Here’s a summer reading book rec for investors:

My wife and I are both reading this. It’s laugh-out-loud funny. Gary is an excellent writer. The novel is written from the point of view of an elite school’s endowment CIO. It presents as a series of meetings with prospective managers, deals with politics within the endowment but also with the external culture of the investing world. If you are in finance, Gary’s sharp eye will delight you til no end.

I’m less than halfway through it and already I can’t recommend it enough. It was recommended to me by an allocator (thanks Tom!) and I saw in a recent Byrne Hobart letter that one of Byrne’s friends physically accosted him for not having read this book yet.

You can see Matt Levine’s endorsement in the screenshot. I wouldn’t have articulated what Matt wrote, but once he said it, I noticed that’s the exact feeling I get reading it.

The CIO’s banter, verbal chess, and inner monologue reveal a fox-like savvy honed by years of battle with both the market and the managers who make convincing cases for how they’ve mastered them. It gave me a tremendous appreciation for the difficulty of the job. If you are not a professional investor and have confused the most generous market run in anyone’s living memory for your own brilliance, then considering the CIO’s constraints will update your context for the pro version.

I found this recently reinforced by Jeremy Giffon in this terrific interview on ILTB.

Patrick: You have this funny view that the whole myth of how difficult it is to beat the market, however you want to define the market, is wrong. I’m curious for you to expound on that. That seems to have become, post-Jack Bogle, one of the deeply held truths of the market is that it’s extraordinarily difficult to beat the market, so you shouldn’t even try. You should just opt out of the battle. I think you have a very different view on this.

Jeremy: Buffett and Munger were my main teachers on investing. Buffett says that he wants his estate outside of Berkshire to be put in the S&P. That’s his advice to the general public. People take that to say that Buffett’s saying you can’t beat the market. I don’t think that’s what he’s saying. I think he’s saying for the average person, you shouldn’t try and beat the market. Implicit in that statement is leaving out any sort of active investor. Maybe the anecdotal side would be Buffett saying you should put all your money into the S&P. That’s the most rational thing you should do. On the other side is the sort of empirical argument, which is, look, most professionals don’t even beat the market after fees. This is this one-two punch of — the godfather of investing says, don’t try. Seemingly the smartest people with the best incentives in the world can’t do it.

And then the other thing is, for a professional manager — and this is sort of the paradox with the Buffett thing — it is really hard to beat the market because you have all these other factors that the average person doesn’t have. And this is the Peter Lynch argument — and increasingly I think Peter Lynch was a genius about this — which is that, yeah, when you’re a professional manager, by and large, you have all these mandates, you’re running a business, you have customers that you need to keep happy. It’s more difficult for the professional money manager to beat the market than the average amateur.

This is from Mandy Xu at the CBOE this week:

The outperformance of small-caps is a sign that the equity rally – which has long been dominated by the mega-cap Tech names – is starting to broaden out. In fact, over the past month, Tech has been the worst performing sector (-10%) while YTD laggards such as Healthcare and Financials have been the best performing (+12% and +8%, respectively). The calm at the index level (SPX Index -1.7% over the past month) belies these large rotations underneath the surface. This is why single stock volatility has been so elevated, even as the VIX^® Index has fallen. The spread between the two, as measured by the VIXEQ^SM-VIX Index spread, widened to an all-time high of 31% last week.

If the single stock volatility is high relative to the index volatility, that’s another way to say the realized correlation between the stocks is very low. The index is dampening volatility because it’s acting well-diversified. A so-called stock-pickers market.

There’s so much violence under the surface. A momentum rotor whirring to give different categories an unpredictable spotlight before abruptly re-targeting.

I’m going to think aloud here a bit.

On the one hand, the momentum rotor feels like a market technicals concept. It’s the signature of flows and liquidity reaction functions. The intent of the orders that generate these flows might have a fundamental thesis behind them. In an investing mind. The trading world is indifferent to the ultimate intent but seeks to collect a service fee by spreading the acute dollar pressure in one part of the investing crust to another part of the surface.

From this perspective, the marginal price is set by an active investor. Perhaps the cumulative orders of levered pods.

At the same time, we hear of the “passive” bid. The Trump accounts and their monogamy to the SP500 (for the moment anyway) being yet one more increment to the bid.

If passive is the marginal price setter, I’d actually expect correlations to be structurally increasing. Not only do they seem to be structurally falling, they are conspicuously dormant at today’s historical lows.

I’m not sure the best way to reconcile these arguments. Is it an artifact of observation at a short time scale (rotor flows) vs a longer time scale (a slice of S in the GDP identity is pro-rata routed to the SP500 as long as the economy grows)? It’s reminiscent of the Earth’s dual rotation, where the planet spins on its own axis while orbiting the sun. The momentum rotor is called a “day” and the steady levitation of valuation comes from the passage of the “years”.

Regardless of the reconciliation of orbits or lack thereof, I am fractured by an ongoing dissonance:

I don’t discuss any of this in the Investment Beginnings Course!

It’s easy to anticipate a commoner’s consolation. “Kris, you are right to be teaching the textbook basics; we want the kids to learn investing, not gambling.” But it does feel quaint and too convenient to not address the marginal price setter. Buffett does so in a particularly striking way based on Graham’s allegory:

He said that you should imagine market quotations as coming from a remarkably accommodating fellow named Mr. Market who is your partner in a private business. Without fail, Mr. Market appears daily and names a price at which he will either buy your interest or sell you his.

Even though the business that the two of you own may have economic characteristics that are stable, Mr. Market’s quotations will be anything but. For, sad to say, the poor fellow has incurable emotional problems. At times he feels euphoric and can see only the favorable factors affecting the business. When in that mood, he names a very high buy-sell price because he fears that you will snap up his interest and rob him of imminent gains. At other times he is depressed and can see nothing but trouble ahead for both the business and the world. On these occasions he will name a very low price, since he is terrified that you will unload your interest on him…

Mr. Market has another endearing characteristic: He doesn’t mind being ignored. If his quotation is uninteresting to you today, he will be back with a new one tomorrow. Transactions are strictly at your option. Under these conditions, the more manic-depressive his behavior, the better for you.

Mr. Market is there to serve you, not to guide you.

Yet, I have my humble reservations about Buffet’s view. It’s not that I think it’s wrong, it’s that it leaves you in a quandary about a critical aspect of decision-making. How do you weigh information when forming an opinion?! A strict reading of Buffet is that Mr. Market is emotional and irrational. But traders are taught to respect bids and offers. They are made with real money in proportion to conviction. It’s exactly why we say betting is a tax on bullshit. The essence of trading decisions is how you form priors and then Bayesian update. You can’t ignore bids and offers if you think they contain information (and you certainly would care about if the bids and offers are forced or “uneconomic”).

[The maximalist Buffet view is that “short-term”, an admittedly poorly defined descriptor, price behavior contains no information. I’m not actually opposed to this possibility under some conditions but it’s obviously not universally true. The price signals from shortages and surpluses in the physical world matter. It’s the entire basis of capitalism. Insofar as share prices are an inference on the supply and demand of the physical world, we should not ignore their deltas. But the amplification embedded in the math of capitalizing those inferences into a multiple leaves a lot of room for accepting and refuting its justifications.]

Circling back to what I teach the kids in the future or possible course edits might be to discuss macro simply in terms of Kalecki-Levy type accounting identities. They aren’t predictive but they are explanatory. Every liability is someone else’s asset. So if the G deficit spends, the private sector savings mechanically increase (I need to review the framework, to be accurate in teaching it, but you get the gist). Some of that S will be siphoned into stocks, creating structural demand to be weighed against the arrival of issuance (ie supply).

From that foundation, one can see the movement of the index of all corporate shares as one orbit, and the micro discernment of relative value underneath being the subject of traditional valuation canon, while the trading/gambling science informing the physical “equations” that govern the sector rotor.

I stuck with physics in adherence to the orbital analogy but we all know investing is biology.

But I’ll wrap with a quote from my article about why it feels like astrology (this remains the most widely read post in moontower history):

In a recent interview on Corey Hoffstein’s Flirting With Models, volatility manager Cem Karsan explains:

I’m not naive enough to envision a unifying theory of investing, but teaching the class does motivate an impulse to do better than the textbook in tying things together in a way that is not just correct-ish but useful and relevant. And respectful to preteens and teens’ intellects, for whom education is increasingly patronizing.

Here’s Victor Haghani:

A high VIX1 is widely considered to be one of the cleaner buy signals out there. A recent piece in The Financial Times made the case directly: when the VIX climbs above 30, forward returns have been well above average, positive most of the time, with double-digit six-month gains.

The Financial Times case is “buy the f’n dip” logic with a VIX gate. It’s exactly the type of thing that a layreader numbly nods at when the SPX is sitting near an all-time high. The Financial Times’ case is lazy from the perspective of both investors and active traders. For the investor, it’s just survivorship bias. Knowing what we know now every pullback has just presented a bargain. The market literally “going on sale” like it’s Prime day. VIX spikes over 30 just coincide with the sales.

The question you care about is one that an active trader hearing that statement would think to hypothesis test. Given that buying any time before an all-time-high has been worked out well, how do I distinguish between relatively better or worse buys?

Back to Victor:

What that leaves out is risk. Buying the spike means taking on a lot more of it, and the strategies that did the opposite, trimming exposure when fear ran high, held up better. So the popular signal may have it backwards.

Raw returns aren’t the right thing to optimize. You care about compounded returns since investing is a repeated game. Compounded returns are risk-adjusted returns because a geometric growth process penalizes volatility.

Elm Wealth tests FT’s claim not on raw return but Sharpe Ratio, or how much return you’re getting per unit of risk taken, as the variable to maximize if we care about risk-adjusted returns.

When Fear Spikes, Should You Buy? Elm Wealth | 5 min read

What they found when they ran the numbers on S&P 500 and VIX data from 1990–2026, they found:

• A plain static stock/T-bill portfolio: Sharpe ratio of 0.50
• A strategy that buys more when VIX > 30% (the popular advice): 0.47 which is slightly worse than the null case
• A strategy that reduces exposure when VIX is high (inverse sizing): 0.54
• A simple momentum strategy (cut exposure when the market is falling, which is typically when VIX is elevated): 0.59 — the best performer

It’s always bears repeating how risk scales:

When volatility doubles, the risk of holding stocks is actually four times as large (because variance, not standard deviation, is what matters to risk).

To merely hold your position when VIX doubles, expected returns would need to quadruple. To justify doubling down, they’d need to increase eightfold, which the authors deem practically implausible.

This post led to some smart quants chiming in on X.

Here’s @ptuomov:

This is a very low-resolution statement because each word represents many variable choices when you get into research:

Define “high”, define “trending”, “six months” was probably just a placeholder term

The degrees of freedom on the choice notwithstanding, the idea makes sense:

You are using the signals from the derivatives market, a place where leverage attracts early movers and smart money, to give a leading indicator on “the market environment is changing from the status quo” and collective anchoring biases make the wider market underreact. The way to profit from the seeds of this new information is to follow the trend.

There’s that line what the wise man does in the beginning, the fool does in the end.

The quant view is trying to find the signal of moving from the end of one cycle to the beginning of another. Trend following in a sense has a long option flavor. The premium is all the false starts and the payoff is when you finally catch a trend.

Meanwhile, buying the dip is a short option strategy in that it is betting on mean reversion as opposed to further divergence. Buying stock when VIX spikes is a mean reversion trade. But when you examine that as a strategy from the vantage point of all-time highs, it takes for granted that the mean is a good thing.

When you read a claim about a course of action, it’s good mental hygiene to first triage it as: is this directionally long or short vol?

Any moontower.ai subscriber can prompt our trained agent. Even if you aren’t a sub you can give it a try for free. Our team plans have included an API but we just launched an MCP allowing users to connect their own AI’s to our API endpoints.

This gives users maximum flexibility. We are tuning our agent on a regular basis, but if you prefer your own tool stack and AI you have that choice now.

We use evals for automatically RLHF’ing Moontower Agent and I also have a manual process where I give the agent and the MCP (using Claude Code) the same prompt, and then judge them myself. Very old-fashioned. I’ll share more about what we’re learning from this in the future, but in the meantime, here’s a relevant article from the market-making firm Optiver:

Where AI Trading Models Work and Where They Still Fall Short (4 min read)

Optiver’s Applied AI team did a different kind of eval. They gave several leading large language models the same assessments they give human interns and junior traders.

The results indicate where LLMs excel…

• grasping trading theory
• calculating fair value
• recognizing risk

…and where they still stumble:

• multi-step reasoning
• updating beliefs on the fly
• maximizing expected value under pressure

Even before AI was dominating the conversation, traders have always been obsessed with learning from data. A common example is in transaction analysis. Looking at the trades you did filtered by counterparty, venue, method (ie voice/electronic) as you suss out where you are most likely to be adversely selected. This is a hard problem even with structured data. For example, it might be straightforward to filter by how you do against live option orders (as opposed to delta neutral packages), but there are so many possible permutations. Should I consider how the quote was framed before the order came in? Do I treat a resting order differently than if I’m hit or lifted? Does time of day matter?

But now consider the scope of the unstructured data problem. The counterfactual. The order a broker showed me, I passed on and proceeded to trade without my participation. You’d need to record every phone call (actually this is already done for compliance reasons. In fact, when I interned at a bank in 1995 one of my tasks was to change the giant reel of tape!). But you’d need to link the audio of what the order was to the print when it hit the tape. Or track the fact that it never even traded. It’s like tracking the p/l of a non-trade that could have been. With transcription so cheap, this is feasible now, but it wasn’t when I was thinking about it. You could have traders note when they passed on a trade, but this would be so tedious that it was always a non-starter on a high-volume market-making desk.

My guess is that some trading shops might be doing things like this now (if not, you’re welcome for the idea). But this Optiver article made me wonder when trading rooms will be mic’d up. Jarvis listening to all the conversations, meetings, and debates to cheaply turn unstructured data to structured data.

Your voice, its quiver, your cadence, your pauses, your keystrokes, your glances, your heart rate. Insofar as humans will still be trading, it’s hard to imagine the data obsession that’s already penetrated the MLB not make its way to desk talent.

You’ll know singularity is close when the employee handbook stipulates bathroom breaks as the only acceptable cause to remove your electrodes. Buy stock in Gillette. Every man on a W2 will need to shave their chest for a clean connection.

Related

Elm Wealth let AI compete with humans in their popular Crystal Ball Challenge. You can give it a try yourself:

https://crystal-ball.elmwealth.com/

Elm’s founder Victor Haghani:

A couple of weeks ago we let you loose on our Crystal Ball Challenge: tomorrow’s headlines, $1 million to trade in stocks and bonds, and four AI models to beat. Humans showed up in force, logging thousands of plays and adding over 1,500 entries on the leaderboard.

Here is how the AI models are doing against human players so far:

– Claude: winning 65% of the time
– ChatGPT: 50%, a coin flip
– ️ Grok: 43%
– Gemini: 40%

Both the Wall Street Journal and The Economist covered the experiment this month, and both keyed on the same finding: the AIs are great at reading market-moving news, but they struggle to size their bets appropriately. Knowing what to trade turns out to be the easy part. Knowing how much is what trips them up.

If you have not played yet, three of the four AIs are losing more than half their matchups. Pick your fight. If you have played but not lately, your spot on the leaderboard might no longer safe.

And finally, just before I scheduled this to send out I came across Dwarkesh’s:

Subtitle: “Labs are throwing away the most valuable data”.

transcript