It’s speedrunning experiential learning in a low-signal environment so the requirements of a successful trading career seem less alien. If trading were as easy as “just study and you’ll get good grades”, motivation and time would be sufficient ingredients. With trading and poker, you could have infinite time but if you don’t know how to learn, you’re pushing on a string.

The overfit sauna

The phrase “studies show…” is almost always followed by bs. To understand why, I’ll point you to a post by David Epstein.

David is the author of Range, The Sports Gene, and the new book Range Adapted for Young Readers which I bought for my 12-year-old son and nephew.

I’ve been a long-time reader of David’s letter and this post is both useful and timeless.

Everything in Your Fridge Causes and Prevents Cancer

It’s a reminder that outlier studies and results in general make headlines, but are statistically inevitable if you do enough studies.

Excerpt:

It wasn’t every sauna enthusiast who reaped the supposed protective effect against dementia; it was specifically those who used a sauna 9-12 times a month. Sauna bathers who hit the wooden bench 5-8 times a month — sorry, no effect. And those who went more than 12 times a month — again, no luck.

That should raise a caution flag in your head.

When only a very specific subpopulation in a study experiences a benefit, it may indeed be that there is some extremely nuanced sweet spot. But it is more likely that the researchers collected a lot of data, which in turn allowed them to analyze many different correlations between sauna use and dementia; the more different analyses they can do, the more likely some of those analyses will generate false positives, just by statistical chance. And then, of course, those titillating positive results are the ones that end up at the top of the paper, and in the press release.

Here’s the point I want to hammer home: when you see a tantalizing health headline — like that saunas prevent dementia — keep an eye out for indications that the effect only applies to specific subgroups of the study population. Even if the headline is very authoritative, revealing nuggets are often buried lower in the story.

I want to stress that you shouldn’t assume the sauna results can’t possibly be true. But when you see Bears-undefeated-in-alternate-jerseys type conclusions — and someone is claiming one thing causes the other — you should hold out for more evidence.

This doesn’t just happen in health news. Investing/trading is another area where making a mountain out of a statistical molehill is rampant. Unless you are specifically studying a phenomenon that you’d expect to be discontinuous (binary, “phase change”, threshold cutoff) you should be wary of any signal from a specific range of an otherwise continuous function.

I’ll take a simple example from Kris Longmore’s article explaining how month-end rebalance trades work. The post is titled How Wealth Managers Pay You To Trade. He writes (emphasis mine):

How I’d Test This

So here’s the hypothesis: if we can identify which asset outperformed during the first part of the month, the underperformer should outperform as we approach month-end, when rebalancing pressure is likely to be greatest.

The first step is simple. Pull daily data for SPY and TLT going back as far as you can get it (I used data from 2007). You can get this from Yahoo Finance – nothing fancy.

Then ask a straightforward question: If I know which asset outperformed during the first 15 trading days of the month, can I predict which will outperform during the last ~7 trading days?

Why 15 days? Because it’s roughly two-thirds of a trading month, and it gives us a reasonable window to identify the outperformer before month-end rebalancing kicks in.

Could you use 10 days? 20 days? Sure. But 15 seemed reasonable and shouldn’t really matter much. If it did, then that would be a big red flag. We want stuff that’s fairly robust to the actual implementation details.

Back in my floor days my biz partner was incubating a futures trend strategy and he’d have me look at the backtest results. I’m no scientist, but I knew enough to realize that if the signal depended on a particular value of the parameter (ie the exact amount of what defined a “breakout”, the number of lookback days, etc), then the result was overfit.

It’s the same idea as David’s sauna therapy study.

When you are in a competitive domain where many people are constantly mining, “too good to be true” discoveries should be met with extra skepticism.

A current example of this is the so-called Mississippi Miracle in which both the left and right appear to have an axe regarding the childhood literacy improvement in Mississippi schools. It checks the box of “domain where many people are constantly mining” so interventions that show huge returns deserve a lot of skepticism. You can count on Freddie deBoer to deliver that, but I think the pushback in the comments section of his post show the complexity:

There Are No Miracles in Education

Moontower #291

Friends,

The phrase “studies show…” is almost always followed by bs. To understand why, I’ll point you to a post by David Epstein.

David is the author of Range, The Sports Gene, and the new book Range Adapted for Young Readers which I bought for my 12-year-old son and nephew.

I’ve been a long-time reader of David’s letter and this post is both useful and timeless.

Everything in Your Fridge Causes and Prevents Cancer

It’s a reminder that outlier studies and results in general make headlines, but are statistically inevitable if you do enough studies.

Excerpt:

That should raise a caution flag in your head.

I’ll take a simple example from Kris Longmore’s article explaining how month-end rebalance trades work. The post is titled How Wealth Managers Pay You To Trade. He writes (emphasis mine):

How I’d Test This

So here’s the hypothesis: if we can identify which asset outperformed during the first part of the month, the underperformer should outperform as we approach month-end, when rebalancing pressure is likely to be greatest.

The first step is simple. Pull daily data for SPY and TLT going back as far as you can get it (I used data from 2007). You can get this from Yahoo Finance – nothing fancy.

Then ask a straightforward question: If I know which asset outperformed during the first 15 trading days of the month, can I predict which will outperform during the last ~7 trading days?

Why 15 days? Because it’s roughly two-thirds of a trading month, and it gives us a reasonable window to identify the outperformer before month-end rebalancing kicks in.

Could you use 10 days? 20 days? Sure. But 15 seemed reasonable and shouldn’t really matter much. If it did, then that would be a big red flag. We want stuff that’s fairly robust to the actual implementation details.

It’s the same idea as David’s sauna therapy study.

When you are in a competitive domain where many people are constantly mining, “too good to be true” discoveries should be met with extra skepticism.

There Are No Miracles in Education

Would be interesting if there was a prediction market on how much literacy scores would improve in places that decide to adopt the Mississippi approach?

Which brings us to this week’s Money Angle, which should get a rise out of you…

Money Angle

Prediction markets are all the rage. They even played a main character role in an episode of South Park just a few weeks ago with Kalshi being specifically shouted:

On Friday I shared a rare interview with SIG founder Jeff Yass that came out this week about prediction markets:

Spooky? Jeff Yass on Prediction Markets

Kris Abdelmessih

Oct 31

Read full story

On the subject of prediction markets, long-time Moontower sub Andrew Courtney has launched a substack with many of his recent topics being analysis of prediction markets. His thought processes look familiar because…well, Andrew retired quite young being an extremely successful SIG trader himself.

You can get started with these posts:

🔗from the Kalshinomics Lab: conditional election probabilities

🔗are you good or just up?

🔗Relearning Math at 38 — Andrew was at the top of the Math Academy leaderboard for a bit which iirc corresponds to learning math with the same time commitment as a full-time job. My kids and I have looked at the top of the list thinking “who the heck are these people?!”. I was envisioning autistic homeschooled kid not retired SIG trader.

Finally, this is also Andrew’s site:

Kalshinomics

If you are in the Philly area, he’s done meetups for prediction market enthusiasts.

Fun fact: I told Andrew I was going to boost his awesome letter this week and I asked him to make me a market on how many subs it would lead to.

He gave me a 90% confidence interval which I thought was a good market although too wide to trade on. I showed him a 175 bid if he wants to hedge his happiness. We’ll see what happens.

Good handicapping practice would be to try to list the info you’d like to have in making such a market!

Money Angle For Masochists

The “Masochists” header word this week is a pointer to “aspiring traders”.

I’m going to reprint Joel Rubano’s tweet in full. Joel is a friend, energy trader, author and entrepreneur running a corporate trading education company with a focus on commodity trading and hedging.

His book: Trader Construction Kit

The tweet pairs well with the post from last week’s so you’re interested in trading.

Joel:

I had the opportunity to guest lecture to a university class yesterday and got some questions about resources for students interested in working toward a trading seat.

The good news is that there are massively more and better resources available now than ever before. The bad news is that for every useful book, class, or podcast, there are 999 more that are worthless at best and massive value destructors at worst.

A few hints to help sort the wheat from the chaff:

Anything that tells you trading is easy is lying to you. Trading is a brutally hard game played against literally the smartest, most disciplined, most aggressive people in the world. The people who survive and thrive tend to welcome that specific challenge, even though most would not describe their time on the desk as “fun.”

Anything that claims a risk-free or can’t-lose strategy is garbage. Most professional traders are hoping to be right 50–55% of the time and relying on extremely strict discipline and risk management to be profitable with that hit rate. They also have to manage capital so they can survive stretches of worse-than-normal performance, which invariably happen.

Anything that tries to sell you trading as a lifestyle — the cars, the watches, the vacations — is almost certainly a scam. Real traders are not sitting there thinking about what the money buys in real time; that’s distracting and leads to bad decisions. There’s even a famous passage in Reminiscences of a Stock Operator about a group of traders who all try to make enough money to buy a fur coat, and they all fail because they were focused on the coat instead of on playing the game well.

Anything that says “anyone can do it” ignores how markets actually work. Most markets are zero-sum: people have to lose for other people to win. The softer version — that anyone can become a trader if they just grind — is also not really true. The job demands unusually high levels of discipline, curiosity, intellectual honesty, and competitiveness. Some people have those traits and can develop into professional traders; most people don’t, and that’s fine. The good news is that there are lots of trading-adjacent roles (risk, research, sales, tech, execution, ops, product) that let you work on markets, think about markets, and have a productive, interesting career without being the person taking risk.

Anyone promising something “just like what the pros use” or “better than the professionals” does not understand what professionals actually have. Elite hedge funds, banks, and merchant trading firms spend huge amounts of money on proprietary tools, data, infrastructure, and staffing to compete in an intellectual arms race. A single trader can easily consume hundreds of thousands to millions of dollars’ worth of technology and information resources per year, which is one reason their profit targets are so high. You are not getting that for $29.99 a month.

Anything that claims “the edge is AI” with no further detail is almost certainly not going to outperform anything. Yes, serious trading firms are racing to integrate AI, and yes, AI will be useful for specific tasks. But AI is very good at some things and still not very good at others. If someone is just putting a thin interface on top of a generic stock-picking model and calling that “AI-driven alpha,” it’s not only unlikely to be useful — even if it does work for a bit, it will almost certainly get out-competed by more specialized, internally developed tooling at a bank or hedge fund.

Ultimately, if you’re serious and you’re early, your main job is not to find a shortcut; it’s to build the traits that compound: discipline, honesty with yourself, curiosity, and competitiveness under stress.

Stay groovy

☮️

Moontower Weekly Recap

Posts:

the messiness of options in the real-world

Recently, I’ve been writing a lot about option funding — implied rates, reversal/conversions, and financing stock positions, and even a touch on early exercise. The posts from earliest to most recent:

You can’t really overstate how important this is, especially to pros.

On the same theme, Stanford lecturer and HF manager Kevin Mak published an outstanding and detailed post:

Holding a high cost to borrow stock? Here’s how to collect the borrow fees using the options market (link)

I want to quote a few key parts:

I cannot stress enough that if you want to be competitive in capital markets, you cannot afford to make these types of mistakes and forgo this return.

Kevin is quite blunt:

If you think this is too complicated to follow, to be blunt, you likely do not have the mental fortitude to have alpha in markets (you may still make money via luck). If you CAN figure this out, but couldn’t be bothered to spend time on it, that’s probably fine, but I suggest staying away from holding a stock with a high cost to borrow. If you insist on holding high cost to borrow stocks, and “not worry” about collecting the free borrow/lending fees, you really should be playing triple-zero roulette, or splitting every pair of 10’s at Caesar’s Palace instead because that would have a lower edge, and be way more fun.

I suggest that by using options to refinance your position, you “inherit the market maker’s funding rates” which are almost certainly better than yours, whether you are long or short.

This is Kevin’s way of saying this which might land better for some (emphasis mine):

If this is arbitrage, it shouldn’t exist right? This unique situation is you “arbitraging” your own holdings. You’re basically holding an inefficient asset since you can’t lend it (or collect the lending fees) so this lets you own it in a more efficient manner. Nobody is going to compete away this arbitrage because nobody can access your holdings except you. In fact, it’s the arbitrage happening in the open markets which is pushing the value of synthetic long to be equal to the long stock (and collect borrow) position.

Kevin wisely tucks all the brain damage into the endnotes to not ruin the flow and central message of the post. I love his intro to them:

This is a beginner treatise on a synthetic long position and covers ~98% of what you need to know about it. The last 2% I could write 25,000 words about and not be finished.

In the spirit of the endnotes, I want to share a recent real-world example of a scenario that resides in that annoying “2%”

Alex, a mutual of mine on X, asked the following:

I bought some deep itm $AIV calls, I think Jan exp, a couple weeks ago because I thought they were cheap vs my financing cost. Not much extrinsic value… Underlying had silly spike after hours, up then down.

Was the right way to trade this, to short the underlying after hours? I thought about exercising to sell, but didn’t want to burn a few months of time value for nothing, even if I didn’t really pay for it.

I’m going to step through the conversation, injecting meta-commentary to bring it down a level.

Me: Your trade expression was because of financing but you are asking about what you should have done with your delta. I’m not being pushy, I’m just trying to help you answer your own question.

There are 2 things I can see so far:

Alex is conflating his strategic financing decision with tactical delta management. I do the Socrates thing as a habit when learners entangle multiple issues into a single question. If I just jump ahead and answer each part of the implied questions, I rob Alex of a valuable opportunity — to debug his own thinking by breaking the big question into its components. I notice many people getting option or trading concepts rolled into a ball of yarn, which tells me they don’t understand each string as well as they should. When you’re forced to decompose your own question, the deficiency is self-apparent.
He’s showing good instincts about not wanting to exercise the calls because he’d be “burning a few months of time value.”

💡Option Pricing Clarifications

The first [but not final] check on whether you should exercise a call is if the value of the OTM option on the strike is worth more or less than the cost of carry.
1. For an ITM call, is the dividend you are exercising for worth more than the put on the same strike?
2. For an ITM put, is the interest on the stock short if you exercise greater than the call on the same strike?
Alex said he thought about “exercising to sell,” but to be clear, you can’t exercise the calls after-hours and have it settle the next day. There is a cut-off for exercise notices. He would have needed to short the stock after hours, then exercise the next day, meaning he would have to carry a short for 1 day. This tactic would have flattened his delta, but he would have to incur any financing ramifications for the 1-day short.

This simple scenario highlights a risk to using heavily-discounted synthetic futures to express a long:

If you plan on liquidating the length before expiration, you are exposing yourself to the change in funding that you otherwise locked in.

In this case, Alex is exposed to the borrow he may have to pay on short shares if he chooses that route to cut length. One day’s borrow, even if steep, is unlikely to make or break the whole trade, but the bigger issue is the “locate”. There might not be any shares to borrow from your broker. If Alex was long the stock instead of the synthetic stock, he could just sell it in the after-market.

If Alex chooses to cut length by selling the ITM call, it may be for the same discount that attracted him to buying them in the first place. It’s like buying a home for a discount because it has lots of street noise. It’s a problem you can’t outrun because you will be on the losing end of the discount when you sell the house.*

Stay groovy

☮️

*Tangential, but I see people misunderstand this in the private school vs public school debate. If you pay up to live in a good school district, you are exposed to 2 risks:

The carry cost on the premium. If you pay 20% more for a good school district, the funding cost on the extra ~20% of mortgage is the cost. If schools are the deciding factor between an $800k or $1mm house and mortgage rates are 5% then your pre-tax cost of the better schools is ~$10k per year. This is probably much less than private school tuition, especially if you have multiple kids.
The perception of school quality. The “school premium” can expand or contract over time. You can win or lose to this when you sell.

In general, the true cost of the premium you pay for good school districts is very small relative to private school tuition, making your VORP hurdle for private school choice high (although worth it for many people).