Moontower #254

Friends,

It’s been a heavy week. I wrote more up top here so Money Angle is just one section today.

First, here’s one small thing you can do to not feel helpless.

https://x.com/khemaridh/status/1878140050778870176

Khe lives in LA (not in the fire regions) and our families both have seen too many pics from friends in affected areas.

Living in the Bay Area you hear your share of fire anecdotes ranging from the Oakland Hills fires in the early 90s to the fires in recent years north of SF. A friend of mine was a top official in Contra Costa County Fire and I’ve listened to some harrowing stories including a time he called his wife from a fire battle on a local mountain with the “I want you to know I love you and the kids”. CA firefighters are a badass breed knowing full well what they’ve signed up for.

There’s a lot of finger-pointing already and the fires aren’t even close to being contained. Getting to the bottom of the pile to find the ball when this is over (sorry to even minimize the current moment with a “when this is over”) is gonna be an ugly mess of scratching, eye-gouging, and nut punches.

If you live here with your eyes open you can see how CA is a devil’s bargain. When I sold my house in 2020 it was under a red sky. PG&E was in the news. Large chunks of people’s net worths being uninsurable just didn’t strike me as sustainable. I was wrong because all home prices did was go up since then.

[Contradicting that thought is my simultaneous hunch that a major natural disaster would super spike the prices of remaining homes when the demand to build devours supply. I thought a widespread disaster in the congested area would have come from a faultline.]

Nothing’s changed. Insurance math ain’t gonna math unless the kindling is cleared. This is a good thread on some ground truth. It’s comforting because physically there are possible solutions. Realistically, it’s TBD. To be doubted.

California’s troubles run generations deep.

1988’s Prop 103 kneecapped the insurance market’s ability to find clearing price for the risk. So the risk is socialized. And when risk is socialized it’s there’s less incentive for its true owners to internalize it. If “crabcakes and football is what Maryland does”, California is sunny weather and price distortions.

Prop 13, enacted the year I was born basically granted landowners a call option on inflation while wage earners were left short convexity via state income taxes required to feed the beast effectively subsidizing landlords. This is not “unintended consequences.” These were obvious second-order effects. Great job all around, direct democracy.

[A side note: I’m not unsympathetic to seniors who don’t want to be displaced because of property taxes but there can be targeted programs for freezing taxes beyond a certain age. But even absent that, you have to look at the problem holistically and in the counterfactual. If you don’t have Prop 13 distorting the cost of living here, people would be forced to plan better. Ya know, like they do in other states. I don’t know enough about what I’m about to say but my guess is the closer the tax receipts are to where people actually live the better aligned elected officials are to the constituents. That compliance usually prohibits hedge fund employees from donating to local officials but not national candidates affirms that intuition. In compliance’s case, the stronger influence is a bad look. In general governance though, it’s better.]

In any case, it’s hard to focus on what happens once the patient is no longer in shock. It’s easier to just fall into the loop…what would you do if you lost your home, your kids lost their school, maybe you didn’t have insurance?

Even if you have insurance it must feel overwhelming. I was chatting with someone yesterday about how 8 years after the Sonoma fires there is so much that is not rebuilt. And that’s not even an urban area.

What if your house is still standing but your neighbors’ houses are gone? At that point you might want to start over somewhere else but your put option didn’t pay out. It makes me think of this dark bit of humor. “My family!!!!”

5 years ago we were, without knowing it, heading to a lost year or more. The fire victims are staring down a similar barrel. The New Year’s resolutions scribbled a week earlier. Literally gone. What’s the difference, no time for that now. Got a full plate for the next million hours just to get back to stable.

[As a matter of commiseration it’s not helpful to relate a story back to yourself. I have no idea how these people really feel. But I’ll share that my apartment burned down in college. Thankfully a roommate noticed the smell and quickly got us to knock on doors…realizing that nobody knew where the smell was coming from we evacuated across the street not knowing what was going on. Until I saw the flames burst out of my bedroom window on the second floor. The fire was in the walls, starting from the take-out joint’s oven below us.

We were fortunate because both the university and Red Cross were able to quickly get us cash and concessions on due dates. And most importantly a friend housed and fed a bunch of us for days as we tried to secure apartment leases elsewhere. Despite the experience, I can’t fathom what it’s like to go through anything like this with your own home and when it happens to everyone around you. But I suspect even small gestures will go a long way.]

I can’t find the link now but someone who lost their house in Hurricane Milton wrote a good thread about how confusing it is. It can take a while to be allowed back to your site (I still remember the day we went back to the building and it does stay with you. And again that was small potatoes). The person explained how you collect little bits of info from everyone as nobody can see the whole picture.

Embodying what it must be like every day for the foreseeable future will hopefully focus the rest of us on being useful right now. But yeah, we are going to need to learn from it.

I hate to say it, but I’m not optimistic.

I don’t wanna be a drag every week in this letter so maybe I just get it all out now so I can get back to sharing educational things.

An unpleasant musing

The “algorithm” knows you can’t hate someone so vastly different from yourself.

Our deepest hate is reserved for those similar enough to remind us of ourselves. You find the Taliban, serial killers, and cartel bosses disgusting but they are too far to hate. They didn’t betray you. You were never on the same team.

If 9/11 were to happen today, would we unite?

Not if the algorithm is in charge.

This leads to a haunting question: is Twitter/X or whatever social media you look at real life? The line feels blurry even when I’m “touching grass” with people in meatspace. I’m increasingly noticing convergence between socials. Which makes sense. If the meme works there, copy it here. (Related: playing video games with my son has made me more aware of Twitterisms that are really gamerisms because there’s no way he would know them otherwise). The “memes” start as compressed thumbnails of an idea until they gradually terraform its host’s personality.

Luigi as a vigilante is a meme. That arsons might plausibly be flaming the original fire feels like it’s coming from a similar place. The Joker is here. I’m not here to point fingers because as wrong as the Joker is, as unjustified as his actions are they start with pain. Explaining the source of pain will never justify the evil it produces, but pretending it’s gonna just go away is begging for the spiral to accelerate. And we are so far from addressing that pain.

Hey, but I hear SPX earnings are expected to grow 15% this year.

Is Twitter real life? Elon says it is. He says it’s the media now. The truth. Ok, do you know what the “truth” is telling us? It’s telling us you have two years to get rich because capital, not labor, will be all that matters. That soon enough as quickly as you can imagine it an AI army will build it. You just need enough capital to pay the electric bill.

For people who still put gas in their car, this sounds like a death sentence. And if you shrug and say, “Well, it’s the truth,” then okay—Jokers everywhere. Don’t be shocked.

Positional Scarcity and the Human Condition

The accelerationist dream of post-scarcity is a fantasy. It sells itself on utopian visions without addressing the reality of positional scarcity. Status, sorting, and competition are zero-sum. They don’t vanish, even in a world of abundance. These instincts are baked into the human condition.

Nature reminds us of these instincts. My kids have been home sick much of the past week as the plague gets traded in our house like Pokemon cards (this is also weighing on my mood. Sick kids are not themselves and it’s sad, pathetic sight). TV has been on a lot. We were watching a nature vid of male humpback whales chasing a female trying to protect her calf. The males all wanted to do the “humpty dance,” but the mom wasn’t interested in Irish twins. The eventual victor was the male who escorted her to safer waters, perhaps hoping she’d “repay” the favor. The narration projected human motives onto the whales, and as awkward as that felt, it refreshed my sense that our capacity to trade, to make mutually beneficial exchanges, is what holds us together.

To situate this capacity truthfully we acknowledge the tense contexts. There’s social Darwinism. Life is competition. As I alluded to earlier, this remains even if we solve hunger. But we also cooperate at least at the tribal level. “Tribe” encompasses many scopes. “America” itself is a tribe that contains many tribes. And one tribe that encompasses even “America” is civility. A tribe that believes in inherent human dignity. It’s an ideological tribe. It’s from that tribe that we sense the trade-off between equity and efficiency. Efficiency means it’s important that our surgeons have proven their steady hands and sound minds. Equity means we don’t throw handicapped babies in Lake Tahoe. These are the outer boundaries of the tribe’s beliefs. Within the boundaries, “reasonable people can disagree”.

What’s happening right now is confusion about the boundaries and whether being part of the civility tribe is even desirable. The tribe is very large. Almost everyone starts within it. But the Joker is outside it by choice. Because of the betrayal of either boundaries or usefulness.

This is colliding with another human impulse.

Just Because

Humans do bizarre things. Flip through the Guinness Book of World Records, and you’ll see countless examples of this: we set records “just because.” Why did you climb the mountain? Because it’s there. Why do you want to go to the moon? Or Mars? Why not?

There are instrumental answers to these questions. Justifications or rationalizations depending on your own leaning. But they are beside the point. It’s gonna happen because humans gonna human.

Injecting some chaos and mutation into an evolutionary system is natural. If we survive it, it will turn out to have been species-level adaptive. We stand on the cusp of great acceleration. People trying to literally cure death. The stakes feel massive. Like humanity forking. The rhetoric sounds like something someone committed to the tribe of civility would say. We “need” to do this.

But today and increasing so…

…when the speaker says “we” the listener doesn’t trust the “we” includes them.

At least not a version of them that is dignified.

The lack of trust doesn’t come because the speaker is evil. It’s because we know from experience that humans will do things just because they can. Steve-O is the kinda guy that would have been a Jackass for free. Curiosity drives us forward. But poison is the dose. Morbid curiosity is what we call an overdose.

Society is looking at a group of people at a party and thinking, “Should you be taking that much?” Some dude in the corner funneling beer is egging them on and the normies who have to wake up early to go to work say “You know it’s getting late, I’ve had a bit to drink and if I wait any longer I won’t be able to catch an Uber?”

Huh?

Bruh, don’t you know that Waymo never sleeps?

A thought that didn’t fit

“Just because” isn’t always “just because”. Doing anything unusual has an algorithmic incentive. My kids watch YouTube basketball channels constantly. I saw an episode where they were record-book maxxing. Just trying to set records for various things like “dunks in a minute”. These videos get tons of views, but I find something deeply unsettling about them.

Making money is a reasonable proxy for “created value”. But you’d have to be pretty far up your own rear to think that there aren’t people who have gotten rich destroying value via deception, excessive rent-seeking, or simply getting away with crimes.

But there’s a wide swath of activity (think legal grifts) that always has at least a few redeeming stories that end up whitewashing the whole enterprise in the eyes of its few satisfied customers. (The rest of the customers churn because grifts are usually short-sighted, constantly hungry for new marks).

And then you have things stuff like Mr. Beast videos.

I just don’t know. It feels really dark in the same way I couldn’t watch more than one episode of Squid Games.

Sure, people point out all the good he does, but here’s my problem: attention is zero-sum. Every minute watching him is at the expense of another entertainer who will recycle that income through the economy or charity. At a macro level, there’s no additional good being done here. It’s just more noticeable when it’s a single ticket.

Maybe this is my “old man yells at clouds” moment. There’s always been nonsense content. But when I see these videos, I feel like I’m staring into Palpatine’s horrific face: “Oh I’m afraid the deflector shields will be quite operational when your friends arrive.”

We’re entertaining ourselves to death.

Related reading

Money Angle

On Friday I tried a little experiment.

I opened a voice channel in our Discord called The Moontower Subscriber Hoot.

[Back in my floor days it was common for traders on the floor wear a headset to be on continuous conference with the traders streaming quotes upstairs in the office. At the fund, we also had an open conference line on our turrets to be able to talk to other traders on the team. I don’t know the origin of the term in the industry, but these conferences are called “hoots”.]

I wasn’t writing or working on something that needed deep attention so I figured why not open the hoot and chat while I tried to sell some TLT puts. [I didn’t get filled because I was cheeky with a limit a penny above the bid.]

I narrated what I was doing which I though could be a nice “over-the-shoulder” way to explain what I’m thinking as I do it plus enjoy the banter, something I miss from the old job.

Regarding TLT, I decided to toe into some bond delta with TLT being down more more than 2 st devs over the past month, more than any other liquid name in the Cockpit view.

I decided to express the delta through short vol as TLT vols in the belly of the term structure look attractive enough to sell on both our DASHBOARD and REAL view.

I chose a 35d put…skew is a bit elevated and the strike vol on the options were up a lot that morning.

ATM vol relationship to realized vol history

We talked about several other trades on the hoot and I got to explain a juicy rev/con trade that turned out to not be a real opportunity but people came away with a better understanding of how synthetic futures work!

In narration, the group was able to see how I use greeks to make sense of what’s going on in real-time. Makes a topic that seems abstract super practical and useful. For example, if I sell .30d puts at $1.85 vs stock at $65 and buy them back at $1.88 when the stock drops to $64.50 why that’s a big winner if I traded them delta-neutral.

You can also use greeks to discuss vol changes from tick to tick.

“See how the option is offered at $1.80 again 20 minutes later even though stock is a dime lower? The option only has a penny of theta, so it wasn’t erosion. It has a .30 delta and .15 vega so that means IV is down .2 clicks”

Overall a great experiment that I’ll make a trend. Not committing to a schedule but when I host them it will be on a Thursday and/or Friday.

A few weeks ago Nick Pardini had me on his podcast Analyzing Finance.

I know Nick from his days as a researcher for Parallax, we sat a few desks from one another.

It was fun to catch up. We cover options, volatility, and how options theory principles are found in all kinds of life or business decisions. The options stuff is really perfect for people trying to learn, it’s not heavy, and touches on a lot of practical questions around when you should consider using them or not and why volatility behaves the way it does.

Stay Groovy

☮️

you can ONLY eat risk-adjusted returns

Last month my friend Khe published a letter pointing to Money With Katie’s adjustment to the 4% rule for avoiding “lifestyle creep”. The 4% thing is a rule-of-thumb for spending so you don’t outlive your assets in retirement.

My Rule for Avoiding Lifestyle Creep: Don’t Live Beyond Your Assets (2 min read)

Katie’s adjustment is a simple formula that marries both assets and income to come up with a spending rule that balances the desire to spend more as you make more while still saving enough for your future.

The formula = (4% of net worth + post-tax income) / 2

I think of rule-of-thumb like this as an API to a complex code base known as “retirement finance”. Nobel Prize winner William Sharpe said knowing how much to save and spend for retirement is “the nastiest, hardest problem” in finance.

That 4% rule abstracts away the interaction between investment returns, inflation rates, longevity, and taxes because, well because, we can’t let ourselves be paralyzed by this problem before brushing our teeth every morning.

But that 4% rule and any other “rule” is obviously a guideline sitting on assumptions with generation long lookback windows. And as we learned in Sunday’s letter, we don’t have a lot of samples of generation-sized windows to generate any confidence-inspiring inferences.

The entire output of the personal finance industry on the topic of investment history is a streetlight problem. But forgivable. Remember “hardest, nastiest problem” and we still gotta find the toothpaste.

Despite a career in options trading, replete with greek letters and complex financial instruments, I find myself in total agreement with Corey Hoffstein’s recent podcast guest Victor Haghani who despite being a super-quant (sometimes super means flying to close to the sun as he was also a partner in LTCM) admit that when he left high finance he realized he didn’t know how to invest.

From Last of the Tactical Allocators:

After LTCM, I woke up, and it wasn’t a dream — I realized that I needed to focus on managing my family savings. Up until that time, I had worked at an investment bank that took a lot of my compensation and put it into the stock of the company, Solomon. At LTCM, it was pretty natural to invest a lot of my savings in our fund that we managed.

It was a shocking realization to see that I had been working in finance for about 17 years, alongside some really brilliant minds — practitioners and academics — yet I had never really thought much about investing for myself. All of my focus had been on research, to begin with, and then proprietary trading.

[Kris: I wrote a post my own wake-up call: My Investing Shame Is Your Gain]

My situation was typical: you come out of college, go to Wall Street, and get trained in everything related to Wall Street. Unless you’re going into private wealth, which Solomon didn’t even have at the time, you don’t get trained at all in personal finance. So, there I was, looking around to see what my friends and respected colleagues were doing. Everyone was following the Yale Endowment Model that David Swensen had made so popular. Yale’s returns were incredibly attractive.

I knew people in private equity, hedge funds, venture capital, and distressed investments, so I started investing as though I were a one-man Yale Endowment. Meanwhile, I was on sabbatical from working, spending time with my young kids. After four or five years, I had this realization that what I was doing made no sense — from a life perspective or a risk-and-fee-paying perspective.

The final straw was when I sat down with my accountant, David, to review my tax return for one of those years. I asked him, David, why am I paying all this tax? I haven’t had that much income this year. He explained, Well, you have this income here as short-term capital gains, and you have these expenses over here that you can’t deduct because they’re miscellaneous itemized deductions.

It was like an epiphany: Geez, what am I doing? I realized I had to go back to basics.

A category of basics that I think are fundamental to investing is what I broadly call return math. It matters because investing is really a nesting of many re-investments. And compounding is the realm of multiplication. While it’s true that multiplication is just addition on ‘roids, a failure to understand it can mean the difference between the prefix “ste-” or “hemor-”.

We’ll go to Victor for some education.

Eating risk-adjusted returns

Corey, playing devil’s advocate, confronts Victor with a common charge leveled against quants:

“you can’t eat sharp ratios”

Victor: Risk-adjusted returns are the ultimate thing that we care about I think investors should be trying to maximize risk-adjusted returns. And what is a risk-adjusted return? Well, a risk-adjusted return is the return that you expect to get or that you did get minus a cost for risk. And the cost for risk comes from the fact that typically we have a decreasing marginal utility of wealth or consumption that makes us risk-averse.

We could write down a formula in an idealized world for what risk-adjusted return is, but let’s just think about what it is qualitatively. I mean qualitatively what it is is that I come to you and you have your optimal portfolio of equities and safe assets and whatever, and I say to you all right, that’s it, I’m going to take away that portfolio from you and I’m going to give you in its place a 100% safe portfolio. But you can’t have the portfolio that you have right now that has all these risky assets in it. What is the lowest return that you would accept on a totally safe portfolio so that you would be not happier or less happy than you were with this risky portfolio that had this positive extra expected return?

Well, when you answer that question you’ve just answered the question of what is the risk-adjusted return on your portfolio. The risk-adjusted return could also be termed the certainty equivalent return of your portfolio. It’s basically what would be the 100% safe annuity that you could turn your wealth into without taking any more market risk. That is what you eat. That’s what you’re going to spend on your food. That’s the only thing that you have to eat — that’s the annuitized risk-adjusted stream of consumption that your wealth will support. And so that is what you eat with the appropriate inflation adjustment.

[Kris: There’s a question bandied around Twitter every now and then cutting to the heart of this — what would the TIPs yield need to be for you to plow all your savings into it and not concern yourself with investing anymore? In this interview, Corey and Victor frequently speak in terms of real returns and what sticks out to me is how much higher people think equity real returns are above TIPs but in reality that number over long periods is ~ 3% give or take 2%, maybe 3%. If the TIPs yield were 4% you could really live by the 4% rule without worrying. Except for taxes of course. But if we are going to talk about taxes, that’s the muscle movement that makes any realistic form of alpha look like 10lb dumbbell curls as far as impact. Reclassifying your entire income to a friendlier tax code is a better use of time than trying to outsmart markets. Unless your answer to a calendar with no meetings is afternoon delight with the solar credits section of the IRS code.]

Sharp ratio is not what you eat. Sharp ratio is we’re looking for portfolios that have the highest sharp ratio, but we’re not trying to maximize the sharp ratio of our portfolios. We’re trying to maximize the risk-adjusted return of our portfolio, and there’s going to be all kinds of cases where you’re going to prefer a lower sharp ratio portfolio to a higher one depending on what your constraints are and other things. But it’s the risk-adjusted return that you’re eating. You’re not eating the sharp ratio, and nobody’s claiming that you’re eating sharp ratio.

[Kris: In this next part, Victor doesn’t use the words “arithmetic” and “geometric”. That’s ok. He’s a gentleman, you buy someone a drink before you go there.]

What you’re not eating is you’re not eating expected return. If you start eating expected return, bad things happen. Let’s take a toy example where somebody tries to eat their expected return. Say you have your wealth, you’re retiring, and you look at your portfolio. You construct this portfolio, and you believe that this portfolio has a 5% real return after cost of living adjustments. So, you got this portfolio. The only problem is that you had to build this portfolio with a bunch of risky things, unfortunately, because maybe you were a pharmaceutical person. So, you’ve built it with different pharmaceutical companies, and this is a pretty risky portfolio, but it has a 5% average annual real return.

And let’s just say the volatility of this thing is much higher than the market volatility, that it’s got 30% annual volatility. You got $10 million, and you say, “Oh, well, the expected annual return of this portfolio is $500,000 a year. I’m going to spend $400,000 a year adjusted for inflation for the rest of my life, and I should be fine because my portfolio has a 5% average annual return. So, I’m just going to spend $400,000 — you know, that’s the 4% rule — I’m going to spend $400,000 adjusted for cost of living for the rest of my life, and everything should be fine.”

What’s your most likely amount of wealth in 25 years? Your most likely amount of wealth in 25 years is zero. You’re going to be wiped out. Why are you going to be wiped out? Because the up 30, down 30 is killing you. The first year you went up 35%, so you went up to $13.5 million. You spent $400,000. Beautiful. It’s all great. But the next year you went down 25%. You went down to 30%, but there’s the 5% expected return. The next year you go down 25%. Uh-oh. Now after you spent the $400,000, you have less wealth than you started off with. It’s volatility drag, and that volatility drag means that your median portfolio has gone to zero before 25 years. Once it starts going down, it really starts going down fast.

That’s what happens when you eat expected return. So, you have to eat risk-adjusted return. If you eat your expected return, it doesn’t end well. Maybe that’s where we get all these missing billionaires from — they were eating expected return. People get rich, and they think the expected returns are high, and then they try to eat a fraction of the expected return, but they’re not eating risk-adjusted return.

I will leave you related ideas to chew on.

Dr. Philip Maymin was recently interviewed on the CFA Institute podcast. You might recognize his name because he’s the author of book I constantly recommend, Financial Hacking.

About Philip:

Dr. Philip Maymin is Portfolio Manager and Director of Asset Allocation Strategies at Janus Henderson. He is also the Endowed Schramm Chair of Analytics and the MSBA Program Director at Fairfield Dolan, the CTO for Swipe.bet, and an instructor at Analytics.Bet.

In the past, he has been a portfolio manager at Long-Term Capital Management, Ellington Management Group, and his own hedge fund. He was Assistant Professor of Finance and Risk Engineering at the NYU School of Engineering, as well as an analytics consultant with several NBA teams and the Chief Analytics Officer for Vantage Sports.

Maymin co-founded the journals Algorithmic Finance and the Journal of Sports Analytics. Additionally, he was a policy scholar for a free market think tank, a Justice of the Peace, a Congressional candidate, and an award-winning journalist.

I recommend listening to it for several threads.

There’s a lot about AI. He’s very much in the weeds, so much so that it’s the topic of his next book.

Next, there’s some nice reinforcement of some of Victor’s ideas (maybe not an accident, Maymin was also at LTCM). Both Victor and Philip talk about dynamic vs static allocations. Victor’s firm helps you dynamically size your portfolio according to your risk own risk function as well as expected return (I call it “Kelly aware”). Phillip emphasizes tail risk management (not in a financial product sense necessarily, he’s speaking generally) because like many things in life, it’s a few moments that have most of the impact or if Wu-Tang Financial went quant — ”power law rules everything around me”.

For Maymin, the focus should be on risk management since the forces of competition make it hard to win big on alpha (alpha being defined as capturing excessive return without paying the risk cost) but those same forces do not keep not inhibit you from avoiding disaster which is a nice asymmetry for the individual.

That conclusion flows easily from his articulation of efficient markets hypothesis. His coverage of that idea, what it actually means, and its copious shortcomings are the best I’ve heard. I also remember him covering it in his book to a poetic degree and extending well beyond markets iirc.

Postscripts

  • Philip suggests de-risking during higher vol periods because if you don’t then those periods will be a much higher proportion of your performance than the length of time that coincided with those periods which is another way of saying that a fixed position size is bigger part of a risk budget when times are volatile. That’s fairly obvious but the open question I have is whether higher volatility periods also coincide with higher returns. I presume they do in arithmetic terms but not geometric which is what matters so my unverified take is you want to be smaller when vol is high even if the expected returns are higher. My own investing process doesn’t switch gears hard with the vol level so this is an area I need to do some work on.
  • I thought about including ideas from Mason Malmuth’s book Gambling Theory and Other Topics, because it emphasize that the most important concept in gambling is to follow a “non-self-weighting” strategy. Which is another way to say “vary your size with the edge”. Such an observation would be banal to this audience but he points to several common strategies that are counterexamples that people generally approve of. He’s a pretty incendiary character, arguing against diversification (he admits this is the largest area of pushback he receives) and claiming that “money management” is a stupid if not moot topic. He also gives the example of nuclear MAD as a self-weighting strategy and the brevity of the Gettysburg address as a non-self-weighting strategy. I gotta admit, reading it again 25 years later, I feel the dude’s a bit of crank. It was required reading at SIG but that must be in spite of the diversification thing. Jeff Yass has repeatedly emphasized that diversification is a free lunch.

Inoculate yourself against “persuasive” charts

The original moontower blog is https://moontowermeta.com/. The “meta” is an important word. Important enough that Facebook stole my language and turned it into their ticker. F’n Zuck. Leave some for the little people bruh.

The reason I used “meta” (besides the fact that moontower.com wasn’t available) was because a lot of things I think about are fairly meta. Knowledge is the object but how we acquire knowledge is the meta. Trading is a very meta discipline because games with counterparties require a solid “theory of mind”.

In the spirit of meta, I really enjoyed a recent post by Robot James titled:

Valuation Timing with Excel (6 min read)

It’s meta because it’s really about arming yourself with data analysis to confront a narrative or chart. It’s worth stepping through the article together to appreciate just how many meta-nuggets it contains.

First, we start with an object-level observation that you’ve likely encountered. I’ll quote freely from the post but all bold is mine:

You may have seen a lot of charts like this recently:

The conclusions people tend to draw from this chart are:

  • there is an obvious and strong relationship between valuation and expected future returns (cheap = good, expensive = bad)
  • valuation estimates are currently historically high; therefore, expected returns of the S&P 500 are historically low.

We should always be wary of drawing strong conclusions from stuff people share on the internet or in sell-side research.

There are a few reasons to be skeptical of the strong conclusions people tend to make on seeing this:

  • the chart might just be wrong (people screw up financial data analysis all the time)
  • 10 years is a really long time horizon
  • all of the 10-year total returns are actually positive
  • why are there so many points? How many 10-year periods has the index even existed for?!

The good news is that, with a few simple skills, we don’t have to believe what randos on the internet say.

Even if we can’t write code, we can use Microsoft Excel and free online tools to investigate these things ourselves.

James shows how simple it is to grab the data that would feed such a chart so we can manipulate it ourselves. One of the first manipulations is addressing the fact that such a chart is really derived from an extremely small sample size because each data point is highly overlapping to the others. A rolling 10-year return is comprised of 120 months so each new “sample” overlaps with the prior one by 119/120.

James starts the exploration by looking at monthly returns (instead of 10-year returns) vs CAPE.

Let’s turn back to James for interpretation.

Unsurprisingly, that looks like a big blob. (Anything with monthly returns on the y-axis will look like a big blob.)

[Kris: that bold statement is a useful bit of knowledge that comes from looking at financial data frequently]

What does James do next?

We can look at longer non-overlapping periods. Let’s keep with the 10-year forward window and look at decades.

The problem is that we now only have 15 observations! Ten years is a long time, and we simply don’t have that many unique non-overlapping ten-year periods. And we certainly don’t have many unique non-overlapping ten-year periods that are similar to the current market structure and competitive environment.

[Kris: that bold bit is an evergreen problem in finance because investing is biology not physics. Markets learn so output become inputs. What does that mean? Markets are more likely to fall AFTER everyone starts believing they can only go up. The “only goes up” is the output or observation that then becomes an input into how much risk investors take. There is always some price that peers back at history and says “not this time”.]

So James slices the data another way.

Plot the valuation metric itself…

whenever we see an effect, we should ask what other than our pet theory might be causing that effect to appear. In particular a lot has changed over that time period. The market looks nothing like what it did in 1900 today.

And, indeed, if we plot a time series of our valuation metric, it looks kinda drifty.

It’s not really reasonable, I don’t think, to assume that CAPE 20 would “mean” the same thing in 2024 as it did in 1900.

He tries another manipulation:

One cheap and dirty way we can make that metric a bit less drifty and more comparable over time is to standardize it by its values over a recent rolling window.

For example, here I’ve standardized it as a 10yr rolling score. (Not necessarily cos I think that’s the right thing to do – I just want to make a point).

Now it looks a lot more stationary. It stays in the same range. It doesn’t drift off. This is unsurprising cos we forced it to look like that.

[Kris: the bold is another lit bit of fingertip knowledge that you acquire from frequent contact with data.]

Yet, another manipulation:

Now, we can plot our next year’s returns vs this standardized z-score.

If we still see an effect when we do this, it would make us more confident in the valuation effect. If we don’t, it won’t destroy our confidence because we’ve made some pretty arbitrary and dubious scaling choices here.

Indeed, at least with this scaling choice, we don’t see the effect we are looking for.

That’s ok. That’s the nature of work like this. We’re just exploring, trying to break things. We try to look at things from as many different angles as we can and see how much of the limited evidence lines up.

[Kris: I just want to pause for beauty as my wife likes to say. James is spoon-feeding serum against chart crimes and charlatans who read “How To Lie With Statistics” as a manual].

James’ Conclusion

I think the evidence (and economic sense) supports the idea that high valuations are correlated with lower expected returns. But it’s nowhere near as clear-cut as the initial scatterplot suggests. We simply don’t have enough data, and the market is constantly changing underneath us, making it hard for us to draw strong inferences.

My conclusion

This points to an uncomfortable reality. If a data analysis was conclusive then everyone would do the thing prescribed until the data exhaust from the behavior was no longer conclusive. This is deeply reminiscent of what I call the Paradox of Provable Alpha.

Notice what James did.

He recognized that the data proves nothing but it’s simply too underpowered to accept or reject any claims. His prior barely gets updated: “I think the evidence (and economic sense) supports the idea that high valuations are correlated with lower expected returns.”

He goes to bed at night with judgment as his best guess much like a farmer’s almanac will do better at predicting the weather in a month vs some meteorological model.

 

Thanks again to Robot James for the heavy lifting on the original article. I was just narrating alongside it to highlight what stood out to me and how it related to other topics we discuss here.

A couple game recs from Xmas 2024

Holiday hibernation always leads to game recs.

🎲Left Center Right (1 min video)

This game is pure degeneracy and takes less than a minute to learn. Asian grandmas and 5-year-olds alike will lose their minds over it. Huge party hit this holidays. It’s actually an old game, but new to me. It has zero skill so when I heard how it works I immediately poo poo’d it but playing it in a group of 15 for a little cash is amazing.

If you want to make it skillful just create an open outcry side-market on who the winner is. Let’s say “Ann” is playing…Ann futures settle to 0 or 100 depending on if Ann wins so you can bid, offer, or trade any integer price between 0 and 100 based on your assessed probability of Ann winning. It’s a faithful simulation of mock trading (and really similar to the StockSlam game I was playing a couple years ago).

Related posts:

💻Turing Machine (link)

This deduction game offers a unique experience of questioning a proto-computer that works without electricity or any sort of technology. (It uses punchcards!)

The Goal? Find the secret code before the other players, by cleverly questioning the machine.

This game is impressive. With 95 punchcards and 48 “verification” circuits (these are the logic gates you use to test your hypotheses against) they generate over 7 million problems! After one round you are just sitting there wondering how big-brain the designers are.

You can play competitively, solo, or coop. The game is beautiful and stimulates that part of your brain that’s trying to nail the logic for a complicated array formula in Excel. The game says 14+ but I’d say it’s fine for any middle-schooler that likes games.

Zak taught me how to play and then cooked me.

Moontower #253

Friends,

Happy New Year all. There are a lot of new subs which seems perverse since I haven’t posted in a couple weeks but I suspect the “don’t post and grow” strategy has its limits 🙂

It’s a convenient juncture to refresh the administrative aspects of the moontower playground.

First, about the substack:

Calendar

Moontower is published 3x per week.

Sunday: The OG letter now is almost 6 years old. The top section can be about anything while the Money Angle and Money Angle For Masochists section are for investors and traders respectively. Sometimes I throw in From My Actual Life which is what it sounds like — personal stories.

Wednesday Munchies: I share what I learned from something I read, watched, or listened to. Can be any topic.

Thursday Paywall: This tends to be more niche but of practical value to traders. I’d also use these to cover stuff I feel more comfortable discussing with a tighter audience.

These are guidelines. When you get down to it, I have an editorial queue but I go off-script all the time as topics spontaneously capture my attention.

Collaboration

For sponsors: The letter occasionally gets sponsored. I don’t advertise that this is possible, it happens by reverse inquiry but you can interpret the next few sentences as my advertising. The readership of this letter is a delightful mix of smart learners/strivers, wildly successful and influential hitters mostly in finance and tech, and parents. Since I have an 8 and 11-year-old, parenting topics have a lot of my mindshare and spill into the letter. Outside of the trading stuff, it is the most engaged segment of my inbound and that makes sense — none of us got an instruction manual to kids, but it’s like we all have parts of the map. There’s a lot to gain from comparing notes or hell just spewing exhaust (the double-entendre of that word in this context is absolutely intended).

If you want to sponsor the letter, reach out.

For people who want to guest post: These have been giant win-wins for readers and the authors.

Last year 3 were very popular:

Guest posts often are the result of weeks or even months of collaboration as I will act as both sparring partner and editor (I’m protective of the quality in here and whether I suck at this or not is not for me to decide but I do take whatever standards I have seriously). I don’t think I’m overstepping to say the writers find it fulfilling because they’ll give me the piece thinking it’s near its finished version and only to find that it’s got a long way to go. It’s not a fun process necessarily but one that will stretch your frontier a tad further.

If you want to use moontower as a venue to post let me know. If the idea resonates or if there’s a version that we can mutually agree I’d love to host it. If you have special knowledge but don’t have any distribution this can be a great jumpstart to a new hobby or more.

My other major project, which has turned into a growing business, moontower.ai is “option analytics with a point of view”.

There is a lot of development on the product side but one of the larger upgrades is on the community front — a Moontower Discord launched just a few days ago. This is available to all moontower.ai subs.

Montower.ai has:

  • thousands of free subs who can access educational content and the Discord)
  • hundreds of paying subs enabled for analytics, calculators, the chatbot, subscriber-only events, and Discord channel within the broader Discord.

Sign up moontower.ai (free or paid) and find your way to the Discord if interested.]

Finally, I’m working on some trippy moontower merch. Let’s just say it’s stuff that feels amazing to hold in your hands and definitely not typical swag.

Money Angle

The original moontower blog is https://moontowermeta.com/. The “meta” is an important word. Important enough that Facebook stole my language and turned it into their ticker. F’n Zuck. Leave some for the little people bruh.

The reason I used “meta” (besides the fact that moontower.com wasn’t available) was because a lot of things I think about are fairly meta. Knowledge is the object but how we acquire knowledge is the meta. Trading is a very meta discipline because games with counterparties require a solid “theory of mind”.

In the spirit of meta, I really enjoyed a recent post by Robot James titled:

Valuation Timing with Excel (6 min read)

It’s meta because it’s really about arming yourself with data analysis to confront a narrative or chart. It’s worth stepping through the article together to appreciate just how many meta-nuggets it contains.

First, we start with an object-level observation that you’ve likely encountered. I’ll quote freely from the post but all bold is mine:

You may have seen a lot of charts like this recently:

The conclusions people tend to draw from this chart are:

  • there is an obvious and strong relationship between valuation and expected future returns (cheap = good, expensive = bad)
  • valuation estimates are currently historically high; therefore, expected returns of the S&P 500 are historically low.

We should always be wary of drawing strong conclusions from stuff people share on the internet or in sell-side research.

There are a few reasons to be skeptical of the strong conclusions people tend to make on seeing this:

  • the chart might just be wrong (people screw up financial data analysis all the time)
  • 10 years is a really long time horizon
  • all of the 10-year total returns are actually positive
  • why are there so many points? How many 10-year periods has the index even existed for?!

The good news is that, with a few simple skills, we don’t have to believe what randos on the internet say.

Even if we can’t write code, we can use Microsoft Excel and free online tools to investigate these things ourselves.

James shows how simple it is to grab the data that would feed such a chart so we can manipulate it ourselves. One of the first manipulations is addressing the fact that such a chart is really derived from an extremely small sample size because each data point is highly overlapping to the others. A rolling 10-year return is comprised of 120 months so each new “sample” overlaps with the prior one by 119/120.

James starts the exploration by looking at monthly returns (instead of 10-year returns) vs CAPE.

Let’s turn back to James for interpretation.

Unsurprisingly, that looks like a big blob. (Anything with monthly returns on the y-axis will look like a big blob.)

[Kris: that bold statement is a useful bit of knowledge that comes from looking at financial data frequently]

What does James do next?

We can look at longer non-overlapping periods. Let’s keep with the 10-year forward window and look at decades.

The problem is that we now only have 15 observations! Ten years is a long time, and we simply don’t have that many unique non-overlapping ten-year periods. And we certainly don’t have many unique non-overlapping ten-year periods that are similar to the current market structure and competitive environment.

[Kris: that bold bit is an evergreen problem in finance because investing is biology not physics. Markets learn so output become inputs. What does that mean? Markets are more likely to fall AFTER everyone starts believing they can only go up. The “only goes up” is the output or observation that then becomes an input into how much risk investors take. There is always some price that peers back at history and says “not this time”.]

So James slices the data another way.

Plot the valuation metric itself…

whenever we see an effect, we should ask what other than our pet theory might be causing that effect to appear. In particular a lot has changed over that time period. The market looks nothing like what it did in 1900 today.

And, indeed, if we plot a time series of our valuation metric, it looks kinda drifty.

It’s not really reasonable, I don’t think, to assume that CAPE 20 would “mean” the same thing in 2024 as it did in 1900.

He tries another manipulation:

One cheap and dirty way we can make that metric a bit less drifty and more comparable over time is to standardize it by its values over a recent rolling window.

For example, here I’ve standardized it as a 10yr rolling score. (Not necessarily cos I think that’s the right thing to do – I just want to make a point).

Now it looks a lot more stationary. It stays in the same range. It doesn’t drift off. This is unsurprising cos we forced it to look like that.

[Kris: the bold is another lit bit of fingertip knowledge that you acquire from frequent contact with data.]

Yet, another manipulation:

Now, we can plot our next year’s returns vs this standardized z-score.

If we still see an effect when we do this, it would make us more confident in the valuation effect. If we don’t, it won’t destroy our confidence because we’ve made some pretty arbitrary and dubious scaling choices here.

Indeed, at least with this scaling choice, we don’t see the effect we are looking for.

That’s ok. That’s the nature of work like this. We’re just exploring, trying to break things. We try to look at things from as many different angles as we can and see how much of the limited evidence lines up.

[Kris: I just want to pause for beauty as my wife likes to say. James is spoon-feeding serum against chart crimes and charlatans who read “How To Lie With Statistics” as a manual].

James’ Conclusion

I think the evidence (and economic sense) supports the idea that high valuations are correlated with lower expected returns. But it’s nowhere near as clear-cut as the initial scatterplot suggests. We simply don’t have enough data, and the market is constantly changing underneath us, making it hard for us to draw strong inferences.

My conclusion

This points to an uncomfortable reality. If a data analysis was conclusive then everyone would do the thing prescribed until the data exhaust from the behavior was no longer conclusive. This is deeply reminiscent of what I call the Paradox of Provable Alpha.

Notice what James did.

He recognized that the data proves nothing but it’s simply too underpowered to accept or reject any claims. His prior barely gets updated: “I think the evidence (and economic sense) supports the idea that high valuations are correlated with lower expected returns.”

He goes to bed at night with judgment as his best guess much like a farmer’s almanac will do better at predicting the weather in a month vs some meteorological model.

 

Money Angle For Masochists

2 links

🔗Lifetime Achievement Award: Don Wilson (27 min read)

I stood next to several DRW traders when I was on the NYMEX. They were formidable in nat gas options especially the back end of the curve. Risk magazine profiles their founder whose generally difficult to find out about. Most prop firm founders somehow manage to keep a low profile despite their extreme wealth. (Meanwhile Bill Ackman and Ray Dalio want everyone to look at them. I guess I’m “just asking questions”)

 

🧵I got asked what my best and worst trades were (Twitter)

 

From My Actual Life

Holiday hibernation always leads to game recs.

🎲Left Center Right (1 min video)

This game is pure degeneracy and takes less than a minute to learn. Asian grandmas and 5-year-olds alike will lose their minds over it. Huge party hit this holidays. It’s actually an old game, but new to me. It has zero skill so when I heard how it works I immediately poo poo’d it but playing it in a group of 15 for a little cash is amazing.

If you want to make it skillful just create an open outcry side-market on who the winner is. Let’s say “Ann” is playing…Ann futures settle to 0 or 100 depending on if Ann wins so you can bid, offer, or trade any integer price between 0 and 100 based on your assessed probability of Ann winning. It’s a faithful simulation of mock trading (and really similar to the StockSlam game I was playing a couple years ago).

Related posts:

💻Turing Machine (link)

This deduction game offers a unique experience of questioning a proto-computer that works without electricity or any sort of technology. (It uses punchcards!)

The Goal? Find the secret code before the other players, by cleverly questioning the machine.

This game is impressive. With 95 punchcards and 48 “verification” circuits (these are the logic gates you use to test your hypotheses against) they generate over 7 million problems! After one round you are just sitting there wondering how big-brain the designers are.

You can play competitively, solo, or coop. The game is beautiful and stimulates that part of your brain that’s trying to nail the logic for a complicated array formula in Excel. The game says 14+ but I’d say it’s fine for any middle-schooler that likes games.

Zak taught me how to play and then cooked me.

 

Stay Groovy

☮️

seat arbitrage

If a tree falls in a forest, does it have a delta impact?

I didn’t feel like writing so you get the answer I’d give over a beer in Roppongi if you gave me a few minutes to collect my thoughts (and if I drank).

Dark arts. Microstructure. Options.

Enjoy…

 

Another story with some powerful lessons…

Let’s start with one of the best stories I got to be a part of at SIG.

Tina recounted it on Twitter, I’ll offer more color below.

Tina:

Ok, seat arb story. One day, ICE announced that they wanted to buy the NYBOT. Jeff Yass runs into me when he came into the NY office one day when this started, and asked if we should be buying these seats for edge – ICE stock in exchange for seat. I was Head of the NYBOT for SIG at the time with traders in coffee, sugar, cocoa, as well the Russell 1k,2k,3k.

I had talked to many traders beforehand and overwhelmingly, they were against the buyout. So I told Jeff, no it won’t pass and and we would lose buying these seats. Then I dug around more and realized that the vast majority of people against the buyout were leasing the seats and that owners with votes were for.

Called Jeff back and told him I changed my mind. Jeff green lights it. This became such a fun crazy time because, I would be trading during the day, watching ICE stock, watching the seat tape -seat prices on the ticker on the boards, and then when the seat offered were at a sufficient discount, I would stop trading, send my clerk to run to the membership office and bring me docs to sign. The edge from these seats became more than the edge from trading so I would literally stop trading during the day at times to do this. Of course then I had to call Jeff’s right hand man Shawn, and then the COO to free cash up.

We had to put all these seats under individual traders, since technically the traders were the members. So myself, Kris Abdelmessih, etc had many many seats in our names which was also funny. The seats were maybe $650k and I bought maybe 30-40 for SIG.

In the meantime, the CME was also bidding for the NYMEX which was in the same building btw as the NYBOT. Somehow the head of energy for SIG was out for a bit and so I was the most senior in the building. I saw Jeff during that time and he said “ I really really want to buy some NYMEX seats”.

So one day, this guy I knew who owned a clearing company is alone w me at the elevator and asked if I would buy his seat. Jeff had given me a $10m top when the seat was maybe worth $10.8m. Think the displayed market for seats was $8.5 at $9.3m or something. Guy is like, “I will sell you my seat for $8.8m. “

I call Bala [Cynwyd], get his admin, he’s in a meeting but I tell her I needed him. Jeff picks me up, approves it, tells me to call the COO to free margin up and wire the money. Was pretty exhilarating that one trade to get so much edge I must say. The best part was also that, SIG got awarded the CME specialist post on the NYSE so we were the only ones who could sell CME short, setting up for a real arbitrage.

All of this happened when I was pretty young, so you can imagine this was all super cool, the trust Jeff had in me to manage so much of his money. I am forever grateful for the opportunity.

Pretty neat.

I’ll add a bit more.

The head of energy on the NYMEX oversaw nat gas trading as well as me (I oversaw oil trading). Before we came to the NYMEX, he was also my boss on the NYSE. I remember being at the NYSE member meeting when then CEO John Thain (after the Grasso departure) started explaining what would become Reg NMS!

SIG also bought NYSE seats before it went public. By the time, the NYMEX and NYBOT were ready to demutualize they understood this particular style of special sit quite well.

Aside on the NYMEX deal

Before the NYMEX was acquired, it was member-owned. The member owned a “seat” which gave them voting rights as a matter of exchange governance plus the right to trade on the floor.

The CME offered buy the NYMEX in stock. A member would receive some amount of CME shares for each seat they owned. To value a seat you had 2 primary inputs:

  1. The amount of CME shares you get x the price of the shares during some fixing window (I don’t remember the details)
  2. The value of the permit which allowed you to trade on the floor

The permit could be valued by a simple DCF based on how much you could lease a seat to a trader or broker on a monthly basis. Forecasting lease rates could be tricky since the life of the trading floor was already in question.

In fact, this is why lessees were so against the deal. They owned no equity in the deal and their livelihood was at risk if the floor’s days are numbered. The seat owners had their golden ticket. In the time leading up to the sale, seats more than 10x’d in value with many seat owners buying even more. That deal spawned lot of generationally rich Sal’s from Staten Island.

The trading permit however was a small portion of the overall seat value so the DCF exercise was fairly inconsequential. The main risk was the CME’s stock price but as you saw from Tina’s story — there was a lot of edge. If CME stock had 30% vol, with the trading permit, you were basically buying the shares at a 1 standard deviation discount (and that’s if you had to hold for a year). With SIG able to short CME to it was a good trade to plow size into.

I know 9 figs sounds quaint, but it was a lot of dough in a pre-GFC, pre-Fartcoin world

 

Being Nimble

You could relate this story back to my video above. There was some opacity to the market because the seat bid/offer prices were maintained by a small group of office workers employed by NYMEX. Our trading assistant would frequently go up to the membership office bearing coffee or treats to chat them up for color on who’s been poking around the order book. Know the chokepoints.

It reminds me of someone who knows their local RE duplex/multi-family market cold. Occasionally a listing comes up and they will know the exact block and layout so they immediately notice that while it says 3 bedroom, it’s really a 4 with a minor change plus it’s on a side of the street worth a 5% bump. Call the broker, offer 50k thru ask if they take the listing down immediately (and this is in the subset of cases where you didn’t get the look before it hit MLS).

Usually this type of fingertip knowledge in dark corners doesn’t scale, but the seat arb was a rare exception. A bunch of jabronis just made their grandkids rich out of the blue and didn’t want to gamble on the closing of the last 20%. Edge.

I think my favorite part of the story though was the moment. We were all in our 20s and Jeff trusted us. SIG was very entrepreneurial. I got to be a member of every exchange in NY except the NYFE in under a decade. You have enough social aptitude plus lots of training in how to think about risk…“go break into that pit”.

Trading firms, at least ones with a floor heritage, have a fairly flat org structure which is strongly on display in Tina’s story. Empowering employees and limiting bureaucracy seems to be a real edge but requires the right culture and alignment. I recently highlighted the flat structure of Valve, but like SIG they don’t answer to any outside investors.

Going from this real-life example up to the level of lesson, this is SIG’s Todd Simkin explaining the advantage in his interview with Ted Seides.

Ted: Over the 30 plus years you’ve been at SIG, you’ve seen in the hedge fund world this growth of multi-manager platforms. How do you view yourselves competitively to some of the bigger people that you see in the markets?

Todd: We have been in the fortunate position of having the most patient capital of all. One of the challenges with hedge funds is their need to frequently manage not just quarterly reports but monthly, weekly, or even daily reports. They must demonstrate adherence to their outlined strategy and deliver consistent returns.

In contrast, our investors are the principals of the firm.

They understand the risks we take, including outsized risks, and they are the ones driving these decisions. If I decide to put on a $100 million insurance risk tied to the winner of the Super Bowl, I’m not worried about explaining losses to a multitude of stakeholders. Instead, I have a single conversation with the relevant decision-maker, outlining the edge I perceive and the terms of the deal. Their involvement includes monitoring the situation, such as checking the health of the quarterback throughout the season.

This patient approach has enabled us to stay in and grow businesses during downturns while shutting down exposures when needed. Unlike others who must adhere to predefined strategies, such as maintaining a certain percentage of long-short equity exposure, we can dynamically allocate capital.

We benefit from the large capital base while retaining the flexibility and focus of having a small number of decision-makers. These decision-makers avoid imposing artificial rules that might constrain our strategies, a common issue when managing external money.

 

Ted: When it comes to trading, even though long-duration capital is an advantage, your focus often remains on relatively shorter time frames. What sets the traders at SIG apart that allows you to stay successful in an extremely competitive market?

Todd: I think there are a few things.

One is that we focus a lot on the decision process—the information available, how we used that information, and then what trade we made—all of that way before we discuss the results. I think a lot of other people have that upside down. They say, “How did you do? If you made money, great, keep doing what you’re doing. If you lost money, that means that you took too much risk, and that’s a bad thing.”

Whereas our traders are focused on the decision process and the expected value first, and because of that, we don’t do things that I’ve seen some of our competitors do that we would think would bleed away some of those profits.

For example, say you do all your work, there’s no selection bias, there’s no reason to think that you’ve gained new information by being able to enter a trade and you get to buy an asset for $10 that you think is worth $20. Seems great, and then somebody comes along and they say they’ll buy it back from you for $19.

Do you want to sell it?

A lot of people at that point would say, “Well, that’s great. I bought it for $10. I sell it at $19. I make $9. I put it in my pocket, and I go away pretty happy, and I sleep well tonight. Nothing bad can happen tomorrow with my position. I’m out of it, and I’ve just made my money.”

And we say, “No.” If anything, if we’re able to buy more at $19 and we still think it’s worth $20, then we would. The fact that we got to buy it for $10 is great, sort of confirmed now by the fact that someone’s willing to pay $19, but that doesn’t mean we want to sell it and lock in this profit just because you have an opportunity to close a position.

That is part of the culture of the firm. We’re not going to give something up just to feel better in our small individual portfolio, which is part of this much, much bigger firm-wide portfolio. If the whole firm had the opportunity to do that and gave up 10% of our profits every time we had a profit-making opportunity, that would be really costly. Somebody else is on the other side of that trade picking up all that extra money that we’d be giving away.

[Kris: That’s exactly the NYMEX/CME example!]

So part of the culture of the firm is one in which we are finding edges wherever we can find them but then capturing all of it by either holding to maturity or holding to expiration or closing at an appropriate rate when we have either new information or where the markets have changed.

Moontower #253

Friends,

A message for the 5th year in a row:

As always around the holidays, Moontower is taking the next few weeks off and returning in January. We can all use a bit less stimulation at the end of the year. Play boardgames, go to sleep late, binge some shows, gain a few pounds. Laugh so hard bourbon eggnog comes out your nose. Shower your loved ones with attention. You’re not missing anything, including Moontower.

This break will be chill. After Thanksgiving’s Egypt trip, we have no travel plans and look forward to staying put, playing games, hanging out and watching the boys in their holiday hoop tourneys.

Random personal bits:

  1. I’ll break out the game Ra out of the shrink-wrap it’s been in for the past year. It came out over 20 years ago. A game-obsessive former SIG director I was chatting with called it the best auction game he’s ever played. He thinks it’s Reiner Knizia’s masterpiece. RK is on the Mount Rushmore of tabletop game design not just for quality but relative to his field might be one of the most prolific humans ever. He’s designed over 700 games.
  2. My 8-year-old Max likes guitar-driven rock and metal. I took this as tacit permission to buy him a cherry red Fender Squier for his size to put under the tree. I’m desperately trying to manifest a musician in my house. I got to play a bunch of songs with some friends Friday night. Max’s favorite is Bring Me To Life because he likes “when the music stops and suddenly the guitar goes chug chug chug”. My personal favorite from the evening.
  3. It’s giving season. Don’t forget that donating stock is like the most tax-efficient hack ever. Apt timing for a reminder since 2024 made some big-risk takers newly rich lol. In general, most of our giving is concentrated in a few orgs we care about (as opposed to spreading it widely). They are mostly local which offers the chance to be more involved than just giving funds. Just sharing what we do because I know a lot of readers here have giving on their minds.
  4. Besides an amazing start to moontower.ai’s life, 2024 was also my busiest year of writing. I nearly doubled my output from last year because of the Thursday posts and the educational writing for the software. Part of this holiday break will involve outlining a book. I kept toying with the idea but my friend
    Paul Millerd

    gave me advice that seems obvious once he said it — “just start outlining and as it comes together if you get more excited than you’re supposed to do it”.

Finally, I just want to say thank you for reading and supporting this whole moontower thing. It’s a privilege to have a direct relationship with an audience scattered around the country and the globe. As career landscapes shift I suspect as valuable as I think this is, I’m probably still undervaluing it. Maintaining it requires staying trustworthy, curious, and industrious to remain useful and relevant. Which is a reminder — everything is hard, but if you’re lucky to match to your work you are at least excited for Mondays.

Key Posts From 2024

I listed the key posts from the first half of 2024 in the Mid-Year Recap.

The key posts from the second half:

Key readings from moontower.ai

For a discussion of what’s on the horizon for moontower.ai this is a recording of our recent community zoom.

🌙Last Call🌙

You are welcome to use the ROBOTWEALTH promo code for 15% off moontower.ai plans. It expires tonite.

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Path, VIX, & Hit Rates vs Expectancy

The CBOE’s VIX index interpolates 30-day implied volatility based on options struck on the SPX index.

A VIX future settlement price is based on the prevailing VIX index at the future expiry date. It’s a bit of a confusing concept. A future that expires to a VIX index level that looks ahead 30 days.

There are ETFs and ETNs that reference VIX futures (VIXY, VXX). They also come in levered and inverse forms (UVXY, SVXY).

Despite the abstract nature of trading “a level of volatility”, these are popular products. There is 2-way interest in them. SPX returns are inversely correlated to implied volatility making long VIX positions a natural hedge. At the same time, the upward-sloping term structure of SPX implied volatility means implied volatility in the future trades at a premium to volatility today. Many traders will short VIX futures and ETFs to capture the downward drift they expect if the market remains calm as the futures will “roll down” to converge with spot VIX by expiration.

Quant finance geeks about these volatility term premiums. Term structures recognize that volatility is mean-reverting. Historically, SPX realized volatility bounces around 15% give or take a couple percent over long periods. Implied volatility typically trades at a risk premium. The premium also bounces around but 16% IV on a 15% realized vol (ie 1% premium) is in the right ballpark.

The averages mask the distribution. VIX is bounded by zero. It’s rare for it to get to about half its average. It’s rare to double but less rare than halving from 15%. But it’s even possible to triple or quadruple (Covid and Aug 5th 2024 for recent examples). It’s also more common for VIX to go to 12 than 18, at least in recent years.

This low-res farmers almanac description paints a picture of a lognormally distributed index. VIX futures will drift lower frequently but occasionally spike and sometimes those spikes are very high (and fast).

It’s natural for us to think in terms of averages. This habit persists despite witnessing price moves that would be impossible if normal curves were in charge (and despite the warnings from cranky Lebanese deadlifters). The nasty side effect of Gaussian-brain is when it creates the illusion that something is massively mispriced when prices are just properly reflecting a skewed or fat-tailed distribution.

In the 2 min read, The Benefit Of Betting Culture, you can see how the price of a futures-style bet vs an over-under style focuses your attention on the distinction between probability and expectancy. This is the heart of the matter. Investors confuse hit ratios with expectancy constantly.

I field emails and calls too often that are basically retail traders saying “I was doing great selling options for 6 months then I lost it all in month 7”. The reasons for these mini-blow-ups vary from oversizing because they’ve been winning to naive pricing but the universal mistake is in the epistemology.

Some traders are executing without understanding the nature of the proposition. It’s not that selling options is a mistake (there’s a price for everything). It’s that you shouldn’t be surprised by the shape of the payoff. Roughly speaking, if I sell a 10-delta option every month and I win 6 months in a row, I haven’t learned anything about whether my strategy has an edge. I should expect to win most of the time. That says nothing about the expectancy. The person is thinking in terms of 50/50 averages, ie win or lose. But the proposition if it’s fair is more like win $1 9 times and lose $9 one time. If you have an edge, then you either win more often for the same payouts or the payouts are not as far apart but the hit ratio is the same. But most retail traders don’t have large enough sample sizes to infer anything from such skewed results. The track record is nothing but a statistically underpowered study.

Unlike rolling dice or flipping coins, it’s hard to learn anything about the distribution of prices from direct experience. Historicals help but you only have to look at acute incidents in markets over the past 5 years alone to appreciate the challenge of calibrating what’s improbable.

But we can strengthen our conceptual understanding to hopefully be a little less blind to hit rate vs expectancy (or median vs mean) illusions. Option surfaces themselves are great teachers in this regard. In a deeper understanding of vertical spreads, we’ve seen how call and put spreads are a rich source of information about a distribution.

In the remainder of this discussion, we’ll get some mileage towards internalizing the difference between hit rate and expectancy from a non-technical discussion about the price of a VIX future.

Pricing a VIX future (via arbitrage)

If you are a professional trader who just heard me say “price a VIX future” and “non-technical” in the same sentence, you feel like you’re at a Houdini act…” How’s this mf gonna pull this off?”

[cracks knuckles, bends neck side-to-side, deep breath]

Ok, a little background for the uninitiated.

The VIX complex of futures, ETFs, vanilla options and VIX options is one of the more technical areas of options trading. There are arbitrage triangles between these things.

They’re not exactly clean though.

Replicating a variance swap also isn’t clean (not every strike exists and even for the ones that do transacting entire strips is not economical). Neither is dispersion. Neither is isolating forward vol.

But all of these things lend themselves to a fair value that can be F9’d in Excel if you ingest the real-time bid/asks for the building blocks. Every large vol desk has a group that computes a fair value for VIX futures that is derived from SPX options and VIX options. You can trade around that fair value by being better bid on the building blocks that are relatively cheap and vice versa. Manage the residual risks and over time you make money.

I’ve never worked out a model for VIX futures fair value myself as I’ve never traded the SPX complex. But we can still step through it conceptually.

Imagine you are short 100,000 shares of VXX at $16.

*VXX references VIX futures. Just to avoid computing position ratios let’s just pretend VXX and the VIX futures trade for the same price.

You are short 100,000 vega because your position vega by definition is “change in p/l per 1 point change in volatility”.

If vol (ie the VIX future) drops by 1 point, you will make $100,000.

Arbitrage pricing comes from replication. If I can construct a portfolio with a cash flow of 0 in every state of the world, then I have a risk free position (and if I get paid to hold that portfolio I have an arbitrage profit).

To offset my VIX futures risk, I must therefore buy 100,000 vega via SPX options.

[This is conceptual, so we are hand-waving important details like what strikes, expires, weights and managing the deltas.]

At this point we are vega-neutral. Long SPX options, short VXX.

What happens if vol suddenly doubles?

You’re going to make a lot of money.

Why?

Because you lose linearly on your VXX short (-$1.6mm or 16 vol points on 100k shares) but you win more on your SPX option longs.

The reason: you are long not just ATM options but OTM options too. OTM options pick up more vega has vol increases. It’s like being long “vol gamma” (it’s literally called volga). Remember how a long gamma position gets longer delta as a stock goes up and shorter delta as a stock goes down. Well, this is the same effect but for vega via vol.

💡See Finding Vol Convexity for a full explanation.

The fact that you make money because your are long “vol of vol” means you aren’t quite replicating the VIX future though. That’s a problem.

[There’s a cost to being long “vol of vol” so we can deduce that vol never changed and expiration arrives that this so-called hedged position would have lost. There has to be a flip-side to the fact that if vol makes a large move that portfolio wins.]

Conveniently there is an instrument that’s a pure expression of “vol of vol”. You guessed it — VIX options.

The conceptual algebra:

VIX future = SPX options – VIX options

In our example, you can short VIX future, buy SPX options, and then sell VIX options to neutralize this long volga exposure.

This identity is loaded with insight.

  • If I’m long VIX futures and short SPX options, I’m synthetically shorting “vol of vol”.
  • If I’m short VIX futures and long SPX option, I’m synthetically long “vol of vol”.
  • If I’m short VIX futures and long VIX options, I’m short vol but long vol of vol which is similar to be being short SPX straddle but long strangles.

You can envision how looking at the VIX complex you can see which leg stands out as cheap or expensive relative to the others. Layer in implied correlation which relates index vols to single stock vols and suddenly you’re Neo in the matrix.

A day in the life of a vol arb desk is market-making all the flows with an axe. Based on the price of the various parameters like vol, skew, convexity, term structure and correlation you might be:

  • Selling VXX
  • Buying 1-month VIX calls
  • Selling 1-month single stock OTM calls
  • Buying weekly SPX calls
  • Selling SPX 6-month straddles
  • Buying 9-month single stock downside puts

Like a chess player chunking their position, you look at this and think:

“I’m short SPX call spreads and vol near-dated, long upside implied correlation near dated, long a 6 month/9month time spread with a dispersion kicker.

I’m long gamma, short vega, long tons of volga, paying theta”

[Note: the greeks will vary based on the ratio of position sizes. If you’re playing along at home you can try to map the positions to the first line of the summary. And for the greeks you can try to imagine what position sizes are required to make the sign of the greeks make sense]

You do this not because these positions are inherently right or line up with some macro view. You do this because the prices are “right”.

You take what the market gives you. Everyone who’s out there trading on their opinions is moving the price of these parameters around. You are agnostic. Pick up the edge, manage the risk.

All you care about is others having strong enough opinions to move prices around and that you can find contradictions in the matrix.

To quote the closing line in Pacino’s speech in Any Given Sunday:

“That’s football folks. That’s all it is.”

Pricing a VIX future (like an option)

The fact that a VIX instrument has a fair value in a similar manner to how an ETF has a NAV has always kept me away from it. Just like I wouldn’t trade an ETF if I didn’t know its premium/discount. If a box has a dozen donuts I don’t want to buy it for a price that implies a baker’s dozen. Negative edge.

That said, lots of people trade VIX products with a belief that they have an edge based on a relative value lens rather than an arbitrage framework. I’m guessing this leads them to selling VIX futures (which is probably the right side from the arbitrage perspective as well.)

[I’ve often thought that if I were to build a VIX or SPX suite in moontower.ai I’d want to “do it right” which is to use the arbitrage lens rather than extending the in-place moontower analytics to VIX as if it applied. I’ll leave it to you to decide if other platforms do it right or if they’re like children playing house pretending to be grown ups. By the way I have similar opinions about 0DTE. I’d use a totally different framework than the one we currently use in moontower.ai to deploy a 0DTE suite.]

Since a proper VIX complex treatment is prohibitively scarce for retail, it’s additive to think about another way to price VIX. I think it’s intuitive to consider VIX itself an option.

(Again, we’ll stay conceptual. Working out the details is out of scope for this post).

I got the idea for “vix as an option” while answering a reader who emailed me. I’ll share my response so as to not expose the question explicitly.

I wrote (this is edited and expanded):

How do you model option prices and even the underlying price itself if it’s a future that is trading for $1 that will likely expire at 0 but can surge to $10 sometime before that? It’s basically a bubble pricing problem because all known bubbles start to have that distribution and even tech stocks themselves in 1999 were pure extrinsic values themselves. It’s also the distribution that governs the H/J nat gas futures spread.

First let’s discuss pricing an option on this asset. Like what’s a reasonable vol for the $10 call?

I understand your temptation to think strike vol is “what IV will be when it gets there” but this like saying life expectancy is 85 if you survive the first year of life. The option needs to balance the price of many states of the world not just the conditional case. In other words, it’s more like what is your life expectancy at conception.

[For the technical, non-metaphor version see the “local vol” discussion in Chapter 7: Skew Trading of Colin Bennett’s Trading Volatility. The book is a free pdf.]

Another approach might be bootstrapping a discrete model. For example, you could use the price of vertical spreads to compute the implied distribution. Then you can those probabilities as your p and then fit various levels of vol to the call options in various states to see which vols are reasonable. I’d guess you’d end up with something that made the market look pretty rational. Like that call option might have a 10% chance of being 200 vol contributing 20 vol points to its IV and the remaining vol points are some sumproduct of the non spike scenarios.

One thing that a bit hairy is implied probabilities are “terminal” probabilities.

It’s easy to understand the distinction when you think of VIX. You have a 9m future that’s trading 18 but will probably expire at 12 or 13. But if I told you it’s 75% to touch 30 during its life how does that effect your intuition of value?

If you use VIX call spreads to assess the probability you miss this because they will assign very little probability to touching 30.

Instead you can use the deltas (delta x 2 is a useful guess for a one touch probability). The one-touch probability is much higher because it respects path.

 

That was the end of my response. But a skeleton for pricing VIX as an option is there.

Think of the lognormal distribution (bounded by zero, positive skew, fatter right tail). As you increase volatility, the distribution “squishes” to the left.

wikipedia commons

A look at March 2025 VIX Implied Distribution from the futures options

Here’s a condensed view of the Mar2025 options chain using mid-market for calls and puts.

Things to note:

The extreme IV skew

  • The 20% OTM call (~21 strike) is 3x the price of the 20% OTM put (~14 strike)
  • The delta of that 21 call is 2x the delta of the 14 put

The distribution

Remember:

the price of a put [call] spread divided by the distance between the strikes estimates the probability that the underlying expires below [above] the midpoint of the strikes.

By looking at the spread of adjacent spreads (ie the butterfly) we estimate the probability density at the midpoint. If we do this across strike we have the implied PDF.

[It’s a bit noisy because of market widths and strike distances not being uniform but I normalized in a reasonable way for these artifacts]

Even though the future is 17.35, the put spreads are expensive and the call spreads are very cheap telling us that March VIX is most likely to expire between 12 and 14.

If you are betting on roll down, it’s already priced in. The 16/14 put spread is $1.03 but the most it can be worth is $2. So despite the fact that the future is 17.35, you get slightly less than even money on the future expiring below 15. In other words, the future falling 14% is already baked in as the median outcome.

Implied distributions like this tell you the market expects the price to fall but it must still balance the chance that in the meantime it can double, triple, or more. It’s the kind of distribution you expect in bubble names where “the market can remain irrational longer than you can remain solvent” but everyone knows the asset is eventually going to be much lower.

Next time VIX spikes watch what happens. The VIX vols will pop, but the put skew will get smashed. The net effect is the put spreads get very expensive because VIX looks like a rubberband to investors…the higher it rips the more distance it has to snap back down which it eventually does. On a VIX rip everyone wants to buy put spreads to have a rick-contained way to capture that reversion, but the surface is too smart for that. You might end up with a VIX future at 25 and all spreads say…”meh, it’s going back to 15”. The contrarian bet would be to bet that it it’s NOT going back home. The options market will give you that bet all day. for good reason. But the trade you want to do is priced like a Chiefs point spread. Sure you’ll probably win, but the risk/reward a priori not a “excess return”. It’s consensus so you’re flipping coins for fair.

This is how the SLV surface repriced in 2021 when the WSB apes tried to squeeze it higher GME style. I was a very active silver options trader then and just found myself frustrated about how smart the surface was in adjusting. Speaking of GME, this type of extreme lognormal distribution took hold when Kitty roared. The cheap call spread beg you to buy them because nobody thinks GME is actually going to expire higher even though it might touch a high price. That it will touch a price is basked into the expensive calls outright and their deltas.

Look at the VIX chain again. The 30 call has a .24 delta. This implies that there’s a 48% chance that VIX will touch 30 at some point before expiry. With such a framework, you can start to see how the VIX future option vols and therefore deltas inform what the price of VIX futures should be. You might draw opinions about a VIX call being expensive relative to VIX itself (notice this is exactly equivalent to saying the implied volatility is expensive).

To be honest, this is all dancing around the fact that just pricing the VIX futures, SPX option, VIX option triangle is the final boss. But the point of this is to give you exercise in noticing that the fact that a VIX future is probably going from 18 to 13 doesn’t mean selling it is necessarily edge. There can be a better leg out there but focusing on “what will probably happen” is a form of probability myopia that distracts from expectancy thinking.

[The difference between positive expectancy and probability is the fertile soil of investing charlatanism. If you were to start a scam strategy from scratch you’d start with trades that have a high hit rate and just hope you collect enough profits before you see the whole distribution. Ideally with someone else’s money.]

We’ll leave it there.

Related reading:

What Equity Option Traders Can Learn From Commodity Options

Bubbles: Knowing You’re In One Is Not Even Half The Battle

laying in the weeds

Friends,

Within a week of the furious post-election rally I went to Twitter:

What is the most asymmetric way to bet on deflation? Is there an option on this that’s left for dead?

There’s conventional choices like bond calls but i want to hear what ya got…also not a short run expression…something long dated that gives time to see the possibility of deflation looking out the Overton window.

Also I know you think it’s impossible. I don’t care about “think” though.

I want to know where deflation is “offered at impossible“.

Deflation doesn’t have to be likely for a bet on it to be profitable. And it’s certainly nowhere near impossible — this is a rich country with wealth concentration (rich people have low MPC), declining birth rate, and an NVDA market cap says robots will have lots more high paying jobs, and an incoming government promises lower spending (although this doesn’t translate to a smaller deficit if the tax receipts fall by even more).

There were plenty of responses.

(Unsurprisingly and for good reason, the most common answer was calls on treasuries or nitroglycerine like 30 year zeros.)

After I posed the question, I thought I’d follow up with a meta-trading lesson that governed how I thought about execution as a discretionary trader. It’s more relevant for professional trading where slippage is a larger concern but I expect anyone can at least benefit from hearing about it.

Laying in the weeds

Right now the market optimism thermometer is Sahara hot.

I charted SPX earnings using data from Gurufocus:

https://www.gurufocus.com/economic_indicators/4202/sp-500-operating-eps-with-estimate-ttm

SPX earnings are up 48% in 5 years from 9/19 to 9/24 or ~8% per year

SPX total return over that period is 110% or 16% per year so multiple expansion has been riding shotgun 1-for-1 with earnings.

And now for 2025, forward earnings expectations are expected to grow 16% after being fairly flat from late 2021 until late 2023. Forward p/e has actually dipped despite SPX at all-time highs because of the animal spirits baked into the denominator.

I’m just using this as an example of divining sentiment. You could use option surfaces, COT reports, the “is my aunt or Uber driver talking about investments” indicator to get a pulse. (See Staring At The Window for an example of thumb-in-the-air vibe detection).

Your vibe detector gives a little elbow nudge, “hey knuckle-dragger, pay attention for a sec, it would make sense that some state of the future is being heavily discounted or left for dead.”

And so you start thinking along the lines of the tweet I opened with. What’s hated? What’s offered at zero? What’s being extrapolated to where only disappointment is possible?

[The most popular post I’ve written is once again relevant: Why Investing Feels Like Astrology]

If you are in a professional seat, you have time. You’re not gonna top-tick it anyway. Relish the complacency. The FOMO. It’s the yang to the yin and why you are able to get the other side. You should toe in and hope to lose while your position is small, because every bad mark is spring-loading energy for Newton’s 3rd Law,

You don’t go lifting and hitting. You don’t aggress. You lay in the weeds. Your waiting for flow that opposes your idea.

But you also want to understand “why”.

You wonder, is there some dumb structured product flow out there whose greeks need to be recycled into listed and happen to match what I’m looking for?

Is the driver of that flow some easy-to-package message about how inflation is here to stay so convince the clients to basically sell the deflation option for free.

Classic Wall St momo herding. Package a trend as a cornerstone allocation. Commodity futures for the long run right? (Nobody talked about roll return in 2004 and it’s all anyone talked about a decade later).

Ride the narrative to sell product because the narrative “makes sense”.

While the vampires seduce the consultants and investment committees you search the conference room trash bin for glossy marketing literature. What product are they hawking now? It probably spits off a ton of greeks that are hated, contrarian, and most importantly — cheap.

So you lay in the weeds with your little axe to own that which nobody wants. You get edge on the price because it was “sold”, you didn’t have to cross the market. As a discretionary trader looking for long shots you generally don’t wanna go around overpaying for lotto tickets. Instead you wait for handsome Oxford grads with accents to convince everyone that the lotto ticket is worthless. The prepared responder vs the enthusiastic aggressor.

And the really fun thing about greeks is now someone has the other side. You don’t just want your low probability idea play out — you want someone in pain on the receiving end. It might not be your direct counterparty but it could be a part of the ecosystem that is implicitly short that option whose clamoring exacerbates the problem. When unexpected things happen, something somewhere breaks.

(I may be showing a bit too much of my commodity roots but my model for those markets is — boring, business as usual until a cowboy shows up. It’s a zero-sum game so the other big players who’ve been around for while see the cowboy pyramiding and winning. And so the funds lock arms — time to run the new guy in. Oh the margin requirements changed overnight, never saw that coming did ya cowboy.)

When the crowded side starts to realize that what was 95% true is only 90% true. They sold something at 5% that’s now worth 10%.

Then you get a little mutual reinforcement…narrative can follow price too. As they need to cover the Overton window is also moving against them. It’s invisible but visceral at the same time.

I know I want the “long where it ain’t” option. The option with volga. Because that “it’ll never get there option”, the one nobody wanted, is now the one you can’t touch and that feeds on itself.

One last thing…

Noticed this yesterday:

Better than T-bills but gotta pay state tax.

[Heard those rates have actually come down too! The crypto peeps tell me the cash/futures arb is insane right now so this is presumably a trickle down to any levered long on BTC, although I’m not sure how fat the margin requirement is on the short IBIT put would be if you were just long the synthetic future.]

Even simple SPY cash/futures arbs are fat.

I’ll re-tell some market history that I remember hearing. Hedge funds did well avoiding the dot-com crash. If you weren’t mandated to be long stocks at extreme valuation you could hide out in 5%+ treasuries. Today with P/Es where they are, the forward implied returns on stocks look pretty awful compared the risk/reward of these simple arbs.

I get the excitement. Deregulation, lower taxes, AI, all that. But I also remember the fiber bubble of the late 90s. The longs weren’t wrong — laying cable across the ocean was gonna change the world. But the competition was fierce and changing the world didn’t mean “earn a return”. shale changed the world. And we got a bunch of consumer surplus because it’s a COMMODITY. AI is amazingly useful. But what if it’s a commodity. My guess based on auction-clearing prices being set by the most optimistic bid —OpenAI, Anthropic, and a host of big companies are in a money-incineration competition. But we’re all gonna be better off for it. No reason to double down on the benefits in your portfolio where the eventuality of the benefit is least reliable.

Personal portfolio thoughts

My equity allocation is on the low end of the range I manage in it as I’ve been trimming in the 2nd half of 2024. Also poking around for asymmetric bets of the wide strangle variety. Basically replacing some length with soft upside deltas as blow-off top insurance plus some downside bullets.

One thing I find tricky is that I suspect that we are going to stay in a low correlation regime absent a large sell-off as I suspect the incoming administration will create all kinds of upside and downside headline risk for single names. But implied correlations are already low which means single-stock vols are relatively high. So this particular view is baked into market already. Means if I’m buying it’s with one hand not two.

In alignment with the trimming, my cash position is on the high end. Speaking of cash, some personal convo on real estate…

One of the reasons we are holding extra cash is we have been house-hunting more actively instead of just passively browsing. Our lease ends in August at which point it will have been 5 years. The landlords have indicated they might want their house back although we are still crossing fingers that they won’t.

Cash is a valuable option when buying a home. Especially here in the Bay Area. In fact the option is probably quantifiable. When we sold our house in 2020, the winning bid was 10% higher than the cash bid behind it and well-thru our asking price. Why?

Because the buyers were contingent on selling their home and they were getting a mortgage so they knew they had no chance if they didn’t dangle a sum that enticed us to accept the risk of their offer vs a clean cash offer.

When we bid (we have bid 3x this past year) we offer all cash and quick close times.

[This also helped us win the house in TX against 4 other bidders…which we ended up flipping a year later. See Reasoning Through a Housing Trade Out Loud]

We are never the best bid, but the cash offer is useful because it gets us a callback and into a situation where we have more info than the initial approach which is our objective. So we can probe.

Sellers don’t like closing uncertainty. They are wary of bidders drawing them into a contract with contingencies then trying to re-trade. In fact, you have no chance of buying here if you don’t waive inspection contingencies. Bring your contractor friend to check it out before you bid.

I guess this cash thing is becoming more common too. In the past year, we’ve given cash to 2 separate family members in other regions so they could win their bids. They financed after closing to pay us back.

[Random: the family we sold our TX house to financed the property with a loan against their stock portfolio. Morgan Stanley sent an appraiser!]

Anyway, back to our recent bids this year. We whiffed on all of them. And that’s fine — that’s a feature of our strategy. Mostly. Missing on the last one hurt. In hindsight, I still can’t tell what the right play was. I’ll walk you through it.

We made a cash offer about 2% higher than the asking price which seemed a touch low. We asked for a 3 day inspection period. At this point the game is hang around long enough to find out if the house is attainable at a price we are comfortable with and then sharpen our pencils if we get to the next round.

There were 7 offers. Ours was middle of the pack but attractive enough from a “they will probably close point of view” so we were one of 5 offers that received a counter.

They asked for another 3% and to waive the inspection period. That morning we brought our contractor and architect friends to see if we could do what we wanted to the house. They gave the green light.

(Having another 9 months on our lease, this was a great opportunity to scoop a smaller house put another 50% into it over the course of a year and end up what we want for an all-in price that we are comfortable with — I’ve been looking long enough to know those stars rarely align which is why this was the first house I started to feel excited about)

We accepted the counteroffer requirements.

That night Yinh and I were going out on a dinner date. I was excited that it could be a celebratory one.

I was wrong.

We were outbid by another 1.5% and they weren’t going to do more rounds. The final bid was also cash.

We understood our choices when presented with the counteroffer:

  1. Accept
  2. Counter with lower offer than what they ask for
  3. Be aggressive and counter even higher

The winners chose option 3.

The tricky thing about the final price is it reminds me of the paradox from The Most Underappreciated Aspect of Trading:

When you look at a price, you wish you could have traded it as counterparty to the aggressing order.

Now for the anti-climax.

It’s a fake idea because had you been on the price, the price would have been different. You would have affected it. This idea is like a painting to be viewed from afar. It’s not of any practical value, um, other than the fact that it holds the deepest insight in all of trading.

Knowing the final price, gun to my head, I would pay it. But if I tried to pay that in real-time I probably wouldn’t have gotten filled and instead hit our top (at this point we are 8% through the asking price which is close to but probably still a touch short of what the highest price I could have imagined the house going for).

Pretty unsatisfying.

If I knew we could rent forever I’d just do that. I have no interest in signing up for renovations, $12k/yr home insurance bills, maintenance (or the cost of outsourcing it), and the forgone returns on the cost premium of buying vs renting.

But renting is roulette in many ways. We won to it for the past 5 years but we know we got lucky so it’s hard to get to down about the recent miss.

And yet…

I become more Georgist by the day.

random oil option features

A moontower.ai user asked about oil volatility and correlation in the context of WTI and Brent crude. I did the editorial equivalent of showing him a cool scar:

Let me take these in turn.

USO is relatively illiquid these days relative to the futures options. It used to be more liquid. I spent the better part of a decade relative value trading it vs the futures options including doing create/redeems (I was constantly at OCC position limits — which have a horrible one-size-fits-all application).

Long-dated USO options are very interesting instruments because they are effectively long-dated options on a rolling CL1 contract. This is very different from say a 12-month futures option that references a less volatile CL12 contract.

This difference is why you can relative value trade it, but it’s a complicated model (actually I think a good interview question for a trader or quant is to come up with a model for this conceptually). It’s nice that it offers you an option chain on CL1 while the futures options don’t.

As far as Brent or for that matter heating oil and rbob which you didn’t ask about, the correlations to WTI change periodically when the fundamental details become bottlenecks in their respective markets. Refineries can’t just easily switch their slate between product grades so you can get over/under supply idiosyncracies. There’s an active “arb option” market which is options struck on the spread between WTI and Brent.

Further complicating matters is that Brent and WTI futures and futures options for a given month do not have the same expiries so when you do relative vol trades between them you end up with these residual calendar risks (brent options expiring a week earlier than WTI!)

There was a period of time where this is all I traded so to download all there is to say on this is impossible. You can make a career out of doing nothing else. If you like bloodshot eyes and your hair to be drained of youthful pigment of course.