This week’s episode of the Money Stuff podcast is out now. Our guest host this week is my friend Mary Childs (of Mary in America). We discuss CDS gossip, suboptimal mortgage refinancing, cheap options, shopping for savings accounts, billing by the hour, AI and the apprenticeship model, second-price auctions, chandelier bidding, the IVV to VOO pipeline, rich person trades vs. hedge fund trades and how much we all miss Katie. You can listen to this week’s episode here:

You can get Money Stuff: The Podcast at Apple, Spotify or wherever you get your podcasts. We’d love your feedback. You can email us at moneypod@bloomberg.net.

The National Basketball Association has rules limiting how much teams can pay players. There is a salary cap set by the league each year based on projected total “basketball-related income” for that year; for the upcoming season, the cap is about $165 million. Loosely speaking, a team is not supposed to pay players more than the salary cap, though there are exceptions. There are also caps on individual players’ pay; the most any individual player can get paid is 35% of the salary cap, or call it something like $58 million a year this year. [1]

The point of these rules is to create competitive balance. Different NBA teams have different amounts of money, in part because some teams are more lucrative businesses than others and in part because some teams’ owners have more money than others from their non-basketball endeavors. For instance, by far the richest NBA owner is Steve Ballmer of the Los Angeles Clippers, the former chief executive officer of Microsoft Corp., whom Bloomberg ranks as the ninth-richest person in the world with a net worth of $173.5 billion. If he was allowed to spend freely on player salaries, then (1) he could hire all the best players at astronomical prices, (2) the Clippers would win the championship every year and (3) a bunch of regular-billionaire NBA owners might bankrupt themselves chasing him. So there’s a cap.

Of course Ballmer might prefer not to be constrained by the cap. He owns the Clippers, presumably, because he thinks it would be cool to win NBA championships. If he could throw some players an extra $100 million a year, that would cost him almost nothing — 6 basis points of his net worth — and he’d be able to build a really good team by outbidding everyone else for the best players. 

If you think about this for five minutes, you could come up with ideas to get around the cap. The simplest idea goes like this: The Clippers call up a star free agent and say to him “we’d like to hire you to play for us. We’ll pay you the maximum salary, $58 million per year, which is what like six other teams are offering you. But, each year, Steve Ballmer will also give you $50 million, out of his own personal account, as a Christmas present. We, the Clippers, will pay you $58 million a year, complying with NBA rules. But you will receive $108 million a year.” We talk about this basic approach — paying a low nominal amount to keep under some limit, and then throwing in some sort of extra Christmas present — all the time.

That’s too easy, though. The NBA thought about it, and the collective bargaining agreement — which sets the salary rules — prohibits it. [2]  

But, okay, you can do better. Basketball teams are big businesses, they do lots of business with other companies, and their owners do lots of business with other companies. Star basketball players are also big businesses and do lots of business — endorsement deals, investments, etc. — with other companies. There are all sorts of opportunities to exchange Christmas gifts. A few ideas:

1. The Clippers negotiate a sponsorship deal with some outside company that genuinely wants to spend $50 million a year putting its name on the Clippers’ uniforms or arena or whatever. The Clippers call up the company and say “instead of charging you $50 million, we will charge you $20 million. But please sign our star player to a $30 million endorsement deal that does not require him to do anything.” 
2. The Clippers buy a big electronic scoreboard from an electronic scoreboard manufacturer. The electronic scoreboard manufacturer sends the Clippers an invoice for $10 million. The Clippers call it up and say “instead of paying you $10 million for this scoreboard, we will pay you $40 million. But,” etc.
3. Steve Ballmer gets a call from a promising startup asking him to invest $100 million for a 10% stake. He calls up the startup and says “instead of investing $100 million for a 10% stake, I would like to invest $130 million for a 10% stake. But,” etc. [3]
4. Etc.

The collective bargaining agreement also prohibits all of these things, or tries to. A team or owner is not allowed to “enter into an agreement or understanding with any sponsor or business partner or third party under which such sponsor, business partner, or third party pays or agrees to pay compensation for basketball services (even if such compensation is ostensibly designated as being for non-basketball services) to a player.” [4]  I think that more or less covers all of my proposals. The NBA has thought of all of this stuff, and you’re not supposed to do any of it.

As it happens, though, the Clippers, did all of it? Allegedly? We talked about this last year, when journalist Pablo Torre published a report alleging that the Clippers paid their star player, Kawhi Leonard, more than the maximum salary for a few years by funneling money through an environmental startup called Aspiration Partners. Aspiration Partners was a weird tree-planting fraud run by a guy named Joseph Sanberg, who was sentenced to 14 years in prison for defrauding Aspiration’s investors in various baroque ways that we have also discussed. One of them was an accounting fraud in which it would (1) sell tens of thousands of dollars of vague tree-planting services to Colombian celebrities (??) and (2) pay those same Colombian celebrities those same tens of thousands of dollars for vague endorsement services, thus booking the tree-planting fees as revenue without any money changing hands. When Torre broke the Clippers/Aspiration story, I wrote:

I guess that if your main product, as a company, is fake endorsement deals, there are a couple of different markets that you can sell into. If you are generally in the business of paying for fake endorsements to create fictitious revenue, you might also get into the business of paying for fake endorsements to get around the NBA salary cap? 

Torre’s report led to an NBA investigation, and yesterday the NBA announced the results, penalizing the Clippers, Ballmer and Leonard for violating the rules. It also released a “Summary Report of Independent Investigators Concerning the LA Clippers and Kawhi Leonard,” written by the law firm Wachtell, Lipton, Rosen & Katz, [5] which is a delightful read and which seems to confirm Torre’s reporting and then some. 

Basically the story is that “Mr. Leonard’s uncle and business manager, Dennis Robertson,” wanted and expected the Clippers to pay Leonard more than his contracted salary by finding him endorsement deals:

Mr. Robertson communicated a target: he expected the Clippers’ assistance in obtaining approximately $10 million per year for Mr. Leonard. He communicated these demands primarily to [Clippers President of Basketball Operations Lawrence] Frank, but also to Mr. Ballmer and [Clippers President of Business Operations Gillian] Zucker. …

In April 2020, Mr. Robertson spoke with Mr. Ballmer and Mr. Frank to express his frustrations about what he perceived to be a lack of effort by the Clippers to facilitate off-court business opportunities for Mr. Leonard. According to contemporaneous notes kept by Mr. Frank … Mr. Robertson complained to Mr. Ballmer that Ms. Zucker was making “introductions” for “bull**** deals,” and that “I [Mr. Robertson] cant [sic] wait on [Ms. Zucker] - I have to get paid.”

So “Ms. Zucker made a series of email ‘introductions’ connecting Mr. Robertson to executives at three companies with which the Clippers were in active conversations about potential business relationships: Boingo (a provider of wireless and other communications networks), Daktronics (a manufacturer of scoreboards and video displays), and Lockton (an insurance brokerage).” Leonard quickly signed $18 million worth of endorsement deals with them. The deals “imposed minimal performance obligations on Mr. Leonard relative to the amount he was paid,” and “Mr. Leonard’s only confirmed activity under any of the agreements was a visit to a military base on a single occasion under one agreement and signing some memorabilia under another.”

The implication is that the companies did the deals, not to get Leonard’s services, but to get deals done with the Clippers. Or, really, not even to get deals done with the Clippers; just to get cash from the Clippers:

Within weeks following the “introductions,” either before or on the same day as the companies signed endorsement agreements with Mr. Leonard, each company entered into a multi-million dollar consulting agreement with the Clippers. …

A former executive of one of these companies told investigators that the consulting agreement entered into by the executive’s company and the Clippers was highly unusual, for at least the following reasons: (i) the company was not in the business of providing “consulting” services, (ii) the services contemplated by the consulting agreement were not worth the money the Clippers were paying for them and, indeed, were typically supplied by the company to clients for free in connection with other business, and (iii) it was atypical for the company to receive any portion of its fee in advance of providing at least some amount of services, and atypical in the extreme (as occurred here) for the company to receive virtually the entire fee in advance. …

These payments may in fact have been made principally to fund the endorsement deals with Mr. Leonard. Indeed, a credible witness with direct knowledge told investigators that the consulting agreement one company signed with the Clippers was in fact a ruse, designed and intended to be a vehicle for the team to provide the company with funds to be paid to Mr. Leonard.

“Consulting fees” is a decent euphemism for “Christmas presents.” You pay your business partners millions of dollars for some vague consulting, and they turn around and pay your star player the same millions of dollars for some vague endorsements.

Also apparently the Clippers bought a scoreboard from the scoreboard company at an agreed-upon price, and then increased the price to get more money to Leonard:

In the spring of 2020, and in response to a request-for-proposal process initiated by the Clippers, Daktronics began to compete to obtain a lucrative contract to supply digital scoreboard and signage technology at the Intuit Dome. In May 2020, the Clippers informed Daktronics that it was the team’s preferred provider for this project, but that the team wanted to agree on a “spend back” arrangement whereby Daktronics would provide some amount of business back to the Clippers — which Daktronics told investigators is not uncommon in its industry. Ms. Zucker thereafter suggested to a Daktronics senior executive that this “spend back” could be accomplished through an endorsement agreement between Daktronics and Mr. Leonard. …

In February of 2021, before the end of the first year of the Daktronics-Leonard endorsement agreement, the same senior Clippers’ executive approached Daktronics again. This time, the Clippers’ executive told Daktronics that, because the team had decided to increase the amount it would spend on the scoreboard, Daktronics should correspondingly increase the amount it would pay to Mr. Leonard. After some negotiation — and again based on its concern that failing to comply could jeopardize its business with the Clippers — Daktronics ultimately agreed to increase its second-year payment to Mr. Leonard by $2 million.

“We would like to pay you an extra $2 million for a scoreboard so you can pay an extra $2 million to our star player,” sure.

All of this is before the Aspiration deal, which apparently really did involve a round-trip where the Clippers paid Aspiration an arbitrary amount for vague tree-planting services and in exchange Aspiration paid Leonard a similar amount for vague endorsement services. Aspiration agreed to pay Leonard $7 million a year in cash (and $5 million a year in stock) for four years, an amount that experts found “extraordinarily high, especially in view of the limited obligations required of Mr. Leonard under the agreement and Mr. Leonard’s relatively insubstantial endorsement profile.” Sanberg apparently negotiated this deal, and other Aspiration executives didn’t like it:

“I have no idea why we’d do this,” wrote one senior executive; “this is not a good investment of our capital [. . . .] It’s $48M over 4 years for Kawhi, who is not a big name [. . . .] Not sure why we would make such a commitment considering we are already paying a huge sponsorship fee to Clippers,” wrote another.

But Sanberg explained “that ‘the Clippers are asking us to do this with Kawhi Leonard’ and that the team would provide additional business back to Aspiration to help offset the financial impact on Aspiration.” And it did:

In January 2022, Mr. Sanberg and Ms. Zucker texted and spoke about a potential sustainability services deal between Aspiration and the Forum, an Inglewood arena Mr. Ballmer had acquired in May 2020 and which Ms. Zucker oversaw — a stated purpose of which would be to “zero out” the Forum’s historical carbon emissions. According to Mr. Sanberg, these conversations followed his informing Ms. Zucker that Aspiration would not sign an endorsement agreement with Mr. Leonard unless it received business back from the Clippers. …

While the deal was described by the Clippers to investigators as an effort to “zero out” the historical carbon emissions of the Forum, it did not start with any meaningful analysis or calculation of those emissions. Instead, the initial draft of the deal’s term sheet from January 2022 contained a heading entitled “Business Back Opporutnities [sic]” and added “[t]o be filled in by Eric Chan ($7M back in business).” Thus, at the deal’s inception, it was contemplated that the Clippers would spend $7 million annually with Aspiration — the same amount as the cash portion of the Leonard-Aspiration endorsement agreement. ...

No Clippers witness could provide a credible alternative explanation for the initial appearance of the $7 million annual payment from the team to Aspiration in the Forum Agreement. Mr. Ballmer and Ms. Zucker both claimed that it was based on a study done by a team consultant who had determined that the Forum needed $28 million to offset its carbon emissions and that the team would pay Aspiration for these offsets over four years. But investigators spoke directly with this consultant, who said that the Clippers had given him a $28 million budget with which to address the Forum’s emissions — not the other way around.

“If we need to give this tree-planting company $28 million, how many trees will that buy us,” I guess. The Clippers didn’t really want the tree planting, Aspiration didn’t really want Kawhi Leonard’s endorsement, but the offsetting tree-planting-and-endorsement deals did allow $28 million to move from the Clippers to Kawhi Leonard. Which is what the Clippers apparently wanted.

Anyway the Clippers “vehemently reject the NBA’s findings, which are the result of a heavily biased investigation.” Maybe the $28 million was just for trees? The consulting fees were just for consulting?

There is a idea in corporate governance called “empty voting.” Let’s say you want to take over a public company. One thing you could do is buy a big chunk of its stock, launch a proxy fight to replace its board of directors, and vote your stock for your slate of directors. For reasons, you probably won’t buy a majority of the stock, so you will still need to get some other shareholders to back your slate. But if you own a big chunk of stock, you’ll have a better chance of winning your proxy fight, replacing the board, and putting yourself in charge. 

Another thing you could do is buy a big chunk of the company’s stock — say, 9.9% of the company [6]  — and short some of its stock as a hedge. If you are long 9.9% of the company and short 5.9%, you have more voting power (9.9%) than actual economic interest (4%) in the company. If you are long 9.9% and short 9.9%, you have a lot of voting power but no economic interest: You are indifferent to whether the company’s value goes up or down. That’s weird. What if you win your proxy fight? You’ll control the company, but you’ll have no economic interest in making its value go up.

If you are long 9.9% and short 19.9%, even weirder: If you win your proxy fight, you’ll control the company, and you’ll profit if you drive it into the ground. Much to think about.

“Empty voting” refers to this idea of being able to vote shares that you don’t economically own, having a lot of voting power over the company while having little or zero or even negative economic exposure to its share price. People worry about empty voting in sort of abstract ways, but as far as I can tell actual examples are uncommon. We talked a few times in 2024 about empty-voting allegations at Masimo Corp., but those allegations were more like “a hedge fund helped out its buddy the chief executive officer” than they are like “a hedge fund took over the company with empty votes to drive it into the ground.” Earlier in 2024, we talked a few times about a company called Shareholder Vote Exchange, which supposedly allowed people to buy shareholder votes without buying the underlying shares, but that shut down pretty quickly. There is a related concept of “net short debt activism,” which is like empty voting but with debt, and which might exist a little bit.

But you can see why people worry. Empty voting seems bad. If you could take over a company while being net short the company — if you could grab control by owning X% of the stock, while also being short 2X% — then you would have incentives that are very much not aligned with those of other shareholders. You could take over their company and drive it into the ground, hurting them, for your own profit. At the very least, if an activist in a proxy fight is doing some empty voting, you might think it should have to disclose that, so other shareholders, in deciding how to vote, can evaluate whether its incentives are aligned.

We have also talked a couple of times about an activist fight at Empery Digital Inc. Briefly:

1. In mid-2025, there was a vogue for DATs, digital asset treasury companies, pots of cryptocurrency with stock-market listings. For a while $1 of crypto traded for $2 on the stock market, so a lot of companies got into the business of selling stock at a premium to buy crypto.
2. Empery is a classic digital asset treasury company, or DAT: It was an electric motorcycle company that pivoted, at the peak in July 2025, to holding $500 million of Bitcoin and trading at a premium.
3. The DAT trade faded, and Empery — like other DATs — started trading below its net asset value.
4. Some holders of Empery stock figured that it should run the trade in reverse: Sell its Bitcoin to buy back stock at a discount, thus shrinking the company and closing the discount.
5. The company’s managers actually did some of that (selling Bitcoin, buying back stock), but some holders wanted it to do more and launched a proxy fight to try to replace the board of directors to accelerate the reverse-DAT trade.
6. Meanwhile the DAT trade is so 2025, and its 2026 equivalent is pivoting to become an AI infrastructure company, which Empery also did.

The main activist investor in Empery is a fund called ATG Capital Opportunities Fund LP, run by a guy named Gabi Gliksberg, which owns 16.3% of the stock. It launched a proxy fight to get its own slate of nine directors elected to Empery’s board. As part of this fight, it submitted a notice of its nominations to Empery’s current board, but the board rejected that notice and said that ATG’s nominees were not eligible to be elected to the board. ATG sued, and last week it won: A Delaware court ruled that “the nomination notice is valid and the investor’s slate will stand for election.”

For our purposes there are two interesting facts here. One is that, while the main activist investor in Empery is ATG, another big shareholder who has pushed Empery to sell its Bitcoin is a guy named Tice Brown. He is all over the court’s opinion, including here:

On February 3 … Brown emailed Bloomberg columnist Matt Levine, writing: “I’d like to liquidate a bitcoin treasury company. I’d like to speak publicly about it[.]” The next day, Levine published an article titled “Cracking Open the DATs,” which named both Brown and ATG. It described an arbitrage and liquidation strategy whereby an investor buys a stake in a DAT company that is trading at a discount to its NAV, and agitates for the company to liquidate its cryptocurrency to return the money to its stockholders.

Disclosure, I guess. (Here’s the column.) The other interesting thing is one of the reasons the board gave for rejecting ATG’s nominees. From the opinion:

The Rejection Letter cited three primary grounds for the Board’s decision, tied to provisions of Empery’s advance notice bylaws (the “Bylaws”). ... 

The Rejection Letter’s second main ground for rejecting ATG’s Nomination Notice was its failure to disclose a Bitcoin hedge. At the time ATG submitted the Nomination Notice, it had hedged its Empery equity position “dollar for dollar” to isolate and eliminate the risk of Bitcoin price movement by shorting Bitcoin ETFs. The defendants assert that this undisclosed hedge misaligned ATG with Empery’s long-only stockholders and incentivized ATG to push for the liquidation of the Company’s Bitcoin. 

The court rejected this reason, because Empery’s bylaws did not actually require any disclosure of Bitcoin hedges:

To determine what a stockholder must disclose, the court looks first to the plain text of the contract. Empery’s Bylaws are specific regarding the disclosure of economic hedges. Section 2.4(c)(ii) requires the disclosure of derivatives, synthetic equity, and short positions in Empery’s own stock. Some DAT corporations’ bylaws require the disclosure of commodity hedges. By contrast, neither Empery’s Bylaws nor its questionnaire for director candidates require the disclosure of commodity hedges, cryptocurrency hedges, or positions in unrelated ETFs.

Fine. But: Should it? Some DATs do “require the disclosure of commodity hedges,” where “commodity” means “crypto.” If you are long 10% of a DAT with 1,000 Bitcoins, and also short 100 Bitcoins as a hedge, is that empty voting? An ordinary unhedged shareholder in the DAT really wants Bitcoin to go up, because the DAT is after all just a pool of Bitcoins. But you don’t care if Bitcoin goes up or down: You are long shares in a pool of Bitcoins, and short Bitcoins. Are you “misaligned … with Empery’s long-only stockholders,” because they are long Bitcoin (via Empery) and you are not?

I suspect the answer is mostly no. Shareholders of Strategy Inc., the original DAT, might be Bitcoin true believers, [7] but as far as I can tell most copycat DATs were created largely for the benefit of arbitrageurs who wanted to capture the magic of selling $1 of crypto for $2. To me, and I suspect to Brown and Gliksberg and a lot of other DAT shareholders, a DAT is a premium trade: The essential investment thesis of a DAT is “this thing trades at a premium to its underlying Bitcoin,” the job of the DAT’s managers is to maximize the premium, and if they are not doing that then they are not aligned with shareholders and should perhaps be replaced. An investor who is long a lot of DAT shares and short Bitcoin has a large unhedged position in the central economic fact of the DAT, its premium, and is aligned with shareholders who also care about that premium.

But that’s just one way of looking at it, and the simpler and more intuitive view might be that a DAT is a pile of Bitcoins, the shareholders are long Bitcoins, and someone who is not long Bitcoins really should not be put in charge of a pile of Bitcoins.

Why was Jeffrey Epstein rich? The direct answer is that a handful of billionaires — Leon Black, Les Wexner — gave him a lot of money. But for what? The two main theories seem to be:

1. They were paying him for very high-end financial and tax advice; or
2. Bad reasons.

They gave him a lot of money, though, which raises the question: Was his tax advice all that good? A few years ago, Apollo Global Management did an investigation of Epstein’s relationship to Black, who was at one time Apollo’s CEO; that investigation concluded that, yep, the advice was really good. I wrote at the time:

Why was Epstein, who was not a lawyer or an accountant or a college graduate for that matter, so good at tax? I actually don’t have too much trouble believing this — in my experience, some people are just born with a natural gift for tax structuring, and need surprisingly little formal training to achieve their potential — but it is fascinating. Black would go his lawyers and say “hey my guy found this way to save a billion dollars in taxes, is it legal,” and the fancy lawyers in the Paul Weiss tax department would say “wow, sure is, this is amazing, why didn’t we think of this, this guy is a Michelangelo of tax minimization”?

That was maybe slightly sarcastic, but only slightly. Later — when I searched my own name in the Epstein files — I actually discovered an admiring email from a fancy Paul Weiss tax lawyer kind of confirming that view. 

Anyway today Bloomberg’s Dylan Sloan, Tom Maloney and Francesca Maglione have a story about the quality of Epstein’s tax advice. He thought it was really good, apparently:

“Leon, you hired me to produce a work of art. it was not inexpensive,” Epstein wrote in a May 2016 email. “the value far exceeds any other piece in your collection.- by FAR.”

Epstein’s art, as he saw it, was a complex plan to help Black minimize taxes. The cost: $158 million, which Black paid to the convicted sex offender between 2012 and 2017, and which Epstein himself found difficult to justify in itemized fashion.

“I guess the value is in the eye of the beholder,” he wrote in an email. “It reminds me of those people looking at a modern art piece and saying ‘my child could do that.’”

Maybe, like, the Mark Rothko of tax minimization. Other experts are less impressed:

“On the surface, the only remarkable aspect of this planning is the price tag,” said Victoria J. Haneman, a University of Georgia law professor specializing in tax issues and estate planning. “You could probably employ all of the top law firms in New York at the same time on the same estate plan and not hit $150 million” in fees….

Jay Soled, a Rutgers accounting professor, said it was “hogwash” for Epstein to suggest his strategies were novel. Most of the tactics were “just-run-of-the-mill stuff that people do.” …

Haneman noted that while there were instances in which the documents show execution strategies that were slightly aggressive, the overall architecture of the estate was rather conventional.

“My child could do that,” several tax professors almost actually said.

Fund Backed by Trump Sons Sold Stocks After Media Attention Drew Investors. Nvidia Buys AI Platform Hugging Face for $13 Billion. Netherlands Moves Gold From New York to London, Citing Geopolitical Unrest. JPMorgan curbed lending to Jane Street as trading firm muscled into bond market. Why Regulators Are Scrutinizing Prediction Markets’ Trading Arms. Blackstone’s BCRED Caps Redemptions Again After 10% Seek to Exit. KPMG warned Guggenheim unit over deficiencies in internal controls. Mark Walter’s Insurers Ramped Up Borrowing From Home Loan Bank to $6 Billion. Highlights From the Messy Divorce Trial of a Hedge-Fund Titan. Leon Black Sues House Committee to Block Epstein Subpoenas. New York’s Top .001 Percent Have Grown Even Richer. More Rank-and-File Workers Are Getting Paid in Company Stock. Can You Air-Condition a City? James Dyson “spent six years inventing an AI-powered toothbrush that promises to floss teeth at the same time as brushing them.”

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Let’s say you own stock in a company that has announced that it will pay a $2 per share dividend to anyone who holds the stock on Wednesday. On Tuesday, the stock closes at $100 per share. If you buy the stock on Tuesday, you receive it on Wednesday — this is called “T+1 settlement” — so you are a shareholder as of Wednesday and get the dividend. If you buy the stock on Wednesday, though, you don’t actually get the stock until Thursday, so you miss the dividend. (Wednesday is the “ex date,” the first day on which you don’t get the dividend if you buy the stock. [1] ) In theory, ceteris paribus, the stock should trade at $98 on Wednesday to reflect the fact that you don’t get the dividend. The stock’s price on Tuesday represents (1) the value of the stock plus (2) the $2 dividend; the price on Wednesday represents (1) roughly the same value for the same stock plus (2) no dividend. So $100 on Tuesday, $98 on Wednesday. Ceteris paribus.

Many countries tax dividends in ways that are bad for foreign investors. In the US, for instance, many foreign investors face a 30% withholding tax on dividend income. And so there is a large financial-industry subculture of finding ways to transform “dividends” into something else that gets better tax treatment. Many of these trades are done with swaps or other derivatives, but here is a very simple one:

1. You are a foreign investor, you own a share of stock, it’s going to pay a $2 dividend, you do not want to “receive” the “dividend” for tax reasons, but you don’t want to give up the $2 either.
2. On Tuesday at 11:59 p.m., just before the dividend ex date, you sell your stock for $100.
3. On Wednesday at 12:01 a.m., you buy it back for $98, and keep the other $2.
4. Now you have the same share of stock that you had before, plus $2.
5. Your $2 is not, however, a dividend. It’s just the result of selling stock at one price and buying it back at another. It’s just capital gains, which are not subject to withholding tax.

That’s pretty good: You sell the stock just before it pays the dividend, and buy it back just after. In theory, ceteris paribus, the stock will go down by $2 — exactly the amount of the dividend — in the two minutes between when you sell and when you buy it back, so your sale price will exceed your repurchase price by exactly the amount of the dividend. You will cash out the amount of the dividend, but it will not be a dividend.

This trade is not quite practical, though, because you can’t really sell your stock at 11:59 (with the dividend) and buy it back at 12:01 (without the dividend). Liquidity is bad at midnight, and even 24-hour-ish stock markets take little breaks to update their computers, so this trade might be difficult to execute. More important, stock trades in the overnight session do not exactly settle T+1, and if you wait until 11:59 p.m. to sell you will still get the dividend. [2]  

So the realistic version of this trade is more like “on Tuesday at 4 p.m., you sell for $100, and on Wednesday at 9:30 a.m., you buy it back at $98.” Here, still, ceteris paribus, you will be cashing out the amount of the dividend, but ceteris are less likely to be paribus. A lot happens overnight! In fact, famously, approximately 100% of stock-market gains occur overnight. Therefore, if you sell your stock on Tuesday at $100 per share, it will probably open on Wednesday at, like, $98.50 or $99 per share. (Empirically, stocks normally do not drop by the full amount of the dividend on the ex date, in part because stocks mostly go up overnight and in part, probably, for dividend tax reasons.) You will not keep the full $2 for yourself. You will miss out on some of the value of the dividend, because you will not hold the stock for 17.5 hours.

For S&P 500 exchange-traded funds, though, there’s a much better trade, Bloomberg’s Zachary Mider and Denitsa Tsekova report:

Investors hopscotch between BlackRock’s iShares Core S&P 500 ETF (ticker IVV) and Vanguard’s S&P 500 ETF (VOO) so they hold neither fund on the date that it confers a right to receive a dividend. Thanks to the way stock prices move around distribution dates, the investors don’t miss out on any of the return from the dividend. Instead, they effectively take the value of the payout in price appreciation — which isn’t taxable for foreigners. …

Flipping from IVV to VOO and back probably saved foreign investors about $147 million in US taxes last year, according to calculations based on the dollar value shifting between the two funds each quarter. …

The switching trade relies on the way ETFs handle dividends from the stocks they hold. The members of the S&P 500 pay cash dividends to shareholders on various dates. Index funds accumulate that over time and pay it to investors once a quarter. The S&P 500’s annual dividend yield is just over 1% of its market value.

To avoid receiving a fund’s dividend, investors need to have sold their position by market close on the day before the ex-dividend date — the point for each ETF when its shares begin trading without the right to receive the upcoming payment. Since 2024, BlackRock’s ex-dividend dates have been consistently earlier than those of Vanguard, making it easy to toggle between the two providers.

That is: You hold IVV until a day or two before its ex-dividend date. Then you sell it, like, mid-day on Tuesday and simultaneously buy VOO, so that you have no slippage: You hold S&P-500-ETF-in-some-form continuously. [3]  The IVV ex-date arrives, and IVV starts trading lower than VOO: For a few days, VOO trades with a dividend and IVV trades without, so VOO trades higher than IVV by about the amount of the dividend. So you sell your VOO (high) and instantaneously buy back IVV (low), again continuously holding S&P 500 exposure with no slippage. When you sell your VOO, you get a higher price than you pay to buy back the IVV. Which is a dividend, economically, but not for tax purposes.

Mider and Tsekova write:

The switching trade adds to the growing list of techniques that Wall Street and its major investors are deploying to minimize tax bills. While many such tactics rely on a special loophole that helps ETFs defer capital gains, this one is simpler, depending merely on the existence of multiple large, liquid, nearly identical funds. … The switching trades are unrelated to the so-called heartbeat transactions that play a key role in the ETF industry.

We have talked a few times about the fact that an ETF is approximately an investment vehicle that doesn’t pay taxes, and that this technology has been so broadly generalized that maybe one day no investors will have to pay taxes. But this isn’t that; this has nothing to do with that. This is just trading around ex dates. This is just: If you can sell a share of stock before it pays a dividend, and buy an otherwise identical share of stock that pays the same dividend on a different date, you never need to pay dividend taxes.

The economic model of law firms is something like this:

1. Clients want to hire certain lawyers — partners at big law firms — for their judgment, skill and experience.
2. The top partners at top law firms can make $20 million a year or more.
3. If you want to hire a top partner at a top law firm to work on your bet-the-company deal or litigation, you have to pay the market rate for her judgment, skill and experience.
4. But, for reasons of history and tradition, you don’t quite do that.
5. Instead, traditionally, lawyers bill by the hour. You pay an hourly rate for her time, which might be as much as $4,000 for a partner at a big firm.
6. If you divide $20 million by $4,000 you get 5,000 hours a year, or about 100 hours a week. The top partners at top law firms do not bill their clients 100 hours a week. [4]  That would be tiring.
7. The price, to a client, of hiring a top lawyer is not really her stated hourly rate times the number of hours she works on your legal problems.
8. Instead, the price, to the client, is some multiple of her stated hourly rate.
9. The way the multiple gets computed is with associates. Instead of buying 10 hours of the senior partner’s time for $40,000, you buy 10 hours of her time plus 200 hours of time from the associates — more junior lawyers — who work for her and help her analyze your legal problem, review your documents, etc.
10. The associates bill out at like, $1,000 an hour or whatever.
11. In total you pay $240,000 for 10 hours of the partner’s time plus 200 hours of the associates’ time.
12. Out of that $240,000, the partner pays the associates some money but keeps most of it for herself, putting her on track to earn $20 million a year. 
13. For your $240,000, you get 10 focused hours’ worth of judgment, skill and experience from a top lawyer at a top law firm.
14. Plus those associates are definitely reviewing some documents!

This is exaggerated, of course; in fact the law firm associates do lots of good and useful work for their clients. (I was one, once, and I did!) But the basic point is that law firms are tied to the billable hour, and the senior partners’ time is underpriced, both in the sense that clients would (and do!) pay more for it than their hourly rates, and in the sense that the senior partners take home a lot more than their hourly rates times the number of hours they work. Whereas the associates’ time is overpriced, both in the sense that the clients wouldn’t pay the associates’ hourly rates just for the associates’ work, and in the sense that the associates take home a lot less than their hourly rates times the number of hours they work. Billing out the associates’ time is a way to capture more value for the partners.

Now there is artificial intelligence, which can, perhaps, do a lot of the work — document review, drafting, legal research, etc. — that associates do. You could just about imagine a law firm of only senior partners, with the associates’ work done by AI. But how would that firm bill? How would the partners make $20 million a year? You could imagine some answers:

• The partners could bill $25,000 per hour. “AI has made us more efficient, so our rates have gone up.”
• The firm could give its AI agents cutesy names and bill them out as associates. “Your bill is $40,000 for 10 hours of partner time, plus $200,000 for 200 hour-equivalents of token use by Legalina, our AI system.”
• Flat fees. “I’ll apply my judgment, skill and experience, plus some AI, to your legal problem for $250,000.”

Those answers all assume that the market rate for the senior lawyers’ judgment, skill and experience is $20 million a year, and that the current system of associates and billable hours is a convoluted but functional system for paying them their actual market value. Perhaps that assumption is wrong, or will be rendered wrong by AI. Some other answers might be:

• The clients type their legal questions into ChatGPT, get perfectly serviceable answers and pay their lawyers $0. [5]
• The clients say “I was paying you $240,000 for a combination of your judgment/skill/experience and your associates’ grunt work, but now you can do the grunt work cheaply with AI so I’ll only pay you $100,000 for that combination.”

Etc. Here’s a Financial Times story about the problem:

Wall Street banks are pushing large law firms to cut fees, arguing that the business model that has enriched top lawyers for decades is not sustainable in an era of AI. ...

The pressure from some of Big Law’s most valued clients could help overturn the financial model at the heart of the legal industry. Under the so-called leverage model, firms maximise profits for equity partners by billing hourly for work done by large numbers of more junior lawyers, often working late into the night at rates that far outstrip the cost of their salaries.

Much of that work, such as research, document review, assessing contracts and trawling through litigation discovery, can now be done far more quickly using AI. … 

Top lawyers have “for a long time been compensated on the foundation of [associates billing for long hours],” Eric Grossman, Morgan Stanley’s general counsel, told the FT. “Their compensation model is now extraordinarily unstable.”

The ability to complete tasks more quickly could mark “a fundamental altering of the revenue foundation for these mega firms”, he said.

Grossman said the bank was willing to continue to pay large sums for the judgment and talent of the best lawyers, but that by the end of this year most external legal work would be tendered through competitive bidding processes and paid for using alternative arrangements such as fixed fees. 

One possibility is that AI will give the top lawyers more leverage: Instead of working on 20 matters with 20 different associates and making $20 million a year, they can work on 200 matters with 200 different AI instances and make $200 million a year. But probably there will be some pricing pressure.

We talked on Monday about hedging KPIs. My basic points were:

1. Companies (and college football teams) want their executives (and coaches) to do good stuff like increase revenues and profit margins (and win football games)..
2. Companies (and college football teams) therefore offer executives (and coaches) contingent compensation packages that pay them big bonuses if they hit certain key performance indicators (like revenue of $X, or profit margins of Y%, or winning Z football games).
3. In a narrow sense, this exposes the companies (or teams) to financial risk: Paying the bonus costs more than not paying the bonus. Perhaps the companies (or teams) should hedge this risk.
4. But not really: You pay the bonus because achieving the KPI is good, for the company (or the football team). Bringing in more revenue or having a higher profit margin is correlated with increasing shareholder value. Winning more football games is also correlated with good economic outcomes for the university: If you win more games, you will recruit more paying students and get more alumni donations and sell more merch and so forth.
5. Therefore it’s just weird to hedge the bonuses, you know? Hedging the bonuses — betting on your company or team to achieve some KPI — is not a hedge; it’s doubling down on your essential business risk. 
6. Nonetheless it appears, from trading on Kalshi, that at least some college football teams hedge their coaches’ bonuses for winning.

Several readers pointed me to a possible explanation, at least of the football thing. When a company pays an executive a bonus for hitting a KPI, the company (1) gets the benefit of the KPI and (2) pays the bonus. College football financing is weirder, though, and it is approximately true that:

• When a college football team wins the national championship, the school benefits (higher enrollment, TV contracts, merch, donations, etc.); but
• When a college football team wins the national championship, a consortium of rich alumni boosters might be on the hook to pay some or all of the coach’s bonus.

From the school’s perspective, this is incentive-aligned and there is no need to hedge. From the boosters’ perspective, though, paying a $3 million bonus really does cost more than not paying it. They might be thrilled to pay it — they got into the boosting business because they want to win a national championship — but it still might hurt a little, and they don’t really share in the school’s extra revenue. Perhaps they should hedge, and perhaps they do. [6]

Back in the glory days of financial blogs, there was one called Long or Short Capital. In 2006, it published a post under the byline Johnny Debacle, with the title “Four Simple Steps to Becoming a Billionaire.” I still think about it from time to time. Here are the steps, in Debacle’s words:

1.. Take outrageous risks with extremely high upside.

2. Be the 1 out of 500 million for whom it pans out. This one is key so focus on it.

3. Attribute your wealth creation to your own hard work, your own genius and the power of your business plan. Be sure to stress how your wealth was singularly made possible by your unique endowment of elbow grease, street smarts, common sense, all of which your competitors obviously lacked (proven by how poor they are compared to you).

4. Buy a mega yacht and/or athletic team.

Yesterday Victor Haghani, James White and Jeffrey Rosenbluth of Elm Wealth sort of formalized this approach in a post titled “Do You Really Want to Be a Billionaire?”:

Say Billy expects to earn about $10 million after tax over his career, and save 20% of it, or $2 million. Let’s say he has 50 years to turn that $2 million into $1 billion, a 500x increase.

Now suppose his investment opportunity set looks like this: he can flip a coin 50 times, and each flip has a 60% chance of landing on heads. On each flip, he can bet whatever fraction of his wealth he chooses. We’ll explain later why this is a reasonable, even generous, stand-in for real investment choices.

What’s the best strategy, and what probability of success does it give him? It’s a tricky problem, but it was actually solved back in 1961 in a somewhat obscure paper by UC Berkeley professor Leo Breiman. The answer: with the optimal strategy, Billy can achieve a 7% chance of becoming a billionaire.

Seven percent sounds pretty good. Only about 0.001% of Americans are billionaires, and probably only around 0.05% of people who’ve managed to save $2 million ever get there. A 7% shot is a huge improvement.

But look at the other 93% of outcomes. In those cases, Billy doesn’t just fall short of a billion – he goes broke. That’s because the strategy that maximizes his odds of hitting $1 billion requires him to routinely bet 100% of his wealth, with also means he’ll routinely go broke. Every path that doesn’t end in a billion ends instead with an all-in bet that loses. There’s no soft landing; there’s no “I’m almost a billionaire.”

This is, like the Long or Short Capital post 20 years ago, obviously a stylized depiction of the problem. Nobody is actually offering you coin flips; you need to supply elbow grease, street smarts etc. to find and execute the coin flips. 

Still it does seem to capture something about at least one path to billions, one that comes up sometimes around here. A few years ago we spent a fair amount of time talking about Sam Bankman-Fried’s approach to positive-expected-value coin flips. Literally: When he was an intern at Jane Street, he went around betting on literal flips of literal coins in ways that were perhaps revealing. But also figuratively: Bankman-Fried did become a billionaire by taking outrageous risks with extremely high upside, but then he kept flipping metaphorical coins and oops. “Everything about Sam Bankman-Fried’s life was perfectly optimized for becoming a famous billionaire and an infamous criminal defendant, in that order,” I wrote.

And just this week, we talked about Bankman-Fried’s and his friends’ investments in Anthropic. I wrote that “Anthropic seems to have raised a lot of money specifically from [Effective Altruism] types who love (1) worrying about AI safety and (2) otherwise taking every positive-expected-value bet that comes their way. … For a while everyone was just out there flipping coins for vast wealth or ruin.” That approach definitely appeals to some people.. And works for some of them.

Chevron to Invest $7 Billion Doubling Its Venezuelan Output. Volkswagen Fights Chinese Competition — and Its Own Board — in Battle to Survive. S&P Weighs Multibillion-Dollar Spinout of Data Platform Capital IQ. Nvidia Nears $14 Billion Hugging Face Deal This Week. The Rapid Expansion That Built Bally’s Is Now Under Pressure. Uber to Cut 10% of Jobs, or 3,300 Roles, to Slash Bureaucracy. College M&A Tool Helps Schools Connect in Grim Higher Ed Era. German industry pushes to increase working week to 40 hours. Parents Push Teens to Start Investing Earlier Than They Did. MrBeast's ‘God King’ problem. “There was no existing blueprint for becoming a toddler techno DJ.”

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Amazon.com Inc. sells advertising on its site: If you search for something on Amazon, some of the results you see are “sponsored listings,” that is, advertisements from companies that want to sell you their products. Approximately speaking, the way Amazon sells those ads is with a second-price auction [1] : Advertisers submit sealed bids for each sponsored-product slot, the highest bidder wins, but the price that the winner pays is a tick more than the second-highest bid. So if there are three bidders who bid $10, $8 and $6 for a spot, the bidder who bids $10 wins, but it pays $8.01, a penny more than the second-best bid. [2]  Second-price auctions are the normal way to sell online advertising, and the theory is that they give bidders better incentives to bid their real valuations than a normal auction where the winner pays the highest bid.

How well this auction works depends on how many bidders there are. If 100 companies bid for each advertising spot, the auction is probably pretty competitive, the second-highest bid is probably pretty high, and you get an efficient auction whose price reflects the value of the ad. On the other hand, if only like two or three companies bid, you might get some inefficient auctions where the clearing price is pretty low.

This comes up because Amazon does not actually sell ads to the highest bidder. In showing users sponsored listings, Amazon considers two factors: (1) who will pay the most for the ad and (2) how “relevant” the ad is to the user’s search. When a user searches for “paper towels,” she wants to see results for paper towels. It might be more lucrative to show her advertisements for, you know, Ponzi schemes; advertisers might be willing to pay more to show her non-paper-towel ads than they would to show her paper-towel ads. But that would be bad for Amazon’s business overall: Users would get annoyed if their search results were always wrong. Amazon says:

Our early approach was simple: advertisers enter a bid, and if they win, they would pay just enough to beat the next highest ranked ad. Our auctions took relevancy into account to a degree, but they were much more weighted toward the highest bid amount.

While we could have decided to continue to favor the higher bids, we instead chose to focus on more relevant bids to ensure the best possible shopper and advertiser experience. As our advanced machine learning-based relevance models more heavily weighted relevance versus highest bid, we saw winning bids drop significantly. That was good for advertisers and shoppers but meant premium placements in our Store were being undervalued.

The rough intuition is: 100 companies were willing to pay for ads on each search, which produced a price that Amazon liked, but only 2 or 3 of them were advertising relevant products, and those were not always the highest bidders. Amazon, over time, decided to prioritize the relevant products: Crudely speaking, it ignored the irrelevant bids and conducted an auction only among the relevant ones. This produced fewer bidders, less competitive auctions and lower winning prices. 

This is a problem, for Amazon: “Premium placements in our Store were being undervalued.” Here is a conceptual solution to this problem: Add one more bidder to the auction. The one extra bidder is Amazon itself, wearing a fake mustache, and it bids “what we estimate to be the true market value of the ad placement.” Where does it get that estimate, if not from the auction price? Shh, never mind, machine learning. [3] Anyway now instead of, say, three bidders, there are four. If Bidder A bids $10 and Bidder B bids $2 and Bidder C bids $1.50, but Amazon thinks the “true market value” is $9.50, then Bidder A wins the auction with a best bid of $10, and Bidder Amazon-in-a-Mustache comes in second with a bid of $9.50. So Bidder A pays $9.51, a penny more than the second-best bid, rather than $2.01, a penny more than Bidder B’s bid.

Arguably this is still a second-price auction: The highest bidder still wins, and still pays the second bidder’s price. It’s just that there’s an extra bidder, Amazon itself, that sometimes provides the second-best price. And that second-best price is sometimes higher than the second-best outside bid.

But what if it’s higher than the first-best outside bid? Like: Bidder A bids $9, Bidder B bids $2, Bidder C bids $1.50 and Amazon’s bid (the “true market value”) is $9.50. Amazon wins the auction, but of course Amazon doesn’t want to win the auction: It wants to sell ad space to a buyer, not keep it for itself. So the actual result is that Bidder A wins the auction, as the highest outside bid. What price does it pay? Well, the second-highest bid. The highest bid is $9.50 (Amazon’s), and the second-highest is $9 (Bidder A’s). So bidder A pays $9, its own bid.

That’s kinda weird. Like: If you are bidding in a second-price auction, and you bid $9, and you hear back “good news, you won, that’ll be $9 please,” you might be puzzled. You might say “no, it’s a second-price auction; if I won with a bid of $9, that means I was first-best, and I should pay the second-best price. Why am I paying my bid?”

Here is where it is perhaps relevant to mention that my description is vastly oversimplified and, as it were, slowed down. These auctions happen bajillions of times per day, electronically, every time someone runs a search on Amazon. No advertiser is submitting a bid package and getting back a result and comparing its bid to the price it pays. It’s using algorithmic tools to automatically submit lots of bids, paying some aggregate cost for the ones it wins and getting a bill at the end of the month. [4] It might never notice that it’s winning the auction and paying its winning bid.

Here’s how Amazon describes this system:

With relevant ads increasingly winning at prices below market value, we began to test a concept called “soft reserve prices,” a real-time minimum value that seeks to better reflect what each placement is actually worth. We also introduced what we call a “hard reserve,” which is the minimum a bid must surpass to enter an auction. The hard reserve helps cover our costs whereas soft reserves represent what we estimate to be the true market value of the ad placement. Reserves like these are common across the industry.

Our auction looks at a combination of which ad is most relevant to the customer and the price an advertiser is willing to pay. Here’s how it works: Advertisers bid a maximum price for a placement. When the winning advertiser’s bid exceeds both the hard and soft reserve, they pay the soft reserve, which is less than they were willing to pay. When the winning advertiser’s bid exceeds the hard reserve but doesn't meet the soft reserve, we still grant the placement to that advertiser and they pay their bid. In no scenario does an advertiser pay more than their bid. …

With this approach, in 2024, approximately 92% of selected Sponsored Products ads were not the highest bid, often by a wide margin. The mean winning advertiser’s bid is typically about the 128th bid by amount. This means the winning advertisers' cost is almost always lower than if we had selected ads on bid alone.

The “soft reserve” is what I have described as the Amazon-in-a-mustache bid, the second-best (or sometimes first-best) bid submitted by Amazon itself. If the winning bidder is higher than Amazon’s bid, it pays the second-best price (i.e. the soft reserve); if it’s lower, it pays its own bid.

Here, on the other hand, is how the US Federal Trade Commission describes it:

[Yesterday], 22 states joined the Federal Trade Commission in filing suit against Amazon, alleging that the company engaged in deceptive and unfair practices that secretly inflated prices in its online search advertising auctions. The complaint alleges that, for over seven years, Amazon has covertly and substantially increased the prices that more than one million brands and sellers were required to pay to advertise on its platform. As a result, the complaint alleges that Amazon’s scheme has likely extracted tens of billions of dollars from its unwitting advertising customers. …

As described in the complaint, Amazon has represented to prospective advertisers for years that Amazon runs “second price” auctions where the winner of the auction would only pay “one cent more than the next highest bidder” for each successful bid for an advertising keyword. However, in practice, the complaint alleges that Amazon has charged its Sponsored Products advertisers their own winning bid close to 80% of the time and has effectively converted its nominally “second price” auction into a first price auction. …

Amazon told advertisers it ran a GSP [generalized second-price auction], but for years its auction pricing had “a surcharge hidden in it,” in the words of one internal Amazon document. The complaint alleges that, beginning in 2019, Amazon changed its auction rules without notice by adding an undisclosed surcharge that Amazon referred to internally as a “soft reserve price.” This resulted in advertisers paying substantially more than the price determined by the GSP auction. 

The FTC and 22 state attorney generals sued Amazon yesterday. Here is the complaint. The theory is that Amazon lied to advertisers about how its auctions work: It told them that it was running a second-price auction, which led them to submit relatively high bids, but actually it “has charged its Sponsored Products advertisers their own winning bid close to 80% of the time and has effectively converted its nominally ‘second price’ auction into a first price auction.” If you say “we’ll charge you the second price,” and actually charge the highest price, that does seem deceptive. 

Amazon disagrees, for kind of an interesting reason. You and I and the FTC can sit around and talk about a schematic idealized version of how Amazon’s ad auctions work, a version in which advertisers submit bids and Amazon picks the highest bidder and then either does (good) or does not  (fraud) charge the highest bidder the second-highest price. But in the real world, Amazon’s ad allocation process is a gigantic machine-learning-driven optimization problem designed to optimize both relevance and revenue, a process that runs entirely electronically many times per day and that is functionally a black box to everyone involved. “Advertisers often do not even know the amount of their bids because they adopt tools provided by Amazon to adjust their bids automatically,” says the FTC complaint, which kind of undermines the argument that Amazon’s disclosures tricked them into making higher bids. Amazon says:

The FTC claims advertisers were harmed because they didn’t understand how our auction worked and therefore overpaid. Not only do we properly describe our pricing and auctions to advertisers, but this claim fundamentally misunderstands how advertisers behave. Advertisers adjust bids based on real-world outcomes, not descriptions of auction mechanics. …

As a customer-centric advertising company, our advertising team, including senior leaders, communicates with advertisers on a regular basis and it’s not unusual to simplify a description in these instances.

Back to how advertisers really form their bids, ad buyers today use highly sophisticated, automated, programmatic advertising platforms that have a deep understanding of how auctions work across different providers, enable bid experimentation and are used to maximize results and return on investment.

Reserve prices are common in the industry and our use of them is consistent amongst industry leaders. The presence of reserve prices actually doesn't change how advertisers bid in practice. Advertisers optimize their campaigns based on actual auction outcomes — what they pay, what they win, and the performance they see. They don't bid based on simple descriptions of auction format. Even if an advertiser wanted to factor reserve prices into their strategy, they couldn't easily do this because reserves are determined in real time and aren't predictable in advance by anyone, including Amazon or the advertiser.

Advertisers of all sizes actively manage their bids using a range of tools and data. Many use automated bidding tools from Amazon or third-party services to manage campaigns based on real-time performance data. These tools monitor clicks, purchases, cost per click, and return on ad spend, and automatically adjust bids to get the best results. 

Amazon’s basic case is that it charges advertisers prices, overall, that are pretty good, and gives them a pretty good return on their advertising spend and pretty good visibility into their results, and advertisers can look at their overall spending and revenue and be satisfied, and that the market microstructure of all of this is Amazon’s own black box that it continually tweaks and is really nobody else’s business. “Simple descriptions of auction format” might be helpful to give new customers an intuition about how the auctions work, but they are not legally binding, and everyone understands that the reality is messier and more proprietary.

I come to this from the financial industry, where things are different. The auction rules of, say, a stock exchange tend to be fixed and binding and subject to regulatory approval, and when a platform also operates its own secret bidder that is often scandalous. [5]  “We use a machine-learning-based model to match buyers and sellers in the optimal way, and our users trade based on real-world outcomes, not descriptions of auction mechanics” is not a standard argument in financial markets, the way it (apparently) is in internet advertising.

I wonder if we’re heading that way, though. The process for matching buy and sell orders on the stock exchange, which has its roots in a time when the stock exchange was a physical location, is just about graspable by humans. The processes for matching online advertising orders on Amazon are more complicated and “aren’t predictable in advance by anyone, including Amazon.” As finance becomes more AI-driven, perhaps that will be the future of markets.

Here’s a weird timeline. GoPro Inc. is a camera company that has had a rough few years; as of last week its stock price was around $0.60 per share, for a market capitalization of about $110 million. By early July, a YouTuber named Mark Fischbach (who goes by Markiplier on YouTube) had bought 13.5 million GoPro shares, about 8.5% of the company. He disclosed that stake in a filing with the US Securities and Exchange Commission on Aug. 20. This disclosure does not seem to have caused much of a ripple at the time.

This past weekend, Bloomberg’s Lucas Shaw published a newsletter with the title “YouTube Star Markiplier Is Now GoPro’s Largest Shareholder,” containing an interview with Fischbach. As far as I can tell, this is how the market noticed Fischbach’s position (which, again, he disclosed on Aug. 20). The stock, which closed at $0.5999 on Friday, went up over the course of the day yesterday and closed at $0.8762, a 46% one-day gain. 

Small beaten-down consumer-facing company, significant short interest, new (or newly noticed) big investment from a celebrity, stock ripping: This all sounds like a meme stock. “A YouTube star is GoPro’s biggest shareholder, and meme stock traders are loving it,” Business Insider reported this morning. “GoPro Stock Rockets 140% as Markiplier Money Meets a Short Squeeze,” reported Benzinga. Fine. 

But then this morning GoPro announced a merger?

GoPro, Inc. (NASDAQ: GPRO) and Starman Optical, Inc. ("Starman"), a privately held optical-photonics company, today announced that they have entered into a definitive merger agreement. In connection with the proposed transaction, GoPro shareholders will receive an aggregate cash payment of $285 million, or $1.14 per share, subject to potential adjustment based on GoPro's net working capital at closing and will maintain ownership of approximately 10% of the outstanding shares of the Company. GoPro's outstanding debt of approximately $92 million will be repaid in full at closing, resulting in a clean, substantially debt-free balance sheet. 

Okay? The stock was at about $1.31 per share as of noon today, which is higher than the cash merger price, though I suppose that reflects the fact that current shareholders will keep 10% of the company. “The company said it was exploring strategic alternatives in May,” noted Shaw, so it is perhaps not quite a surprise that it announced a merger today. 

Still, odd timing. Like, by this point there is a traditional playbook for a public company that suddenly becomes a meme stock. Mostly you sell stock, in an at-the-market offering or maybe a floating-strike convertible. I suppose doing a merger is a version of that: “Oh, our stock is shooting up, we should sell 100% of it to an acquirer.” [6] I don’t think I’ve ever seen that version of the playbook executed so quickly: GoPro became a meme stock on Monday and announced that it was being acquired on Tuesday.But surely GoPro didn’t call up Starman Optical yesterday to propose this deal; surely this deal has been in the works for a while. In that sense, becoming a meme stock yesterday was arguably inconvenient. When GoPro was trading at $0.60 per share and drifting downward, selling for $1.14 or $1 or whatever the deal price was last week would have been relatively easy. Once you’ve got a meme-stock rally, it might be harder to strike a deal: An acquirer will only want to pay the fundamental value of the company, but the meme shareholders are not similarly constrained. if GoPro had traded to $10 per share (!?) on Markiplier’s investment, it would have been hard to get a deal done: Meme shareholders’ expectations would outstrip a buyer’s willingness to pay. Once GoPro became a meme, getting the deal done became more urgent.

Bloomberg’s Ira Boudway has a story about a gambling addict who relapsed into gambling on Kalshi. (“This is a cherry-picked case,” says Kalshi.) We have frequently discussed the weird situation that Kalshi is (1) obviously an online gambling site but also (2) a federally regulated financial market. Federal financial regulators care about stuff like market manipulation and fraud and insider trading, while state gambling regulators care about stuff like consumer protection and gambling addiction. Kalshi’s federally regulated status means (probably!) that it is exempt from the state rules designed to limit the harms of gambling addiction.

But Kalshi is an online gambling site, and would like to mitigate the harms of gambling addiction for altruistic or marketing or political reasons, so it sort of voluntarily follows some gambling-site best practices. (Not all! It offers gambling to 18-year-olds, for example. And in Utah, which prohibits gambling, and where Boudway’s protagonist lives.) Boudway writes:

The company offers a variety of risk management tools, the spokesperson says, including allowing users to temporarily restrict their own activity, self-exclude and set deposit limits. Kalshi maintains a nationwide list of users who have self-excluded, the spokesperson adds, and has asked state gaming regulators to share their opt-out lists, so far, it says, to no avail. It also limits potential losses on each market and works with the National Council on Problem Gambling and with addiction treatment providers to help address irresponsible behavior.

“We’ve prioritized making Kalshi the safest venue for people to trade on,” the spokesperson says.

“Has asked state gaming regulators to share their opt-out lists” is the weird one there. Like: State gaming regulators try to make Kalshi comply with their rules, and Kalshi says “nope, we’re not a gambling site, your rules don’t apply.” And then it turns around and says “hey can you send us your list of self-excluded problem gamblers so we can responsibly exclude them from our gambling site,” and the states are understandably confused.

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We talked last week about a proposal by Cboe Global Markets Inc. to list binary options on key performance indicators (KPIs). A “key performance indicator” is a measurable value that shows how well an organization is achieving its goals. Cboe wants to list options on KPIs for public companies, things like net income or revenue or, in the case of a car company, how many cars it makes. These KPIs tend to demonstrate how well the company is creating shareholder value, and so they correlate with stock price. Not perfectly — a car company can make more cars in a way that is bad for shareholders — but as a rough approximation.

We also talked about KPIs for public companies that happen to be sports teams, like Madison Square Garden Sports Corp., which owns the New York Knicks. The obvious KPI for the Knicks is how many basketball games they win. I suggested that there’s probably some correlation between basketball wins and business success, writing that “if the Knicks do great, MSGS makes more money selling tickets and whatever; if the Knicks are terrible MSGS makes less money.” 

One purpose of KPIs is to measure and reward employee performance: You figure out what you want from a division manager, you set some KPIs to reflect those goals, and then if she hits those KPIs you pay her a bonus. Senior corporate executives will often get bonuses based on achieving company-wide KPIs like revenue growth or profit margin. Elon Musk gets a bonus at Tesla Inc. for delivering a million robotaxis. 

A company that promises its chief executive officer a bonus for hitting her KPIs is taking on some financial risk. The risk is like: “If our CEO succeeds in selling a million cars, we have to pay her a $20 million bonus; if she doesn’t, we don’t.” Twenty million dollars is a lot of money, and you could imagine the company wanting to hedge that risk. Perhaps it could go to Cboe and buy a contract — on its own car deliveries — that pays it $20 million if it sells a million cars, and $0 if it doesn’t. Now it is hedged; it is indifferent between hitting the KPI (and paying the bonus) and not hitting the KPI (and not paying the bonus).

Except this is stupid? The company (1) is not indifferent between hitting the KPI and not hitting the KPI and (2) obviously shouldn’t be. The company wants to sell a million cars. Selling a million cars is good: The company set “sell a million cars” as a goal because it thought that would be correlated with increasing shareholder value. The reason the company promised to pay its CEO a $20 million bonus for selling a million cars, and $0 for failing to sell a million cars, is because the company expected to be better off, financially, in the state of the world where it sells a million cars, even after paying the bonus. [1]

And so hedging the bonus feels misguided. If the executive fails to hit the target, (1) the company is worse off overall and (2) it loses on the hedge. If the executive succeeds in hitting the target, (1) the company is better off overall and (2) it gets paid on the hedge. It’s not a hedge. Or rather: It’s a hedge to the executive’s bonus, but it’s not a hedge to the overall situation of the company. The executive’s bonus is, itself, the hedge. The executive costs the company more in good states of the world (for the company), and less in bad states of the world. Why go to prediction markets to reverse that?

(Of course you don’t need prediction markets. A company could just buy regular options on its own stock, to hedge the risk that its executives’ stock options will turn out to be very valuable because they successfully increase the value of the company. But, again: stupid! The company doesn’t need to hedge against the risk that its value will increase! Though of course companies often do buy back stock to offset dilution from stock-based compensation.)

Anyway a several readers sent me this story from InGame:

A third party helping Louisiana State University hedge against potential bonus payments to football coach Lane Kiffin used bets on the team’s performance on Kalshi to offset exposure. Similar trades were placed last month on South Carolina’s football team, again from a third party rather than the university, though the exact risk being hedged for those trades is less clear.

Two weeks ago, five “block” trades — trades negotiated off exchange — were placed on LSU to have a successful college football season, worth a combined $3 million.

In July, there were block trades in two markets concerning the University of South Carolina.

The trades were not placed by the schools. In fact, Kalshi’s trading prohibitions for the market would ban employees of teams involved.

Instead, InGame understands that the trades were placed by a third party that assists with hedging of risks such as coach bonus payments for sports teams.

Large block trades worth between $300,000 and $900,000 were placed on LSU to make it to the College Football Playoff, make it to the quarterfinals, make it to the semifinals, make it to the national championship game, and win the national championship.

The total combined payout of $3 million if LSU won the national championship (causing all five trades to pay out) is exactly equal to Kiffin’s national championship bonus payment. The payout for making it to the playoff or advancing to the quarterfinals, semifinals, or finals are all close to Kiffin’s bonus payment for each round, though never exactly matching.

One point here is that, within living memory, people thought it was bad for athletes and coaches and teams to bet on their own games. Now it’s “hedging” and it’s fine.

Another point here is: If it is bad — insider trading, under Kalshi’s terms [2]  — for teams to bet on their own games on Kalshi, is it also bad for teams to do over-the-counter trades on their own games with counterparties who then lay off the risk on Kalshi? Isn’t LSU essentially trading on Kalshi, in this scenario? 

But the main thing that bugs me here is: Why should LSU hedge this risk? Surely LSU wants to have a successful college football season? Surely winning the national championship would be good for LSU? Like, economically? I am not an expert in the economics of college sports, but I gather that if you win national championships that helps you recruit tuition-paying students, and extract donations out of alumni, and get lucrative television contracts. The reason that you offer a celebrity coach a cash bonus for winning the national championship is that you expect a national championship to bring in more money. Why do you need to hedge that?

I wrote above that perhaps a car company “could go to Cboe and buy a contract — on its own car deliveries — that pays it $20 million if it sells a million cars, and $0 if it doesn’t.” Here’s a question: Is that insider trading?

One simple answer is that, if the company has no material nonpublic information about its own car deliveries, it’s not insider trading. After all, the company is buying that contract as a hedge, meaning that it is genuinely uncertain about how many cars it will deliver. Perhaps the company announces its 2026 results in February 2027, and in the earnings announcement gives guidance about how many cars it expects to deliver each quarter, and then it goes out and buys a contract on its 2027 car deliveries as a hedge. It can argue “the market knew everything we knew, so we weren’t insider trading.”

You could quibble — even after the company issued that guidance, surely it knew more about its own sales prospects than the market did — but this is in fact a traditional analysis when companies trade their stock. When a company wants to issue stock, it puts out a prospectus disclosing its financial results and any material business news; when it wants to buy back stock, it usually waits until after announcing earnings to start a buyback program. [3]  After those disclosures, everyone just agrees to pretend that a company does not know more about its business than the market does, so the company can trade its stock. Perhaps prediction-market contracts on KPIs like net income, revenue or units sold would work the same way.

But let’s assume that the company does have material nonpublic information about its own deliveries. Let’s say it’s totally trading on the basis of material nonpublic information: Let’s say there’s a contract on “Company X announces deliveries of at least 1 million cars for the year through August,” and that contract is trading at 40% on Aug. 31, and the company knows it has sold 1.05 million cars this year, so it goes and buys up a ton of the contract at 40 cents to make a quick profit of 60 cents. Is that illegal?

Nothing here is legal advice, and I don’t really know the answer, but let me make four points:

1. It would be illegal if the company was trading its own stock, or its own stock options: Companies are not allowed to trade their own stock using material nonpublic information, on the theory that this violates their fiduciary duties to their shareholders. [4] The same rule applies to stock options. What we discussed last week is that Cboe wants to list these sorts of KPI binaries as, essentially, stock options: The theory is that a contract that pays $1 if a company’s earnings or revenue or car deliveries exceed X, and $0 if they don’t, is close enough to a bet on its stock that it should be listed on a stock options exchange, regulated by the US Securities and Exchange Commission, and treated like a stock option. If that’s correct — if the SEC lets Cboe list these KPI binaries — then the same insider trading rules might apply, and it would be illegal for the company to bet on its KPIs using inside information.
2. It would not, on the other hand, be illegal for an oil company to make bets on the price of oil, even if it had inside information about its own oil production that could be material to oil prices. Trades in commodity derivatives — like oil futures contracts — are regulated by the US Commodity Futures Trading Commission, not the SEC, and the rules are slightly different. I sometimes quote a CFTC commissioner’s point that commodity insider trading is only illegal if it involves “misappropriated confidential information in breach of a pre-existing duty of trust and confidence to the source of the information,” because of “the special characteristics of the derivatives markets, where end users necessarily trade on the basis of their own proprietary information in order to hedge their risks.” That is: Oil companies trade oil futures to hedge their oil-price risk, and they’re allowed to do that using their own proprietary information. And US prediction markets like Kalshi are generally regulated by the CFTC and treated as commodity derivatives markets. And Kalshi does list contracts on car deliveries; part of our discussion last week was about a turf fight between Kalshi (which wants these contracts to be regulated by the CFTC) and Cboe (which wants them regulated by the SEC). If a bet on car deliveries is a commodity swap, then maybe the company is allowed to bet on its own deliveries using inside information?
3. Nonetheless Kalshi, the leading US prediction market, seems to take a more expansive view of insider trading than is required by US commodities rules. Kalshi’s exchange rules, which “are approved and certified by the CFTC,” prohibit trading by “any person who has access or is in a position to access material non-public information before such information is made publicly available,” and by “any person who is a decision maker, direct or indirect, or has any influence, direct or indirect, on the outcome of the underlying event for any contract.” Does that cover the case of a car company betting on its own deliveries? I think so. (Thus the discussion above about LSU betting on its football results over-the-counter rather than directly on Kalshi: Kalshi appears to prohibit LSU from betting on itself directly.) If a car company bet on its own deliveries on Kalshi, using inside information, that would probably violate Kalshi’s rules. Would violating Kalshi’s rules also make it illegal insider trading? I dunno, maybe.
4. In any case, if an employee at the car company bet on its deliveries using nonpublic information, surely that would be illegal. Whether or not the company can trade using its own information, an employee can’t trade using her employer’s information. (“Misappropriated confidential information in breach of a pre-existing duty of trust and confidence to the source of the information.”) “Insider trading,” I often write, “is not about fairness, it’s about theft,” and that would be a theft of the company’s information.

By the way. You can see why Kalshi would have a blanket prohibition on insider trading: It’s trying to position itself as a well-regulated safe exchange, and to market itself to retail gamblers; letting companies bet on their own results using inside information sort of undermines Kalshi’s positioning. On the other hand, Kalshi also wants to be a good platform for corporate hedging, and corporate hedgers do sometimes trade with inside information. (Oil companies trade oil futures, or LSU hedges its football results.) I feel like we are still early in figuring out the right insider trading rules for prediction markets. Should a sports team be allowed to bet that it will lose a game, to hedge the risk of its star player getting injured? Should it be allowed to bet against itself, knowing that the star player is injured? Etc.

Anyway here’s a Wall Street Journal story about insider trading on KPI binaries on Polymarket, a less-regulated exchange:

Another case authorities are pursuing focuses on an employee at KPMG, the global accounting and consulting firm, people familiar with that investigation said. The employee is under investigation for betting on whether a specific public company would beat the consensus estimate for quarterly earnings, one of the people said.

Authorities view such bets as illegal if the person was entrusted with material nonpublic information about the company’s financial performance as part of their job. 

Right that one seems straightforward. Elsewhere, Kalshi banned George Santos for life, sure.

I used to say that “one of the best services a retail broker can provide is not answering the phones during a crash.” The idea is that a lot of retail investors have some bad ideas, and the more opportunities they have to trade, the more bad trades they will make. I have softened a bit on the specific point: Actually modern retail investors tend to buy the dip, not sell in a panic when markets crash, so a retail broker who doesn’t answer the phone [5]  during a crash, these days, is costing investors money. 

Still, there is probably some continuing relevance to the intuition that, the more retail investors trade, the worse they will do, so retail-investor-facing technology should be glitchy and hard to use. We talked a few weeks ago about the rise of agentic artificial intelligence for retail investors, which is the opposite of this: Agentic AI will let retail investors make tons of trades effortlessly, which will be convenient for them, but also, in expectation, expensive. In general, financial markets — all markets — have a tendency to eliminate frictions and make everything easier, faster, more efficient and more pleasant. But making it easier to lose money is bad.

South Korea is pushing the other way, though, making at least some retail trading unpleasant for paternalistic reasons:

Leveraged exchange-traded funds targeting twice the daily returns of chipmakers Samsung Electronics Co. and SK Hynix Inc. have seen their trading value collapse to 4% of its June peak and are set for their first monthly outflow.

Key to sapping demand has been a series of regulatory tightening moves, most recently a rule to complete five-day simulated trading. Investors must download a Windows-only program on PCs and spend at least an hour a day learning the ropes — and the risks — of leveraged trading with virtual cash. Interviews with several Korean retail investors suggest the new requirement, effective Aug. 19, is too cumbersome to meet.

When Kim Jung-hoon, a 41-year-old resident of Gyeonggi province outside Seoul, heard about the mandatory mock trading, his first reaction was that he wouldn’t even attempt it because it was “too much of a hassle.”

“The hours sound long and you can only download the program on PCs,” Kim said. “My work computer can’t download external programs. It doesn’t sound easy to bring an extra laptop with me to work.”

“Make people download annoying software” does not sound like an effective financial stability tool, but it probably is.

A crude story that you could tell is that, for a while, the preferred currency of online criminals was Bitcoin. The US government caught a number of those criminals and was able to seize their Bitcoin. And thus the US government has a stash of Bitcoin that it acquired from criminals. The libertarian anti-establishment currency is now an asset of the federal government.

A more speculative story you could tell is that, for a while, the preferred currency of risk-loving effective altruists was Anthropic shares. The effective altruism movement and Anthropic — the most vocally AI-safety-worried of the big AI labs — are deeply intertwined, and in the early days, before Anthropic was a $2 trillion company that can raise money from everyone, it seems to have raised a lot of money specifically from EA types who love (1) worrying about AI safety and (2) otherwise taking every positive-expected-value bet that comes their way. 

With that attitude, they tend to blow up. But Anthropic doesn’t blow up. Anthropic is doing great, and provides some cushion for the blowups. Leopold Aschenbrenner’s Situational Awareness hedge fund owns a big chunk of Anthropic, because of course it does, and when it blew up it shopped that stake around, though ultimately it kept the stake and sold other stuff instead. And when Sam Bankman-Fried’s FTX crypto exchange blew up, its Anthropic stake was similarly sold to pay off its debts. Or not quite. Actually FTX didn’t own Anthropic stock; Bankman-Fried did, and he turned his stock over to FTX to help cover its customer claims. But he wasn’t the only one. Business Insider’s Jacob Shamsian reports on the personal Anthropic stakes of other FTX executives:

Bankman-Fried's own Anthropic stake was liquidated in the bankruptcy of his failed cryptocurrency exchange. The Anthropic equity owned by Caroline Ellison and Nishad Singh, two associates who invested alongside him, took a different path. The government seized those shares and sold them to existing Anthropic shareholders, according to a person familiar with the sale. …

Bankman-Fried, Singh, and Ellison each invested in Anthropic's 2022 Series B funding round. Bankman-Fried bought $500 million worth, which according to court records represented 13.56% of Anthropic at the time. Singh acquired $40 million in shares, and Ellison acquired $10 million in shares, court records reviewed by Business Insider show. …

The Anthropic shares purchased by Singh and Ellison could together be worth between $4.17 billion and $5.03 billion today, based on the $965 billion valuation the company announced this May, according to Olav Sorenson, who teaches venture capital strategy at UCLA. Harrison Rolfes, an analyst at PitchBook, put the combined figure at $2.62 billion. If Anthropic went public at a $2 trillion valuation, the shares would be worth about $5.44 billion, Rolfes said.

For a while everyone was just out there flipping coins for vast wealth or ruin. And then all their coins came up “ruin,” but much later their Anthropic coins all came up “vast wealth.”

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A central fact of consumer finance is that big sophisticated financial companies can offer ordinary consumers irrationally good deals, because the consumers will probably mess them up for normal human reasons. The best-known example might be credit card rewards. Patrick McKenzie has a classic explanation, but the schematic story is something like:

1. If you buy stuff on a credit card, your bank collects a fee from the merchant of, call it, 1.5 cents for every $1 you spend.
2. The bank gives you a “reward” — cash back or points you can use to buy stuff — of X cents for every $Y you spend.
3. On average, the bank might pay its customers about 1 cent for every $1 they spend, keeping the other 0.5 cents for itself.
4. But there are some categories of spending, and some sorts of rewards, where the ratio is higher. “Get 5% cash back when you put gas in your car, and 1% cash back on everything else,” maybe. Or “get 1 point for every $1 you spend, the points can be converted into cash at 1 cent per point, or you can buy a first-class round-trip ticket to Paris for 100 points when the moon is waxing gibbous and Libra is in the seventh house.” Those points are worth 1 cent per $1 you spend, unless you use them the exact right way, in which case they’re worth more. 

If you have a lot of free time and a certain sort of brain, you might optimize this.. You might take out a bunch of credit cards and use each one only for its most rewarding purpose. You might get the 5%-cash-back-on-gas card and use it only to buy gas, earning 5% aggregate cash back on that card. You might get the round-trip-to-Paris card, use it once to buy $100 of groceries, book the ticket at exactly the right time, and earn like 2,000% rewards on that card. People do stuff like this; there are websites and Reddit forums devoted to it. [1]

If you use each card optimally, the bank will not make much money on your account; it might even lose money. The bank might be paying you more in rewards than it makes in transaction fees. The bank is giving you a deal that is too good, a deal that might bankrupt it if everyone took the deal. But almost nobody does this. Most people just have one or two or three credit cards, and they use them for all of their stuff, and they mostly get the average reward payment and it’s fine. Some of them do even worse; they forget about the rewards and they expire, say. Some people are on the Reddit forums trying to optimize their rewards, but that’s fine. [2] Most people are busy. 

Every so often a bank will mess this up and lose money. (We have discussed an example, Bilt, a credit card for paying your rent.) But for the most part it works out well. Competitive pressure forces banks to offer deals that would be good if consumers used them optimally, and to rely on the fact that consumers mostly don’t..

That’s the story of credit cards, but it is a useful paradigm for lots of consumer finance. Let me briefly mention two of my favorite examples. One is that Capital One offered a high-yield savings account with an advertised very high interest rate that attracted a lot of customers. This was a bad deal for Capital One: It was paying more than the going rate for deposits.. But then, over time, Capital One lowered the rate on the account, until it was paying much less than the going rate for those deposits. Customers could take their money out at any time without penalty, and one assumes some of them did, to put it in other banks paying higher rates. But many of them didn’t, because they were busy. They were not checking every day to make sure that the rate was still top-of-market. “We will pay you an above-market interest rate for now, and you can take your money out any time if we are no longer above-market” is on its face a bad deal for the bank, but in fact it was fine. [3]

My other favorite example is life insurance. Life insurance is often underpriced: If you buy 20-year term life insurance, your total monthly premiums over 20 years will probably add up to less than (1) your death benefit times (2) the actuarial probability that you will die during those 20 years. If 10,000 people signed up for term life insurance and paid all of their premiums, the insurance company would probably lose money.

The insurance companies are not idiots. They can offer underpriced insurance because many people do not pay all their premiums. They buy term life insurance, they pay premiums for a while, and then they stop: They need the money for other purposes, their circumstances change, they stop paying and the policies lapse. They do not “optimally exercise” their life insurance policies; they let them lapse when they shouldn’t.

We have talked about this a couple of times because, for a while, financial investors like Apollo Global Management were able to buy up a lot of these policies from insurance customers. The financial investors, unlike the ordinary customers, did optimally exercise; they kept paying the premiums whenever doing so had positive expected value. This was bad for the insurers.

It wasn’t as bad for the insurers as it could have been. You could imagine an extremely efficient world in which everyone took out millions of dollars of underpriced life insurance and immediately turned around and sold it at a profit to Apollo, which then made a lot of money at the expense of insurance companies, which then had to significantly raise the premiums they charged for life insurance. This mostly did not happen in the real world, in part for legal-risk reasons, but in large part because people are busy. If I ran up to you saying “hey you can make a quick buck by taking out some life insurance and selling it to Apollo,” you would almost certainly think I was nuts. That sounds gross and weird. What’s the catch? (Will Apollo, uh, hasten your demise?) Also it’s not really a quick buck; you’d have to get a physical and fill out forms. The underpriced option that insurance companies sell to customers is just comprehensively too hard for the customers to monetize, which means that it can keep being underpriced.

Those are my favorite examples, but surely the most important example is the 30-year fixed-rate mortgage prepayable without penalty. In the US, if you want to buy a house, a bank will probably lend you 80% of the value of the house at a fixed interest rate of, say, 6.65%. If interest rates go up, you don’t care: You pay 6.65% for 30 years. If interest rates go down, though, you can refinance at any time: You can go to a bank, take out a new 30-year fixed-rate mortgage at 4.5% or whatever, and use the money to pay back the old mortgage. You have a valuable interest-rate option: You have locked in a maximum interest rate for 30 years, but you have no minimum rate. In some approximate sense, you’ve got a floating-rate mortgage with a rate cap struck at today’s rates. 

And the bank [4]  will sell you that option quite cheaply: That 6.65% rate you’d pay on a 30-year mortgage is lower than the yield on Meta Platforms Inc.’s 30-year bonds, [5]  even though (1) Meta is a giant AA- rated company and you’re just a person and (2) Meta can’t prepay its bonds without penalty: If interest rates go down and Meta wants to refinance the bonds, it has to pay bondholders a make-whole payment that essentially captures the value of the interest-rate savings. 

Why do banks underprice this option? The basic answer is “because people do not optimally exercise it.” For one thing, when rates go down, people often do not refinance (or otherwise prepay) their mortgages. They are busy! They have a lot going on, refinancing takes time and is a pain, they do not trust mortgage bankers or want to spend a lot of time dealing with them, mortgages are confusing and it is hard to understand whether refinancing is a good deal. They are not checking in on interest rates every day, waiting for the optimal time to refinance. They’re getting tons of junk mail from mortgage companies saying “The Optimal Time to Refinance is NOW,” but that sounds fake and they throw it away.

Here’s “Why do borrowers make mortgage refinancing mistakes,” by Sumit Agarwal, Richard Rosen and Vincent Yao (2013), which gives a flavor of the problem [6] :

The decision to refinance a mortgage optimally requires solving a complicated system of partial differential equations. This can prove to be problematic because significant cognitive ability often is needed to properly make optimal financial choices. … 

Refinancing a mortgage requires not only that a borrower select an interest rate at which she is willing to refinance, but that she take the actions necessary to refi (such as contacting a broker or bank and completing paperwork). Agarwal, Driscoll, and Laibson (2012) argue that borrowers do not actively monitor mortgage rates and, even if they notice that the mortgage rate has reached their “trigger rate” for refinancing optimally, they may not immediately refi because they are too busy. …

They do not always monitor mortgage rates closely. Borrowers are faced daily with many complicated, time-consuming choices. Given a binding time-budget constraint, distracted borrowers may only be able to make certain decisions at stochastic intervals – or, put less formally, when they have a spare moment.

For another thing, when rates go up, people often do prepay their mortgages, even though that is suboptimal for them and a windfall for the bank. If interest rates move from 6.65% to 8%, a lot of people with 6.65% mortgages will nonetheless pay them back and take out new 8% mortgages. People move. If you get a new job in a new state, or if you have children and need more space, you might move to a new house. That will normally mean selling your old house, paying off the mortgage, buying a new house and taking out a new mortgage. If rates have gone up, you pay the higher rate. You’re optimizing things other than your mortgage rate.

Mortgage rates have gone up a lot since 2020, which has led to complaints and proposed solutions — like assumable or portable mortgages — that would allow people to avoid suboptimal prepayment. But if people could easily avoid suboptimal prepayment, mortgage rates would be higher. Right now, banks will give you a mortgage with a cheap prepayment option (that is, a relatively low rate), because they know that lots of people will exercise it suboptimally (because they move). But if that problem were solved — if you could keep your old low mortgage rate when you moved — then the banks could no longer sell you the option cheaply. The fact that people regularly move, even when mortgage rates have gone up, keeps mortgage rates lower than they otherwise would be.

Anyway Bloomberg’s Jack Trapanick and Scott Carpenter report:

Lenders say artificial intelligence will help homeowners whose mortgages are ripe for refinancing secure a new, cheaper loan far faster. The result could also squeeze investors in the $9 trillion market for mortgage bonds.

When interest rates fall, only about a third of homeowners who could save substantial sums by refinancing actually do it, according to research from Morgan Stanley. That’s because candidates don’t know they’re eligible or don’t want to go through the notoriously drawn-out and tedious process.

The number of takers is likely to rise, though, as mortgage lenders embrace AI to churn out approvals in a fraction of the usual time. Rocket Mortgage says a borrower can get from application to rate lock in just 30 minutes, and it’s aiming to cut that to 10 minutes. Rival United Wholesale Mortgage says initial approval can take as little as 15 minutes. Better.com, another digital lender with a small slice of the market, claims the firm can do it in only two.

Faster turnaround could double the percentage of eligible homeowners who refinance to perhaps 60%, according to a report from Morgan Stanley strategists including Jay Bacow, co-head of securitized products research. If that happens, they wrote, AI could make the 30-year mortgage seem like something “closer to a floating-rate instrument that only floats down.”

The Morgan Stanley analysts estimate that, if this happened, mortgages would “become more costly as investors demand extra interest to compensate for the added risk — perhaps one or two tenths of a percentage point.” “A floating-rate instrument that only floats down” should pay a higher rate than a 30-year-ish fixed-rate-ish instrument. Right now, US mortgages are more like the latter; in this imagined AI-assisted future, they’d be more like the former. (People would still move, though.)

This is, perhaps, a big deal on its own: There’s like $15 trillion of US residential mortgages outstanding, so a 10-basis-point cost increase would be $15 billion a year. But one could imagine a more general story like “broad AI adoption will lead to consumers exercising options more optimally.” Like: If you have three credit cards, an AI plug-in in your browser could automatically choose the most rewarding one for each online transaction. Or: Instead of “solving a complicated system of partial differential equations” to decide whether to refinance your mortgage, you just tell your AI “hey AI let me know when I should refinance my mortgage,” and the AI solves the equations and pings you when it’s time. Or: You could set up an AI agent to browse high-yield savings account offerings each day and move your money to the best one. When some US regional banks ran into trouble a few years ago, people attributed the scale and speed of the problem to the rise of the internet and social media: With social media, you could quickly learn rumors about a bank’s instability; with online banking, you could quickly move your money from an unstable bank to a safer one. “Game’s the same, just got more fierce,” said the vice chairman of the Federal Deposit Insurance Corp. Agentic AI could make it fiercer.

A lot of the consumer financial industry is based on consumer irrationality and inattention. Consumer financial products are built, and priced, for a world in which rationality and attention are scarce. AI could create a world in which rationality and attention — not human rationality and attention, but some bot that can search the web and do math — are abundant. What will that mean for credit cards and life insurance and mortgage rates?

Historically, private equity was a lumpy business. You and a couple of your buddies started a private equity firm, you went around looking for companies to buy, and occasionally you bought one. When you did, you’d need to write a big check. You spruced the company up for a while, and then you sold it again and received a big check. You might do this for 10 years, writing a dozen big checks to buy companies and receiving a dozen big checks for selling them. You might go months between checks.

Where did you get the big checks to buy the companies? Well, you had some investors, some limited partners who agreed to provide the money to buy the companies. These investors were the sorts of people who were willing to lock up their money in risky investments for years to earn higher returns: endowments, pension funds, sovereign wealth funds, super-rich individuals. You could imagine a system where, each time you wanted to buy a company, you called a handful of investors you knew and said “hey want to buy this company with me?” You’d cobble together money from investors for each deal, and then pool their money to buy the company. This exists — it’s called the “independent sponsor” model — but it’s a bit cumbersome; if you want to buy a company, it’s good to have the money lined up in advance.

So the more normal approach in private equity has been to raise a thing called a “fund,” which is maybe not exactly what it sounds like. (A more specific name is a “drawdown fund..”) A fund is like: You call some investors you know, you say “hey want to buy some companies with me,” and they commit a certain amount of money to you. You raise, say, $10 billion of commitments from those investors, and then you use that $10 billion to buy companies over a few years. You don’t have to raise money for each deal; the money is already there. Well, not quite. It’s not like the investors give you $10 billion and you put it in the bank and use it to occasionally write big checks. You don’t write that many checks; you don’t need to keep all that money in a checking account. Instead the investors give you commitments: They promise to give you that $10 billion when you need it, and then, when you do find a deal, you call on their commitments. You find a company to buy, you need $1 billion of equity, so you call 10% of each investor’s commitment. They are contractually obligated to wire you the money, and they do; you pool their money together and use it to buy the company. You do this each time you buy a company, and each time you sell a company you return some money to them.

For your investors, this is probably better than giving you all the cash upfront to put in a checking account: They can probably earn more money on their cash than you’d get in your bank account. It is also good for you, because your investors traditionally measure your performance based on your internal rate of return, measured from when you call capital to when you return it. Raising money from investors and parking it in a checking account earning 1% for two years lowers your IRR, and in fact there is a whole business of delaying capital calls to increase IRRs.

Still it is somewhat annoying for your investors. An investor that puts $1 billion into your private equity fund doesn’t wire you $1 billion that day. It wires you portions of that $1 billion, in lumpy unpredictable increments, over some period of years, as you find companies to buy. In the meantime, it has to do something with the money. It can’t invest it long-term; you might demand it any time on somewhat short notice. It has to keep an eye on its inbox for your capital calls, and when it gets one it has to move money around to send to you.

This is the traditional story, but modern private equity is a bit different, in two related ways. First of all, it’s giant. Private equity is not a niche business done by a couple of buddies for a handful of sophisticated clients; it owns perhaps 20% of the economy. It is still a lumpier business than, like, high-frequency trading, but it’s not like the giant modern alternative asset managers that evolved out of early private equity firms are going months between writing checks. If you’re deploying a trillion dollars, you’re doing a lot of deals.

Second, it is marketed to retail investors: If you’re deploying a trillion dollars, some of that comes from ordinary people’s retirement accounts. And ordinary people can’t really invest in drawdown funds. You can’t go out and raise, like, $5,000 commitments from thousands of retail investors, find a company to buy, and send each investor a capital call for like $500. It’s just too much of an administrative pain, and some of them will have moved and your capital calls will bounce back, it’s a mess. If you are going to sell private equity products to retail investors, you have to take their money upfront: If you raise a $1 billion retail fund, that means collecting $1 billion in cash and putting it somewhere until you need it. Maybe in a checking account, but not necessarily. If you’re constantly doing deals, and you have lots of existing portfolio companies and lots of other investors, you can probably find a more private-equity-ish use for their money. Put the $1 billion into some of your existing companies to pay out old investors or whatever. The money doesn’t have to sit idle for long.

Similarly, it’s not that convenient to go raise $1 billion from retail investors all at once: There are so many of them, and you can’t contact them all in a month to raise a fund. It’s better to have an “evergreen” fund where people can put in money whenever they want, so that if an investor gets an inheritance or wins the lottery or gets a bonus at work, she can immediately bash some of the money into your fund. Again, if you have a very lumpy business where you only deploy money every few months, this is annoying for you; the money coming in has no real correlation to the money being spent. But as your business gets bigger and more complicated, it’s less lumpy; if someone bashes $5,000 into your fund on a Tuesday you can probably find somewhere to put it by Friday.

And so your retail offering is “put money in whenever you want, however much you want; we’ll take it immediately and find something to do with it.” (It might also allow investors to take money out, at least some of it, at least some of the time: As money comes in from new investors, some of it can be used to cash out old ones, and retail investors love liquidity.) Meanwhile the institutional offering is “commit a fixed amount of money for years, and then we’ll call you for some of the money at times and in amounts that are convenient for us, and eventually we’ll pay you back when we decide to sell.” Which is … you can understand why it’s that way, but it’s kind of worse customer service, no? Like the retail product is kind of better?

Anyway the Financial Times reports:

Institutional investors are putting money into Blackstone and KKR vehicles set up to woo wealthy individuals, a move by the traditional backers of private equity that could ultimately upend the sector’s traditional 10-year fund model.

Evergreen funds, which allow investors to withdraw funds at regular intervals rather than lock up their capital for long periods, have become increasingly common as a way to make private equity and credit accessible to wealthy individuals.

But the funds, which tend to charge lower fees and target lower returns than the industry’s traditional closed-end funds, have also started to draw in institutional investors.

Blackstone’s evergreen funds for individuals had raised a “small percentage” from institutions, the firm’s head of wealth Joan Solotar told the FT. Interest had increased in the past year, she added. “It will continue to evolve and grow.”

KKR’s head of client solutions, Eric Mogelof, said it had recently launched institutional share classes in its buyout, credit and infrastructure evergreens to meet “growing demand”. …

Evergreens, meanwhile, take all of an investor’s commitment as cash on day one, removing the need for complex cash flow management that small organisations can find cumbersome. The funds’ need to keep cash on hand for redemptions generally leads to lower returns.

“We are at the beginning of a trend, but it’s going to continue to happen as the evergreen market evolves,” said Hugh MacArthur, chair of private equity at consultants Bain & Company. “Institutions are going to want the same . . . conveniences as individuals.”

It’s weird when the individual product is better than the institutional one. [7]  Part of the explanation is that it isn’t really better — evergreens “tend to charge lower fees and target lower returns” — but part of it might be that the institutional product was invented first, and the technology has advanced since then.

Arguably the way jobs work is:

1. You work at a job, they like you, they pay you.
2. The amount they pay you is, generically, enough to keep you. 
3. Therefore, to get more money, the main thing you have to do is to credibly demonstrate that someone else will pay you more.
4. The simplest way to do this is to go out and get another company to offer you a job at a higher salary, and then bring that back to your current job and say “see?” And then they give you a raise.
5. The other company in this scenario has done you a very valuable service: It has gotten you a raise at your current job.
6. What does the other company get out of it? I guess the answer is “some possibility of actually hiring you”: Maybe you’ll like the other company so much that you’ll actually change jobs, or maybe your current company won’t give you a raise and you’ll kind of have to change jobs. If it makes a lot of these offers, sometimes it will hire people!
7. But, in many cases, the outcome — in some sense the expected outcome — is that you stay at your current job and get more money, and the outside company gets nothing. It has given you something of value — a bid on your services that you can use to extract money — for free.
8. Inefficient!
9. It should charge you. Sign a contract like “sure we’ll interview you for this job, but if we give you an offer and you just use it to extract a raise from your current employer, you have to give us 10%.”

One could quibble. (Doesn’t the signal value of the outside offer go down, if the outside company is mostly making the offer to get a share of your raise?) Anyway:

Millennium Management is suing a Dymon Asia Capital employee for millions of dollars in a Hong Kong court after she accepted and later reneged on a job offer, said people with knowledge of the matter.

Millennium, a $92 billion hedge fund firm, is seeking HK$19.7 million ($2.5 million) from Hong Kong-based Dymon portfolio manager Tang Lin. That amount is intended to cover so-called “liquidation costs,” expenses that Millennium incurred while preparing for her to join the firm, said the people, who asked not to be identified discussing private information..

It is the latest example of the world’s biggest multi-strategy hedge funds fighting back at the practice of “gazumping.” The informal term, originally from the British real estate market, refers to when a seller initially agrees to an offer but then accepts a rival offer before the deal is done. Tang ultimately chose to remain with Dymon, people said.

I don’t know enough about her pay package to speculate, but it’s conceivable that paying Millennium $2.5 million for its participation in the sequence of events that led to her staying at Dymon could be a bargain for her.

Jane Street’s growing pains. Private Credit Investors Prefer to Be Trapped Than Take 26% Loss. Todd Boehly’s Insurer Vows to Slash Pile of Collateral Loans. Nvidia Sees AI-Fueled Demand Boosting Sales 70% Next Year. Nvidia Agrees to Buy Open Source AI Platform Hugging Face For $12.9 Billion. Bitcoin treasury companies shed $80bn in value as business model unwinds. KKR Agrees to Pay Record $250M Penalty for Serial Violations of Federal Premerger Review Law. Wells Fargo Steps Up Wealth Hiring After $1.5 Billion Revamp. Single Stock Leveraged ETFs Not Good for Japan, Regulator Warns. Harvard Business School explored European outpost after Trump’s visa threats. Greer Suggests US Needs to Consider Bans on Canadian Goods. The $2 Billion Brawl Over a Ruinous Wood-Pellet Trade. Junior consultants called back to office as AI increases need for human skills. Miami’s Wealthy Are Beating the Traffic With Helipads and Private Docks.

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Classically, a share of stock in a company is worth the present value of the expected future earnings of the company. Therefore, a share of stock is a bet on the future earnings of the company: If this quarter’s earnings are surprisingly good, or if the market comes to expect higher future earnings, the price of the stock will go up. I am using “earnings” in a loose and generic sense, just, like, some measure of the money that will eventually be available to shareholders. The stock’s value is a fairly straightforward function of future dividends, and a somewhat more complicated function of future net income or EBITDA or gross margins or revenue or units sold or whatever. Doubling a company’s future revenues probably won’t exactly double the future cash flows available to shareholders — it might even decrease them — but there’s some correlation.

Also, the price of an oil company’s stock might be a function of the price of oil: If oil prices shoot up, oil-company stock prices will probably go up. For that matter, the price of a sports company’s stock might be a function of sports performance: If the Knicks win the NBA championship, for instance, the stock of Madison Square Garden Sports Corp. might go up. In fact, the Knicks won the NBA championship in June, and MSGS is up more than 50% this year. I wouldn’t put a ton of weight on that one data point, and there are a lot of steps between “Knicks win games” and “future cash flows available to MSGS shareholders are higher.” But there is a connection, and in a very loose sense, MSGS’s stock is a bet on the Knicks to win a lot of future basketball games.

You could almost imagine MSGS using this for fundraising purposes. [1]  Like:

• MSGS goes out to investors to raise $100 million to spend on getting better at basketball or whatever.
• MSGS promises the investors “we will give you back $2 million for every game the Knicks win in the 2026-2027 season.”
• That has an expected payoff of about $106 million according to Kalshi, though with plenty of variance.
• The investors share in the upside (if the Knicks do great they make more money) and downside (if the Knicks are terrible they lose money) of the team.
• MSGS also shares in the upside and downside of the team: If the Knicks do great, MSGS makes more money selling tickets and whatever; if the Knicks are terrible MSGS makes less money..
• The investors do not own actual equity in MSGS; they do not own a residual claim on MSGS’s cash flow. They own a sports bet. But it has a certain family resemblance to equity; they share in the upside and downside of the company’s business performance. [2]

Why would MSGS want to raise money this way, rather than by issuing stock? Well, I mean, it wouldn’t; this is a silly hypothetical. The market for public-company stock is much deeper and more liquid and better priced than the market for sports bets, for now.. But we have talked a lot, over the years, about related topics:

1. For a while, if you had a business idea, you could raise a lot of money at attractive prices by selling crypto tokens. What made this extra attractive was that you (maybe) didn’t have to comply with US securities regulations; you could sell the tokens to investors without filing public disclosures and getting audited financial statements and doing all the other stuff required of US public companies. You weren’t selling “stock,” see; you were selling “tokens.” US securities regulators sort of tolerated this for a while, and then stopped tolerating it, and are now maybe back to tolerating it.
2. More recently, there have been suggestions that “tokenized stock” of private companies could be sold to public investors without complying with securities laws, though I’m not sure anyone quite believes that.
3. We have talked recently about prediction markets on corporate valuations. You can go on Polymarket and bet on Anthropic’s future valuation, that is, its stock price. Again, Anthropic does not file public disclosures with the US Securities and Exchange Commission, and yet Polymarket offers those bets to everyone. (Except, very nominally, US traders. Very nominally!) 

None of these products, I think, quite works, as a matter of US securities regulation. The tokenized stocks and private-company-valuation bets are (supposedly) not sold to US investors. I think that tokenized stock is clearly a “security” under US law, and prediction markets on corporate valuations are clearly “security-based swaps,” and both of them are subject to the SEC’s disclosure and registration regime. They are very close substitutes for stock, which means that US securities law treats them like stock.

But what about slightly less close substitutes? Instead of selling bets on a company’s stock price, what about selling bets on its net income? Those things are different, but the stock price can theoretically sort of be decomposed into a series of bets on its future net income. Would a bet on net income — a bet that pays $1 per $100 million of Tesla Inc.’s 2026 net income, for instance, or a bet that pays $1 if that net income is above $4 billion and $0 if it’s below — be a security, or a security-based swap?

I’m not sure, but my impression is “kind of, yeah.” One piece of evidence is that Kalshi doesn’t list bets like that. Kalshi is a US regulated prediction market. It is registered with the US Commodity Futures Trading Commission, which allows it to trade all sorts of “swaps” (bets), but not security-based swaps. (It has proposed to list equity index bets, because weirdly broad-based equity indexes are commodities, not securities, under US rules.) So it doesn’t list the sort of Anthropic-valuation contracts that Polymarket (which is mostly not US regulated) does, and it doesn’t list Tesla net income contracts either.

It does list Tesla total deliveries contracts, though. You can bet, on Kalshi, on how many cars Tesla will deliver in 2026. There is probably some correlation between how many cars Tesla delivers in 2026 and its stock price at the end of the year; the stock price is in part a function of the car deliveries. But those things are different enough that Kalshi can reasonably argue — and the current SEC and CFTC seem to agree, or at least not care — that the total-deliveries contract is not a “security-based swap.” It’s a bet on cars, not a bet on Tesla.

That is: A bet on Tesla’s stock price is a close substitute for buying stock, and so is regulated by the SEC and off-limits, for now, to Kalshi. A bet on Tesla’s net income is a somewhat more indirect substitute for stock, and so is maybe regulated by the SEC and maybe off-limits to Kalshi. A bet on Tesla’s car deliveries is an even more indirect substitute for stock, and so is maybe not regulated by the SEC, and listed on Kalshi.

Kalshi definitely lists Knicks total wins contracts. 

Bloomberg’s Bernard Goyder reports:

Kalshi is asking regulators to delay products from one of its competitors, Cboe Global Markets Inc., as the friction between upstart prediction markets and incumbent financial exchanges heats up.

The event-betting platform sent a letter to the Securities and Exchange Commission this month, asking the agency to hold off on approving new binary options contracts tied to specific line items in corporate earnings reports — products that would compete with some so-called event contracts already offered by Kalshi.

The dispute flips the script on previous industry debates in which Cboe and CME Group Inc. have both argued that prediction market products have been approved too quickly by the main regulatory agency overseeing the platforms, the Commodity Futures Trading Commission. …

Previously, many financial products tied to public stocks — such as equity options — have been governed by the SEC. But Kalshi has operated its contracts under the oversight of the CFTC, which has said that prediction markets are derivatives exchanges that should come under its oversight.

Cboe, on the other hand, sought approval from the SEC for its new binary options tied to corporate performance metrics.

Cboe and CME have both complained that the CFTC has allowed new kinds of prediction market contracts to start trading with minimal scrutiny, in contrast to the SEC’s slower process, which, they argue, has been rigorous and attuned to investor protection.

Here is Cboe’s proposal, and here are the public comments filed with the SEC. (Here’s Kalshi’s, and here’s Cboe’s response.) Cboe wants to “amend its Rules to permit the listing of binary options overlying key performance indicators (‘KPIs’) reported by certain issuers of stock (‘binary KPI options’).” A binary option is a yes/no bet that pays $1 if the KPI is above the contracted level and $0 if it’s below. It seems like the bets would be on things like earnings per share, net income, total revenue and segment revenue. You could bet on whether Tesla’s earnings per share this quarter will come in above or below $0.40, for instance. For that matter, Cboe would also let you bet on Tesla’s “Model 3/Y Production (#),” that is, how many cars it produces.

These would, in Cboe’s view, be securities bets. They would be equity derivatives, just like options on a stock’s price. They would be regulated by the SEC. They would be part of the stock-market ecosystem regulated by the SEC, not part of the swaps/bets/prediction-market ecosystem regulated by the CFTC.

Kalshi disagrees, for somewhat obvious reasons. The point, for our purposes, is that, for now:

1. Stocks and stock options are listed on stock (and options) exchanges, regulated by the SEC, and subject to a regulatory regime in which companies need to disclose a lot of business information if they want public investors to be able to buy their stocks.
2. Like, sports bets are listed on prediction markets, regulated by the CFTC, and not subject to the same sort of corporate financial disclosure regime. You can bet on the Knicks, whose financial statements are public, or on the Lakers, whose aren’t. The CFTC, correctly, does not consider it essential that every sports team’s audited financial statements be disclosed to bettors. 
3. Bets on companies’ performance, bets that are correlated with their stock price but are not exactly their stock price, are up for grabs. A bet on net income or revenue or units sold is not the same as a stock investment, but it’s not so different either. Is is part of the stock market, regulated by the SEC, requiring corporate disclosures? Or is it part of the betting markets, regulated by the CFTC, not requiring those disclosures? Perhaps we’ll find out.

Of course my real interest is in sports teams raising money by selling sports bets. Or more pragmatically: What if net-income bets, or KPI derivatives more generally, are not securities? Could a private company raise money from public investors by selling net-income contracts on prediction markets? A share of stock is a bet on a company’s future income, but maybe a bet on a company’s future income is not a share of stock.

Here is one bad thing that could happen. A company raises money by issuing bonds. It wants to sell, say, $1 billon of bonds. In the bond offering, it tells investors that it has earnings of $300 million per year. Investors read the prospectus and think “ah, this company makes plenty of money to pay back these bonds,” and they agree to buy the bonds at, say, a 6% interest rate. In fact the prospectus is wrong and the company actually earns $0 per year. The first interest payment on the bonds comes due and the company says “whoops, no money.” It defaults on the bonds, it goes into bankruptcy, and the bondholders get back $0 of their $1 billion. I think it is self-evident why this is bad.

Here is another bad thing that could happen. A company raises $1 billion of bonds at 6% by telling investors that it has earnings of $300 million per year. In fact the prospectus is wrong and the company actually earns $100 million per year. The first interest payment on the bonds comes due, and the company pays it. In fact, it makes all of the interest payments when due — $100 million is much less than $300 million, but it is enough to pay $60 million of interest — and at maturity it repays the full $1 billion. The bondholders get back their $1 billion, plus the promised interest.

Is this bad? I mean, you could make an argument that it’s fine. Like:

1. The bondholders bought the bonds because the company promised to repay them their principal with an agreed interest rate, and it did, so that’s fine. No bondholder lost any money: They invested $1 billion and got $1 billion back with interest.
2. The bondholders agreed to the fairly low 6% interest rate because they concluded that the company was relatively safe, that it would be able to repay the principal and interest without too much trouble. In drawing that conclusion, perhaps the bondholders considered the (incorrect) disclosure that the company earns $300 million per year; who can say really. That disclosure was wrong, but the conclusion was right: Ex post, the company really was safe, and it really was able to repay the principal and interest. The company’s realized credit risk was low, and therefore its 6% interest rate was fine.

These arguments seem bad? You can probably spot the flaws. Here are a few, though you can doubtless add others:

• Most risky bonds do not default, so “this bond got paid back and therefore its realized credit risk was low” is not a real argument.
• The ex ante probability of default was higher than the market thought — the company had less cushion to pay its debts than investors thought — which is the bad thing.
• If, a year after issuing the bonds, the company had said “whoops actually we make $100 million per year,” the market price of the bonds would have gone down. (Their expected yield would have gone up.) Bondholders would have lost money, on a mark-to-market basis. Of course if they held to maturity they’d get their principal back, but that is not the only relevant measure.
• If, before issuing the bonds, the company had accurately disclosed its earnings, bondholders would have charged a higher interest rate. Therefore they did lose money, measured against the correct baseline: Had they known the true facts, they would have gotten paid more interest.
• In fact there are cases of companies getting in trouble for this sort of thing: If you make incorrect financial disclosures to bondholders, that’s arguably securities fraud, even if you pay the bonds back on schedule.

Here is another thing that could happen. An insurance company raises $1 billion of annuity money. That is, it goes out to 1,000 investors and says to each of them, “if you give me $1 million today, I will pay you a steady income for the rest of your life.” [3]  That is like a bond; the company is raising cash today by promising payments over time. You could imagine the investors evaluating it like a bond: “This company has plenty of capacity to make the promised payments,” the investors might think, “and therefore I will accept an expected annual return of about 6% to reflect the safety of this investment.” But that’s not a real thing. I mean, that’s how the bond market works, [4]  but it is not a reasonable thing to expect of retail annuity buyers. People looking to buy annuities do not, generally, scrutinize the financial statements of insurance companies and choose between buying an annuity from a safe company at 6% and buying one from a risky company at 8%. Evaluating the financial strength of an insurance company is a complex and specialized business, and even figuring out the implied yield of an annuity — figuring out what sort of credit spread is embedded in the annuity product — isn’t always easy. [5]  You can’t say “this company brings in $300 million a year so I’m happy to get a 6% yield on my annuity,” because you can’t figure out how much the company brings in or what yield you’re getting on your annuity.

Instead, there is a somewhat more binary system in which state insurance regulators decide which insurance companies are safe. Safe companies can sell annuities to raise money; unsafe companies cannot. Evaluating the financial strength of an insurance company is a complex and specialized business, so it is done by state insurance regulators using a risk-based capital framework. If you have $X of capital, you can raise up to $Y of annuity money, etc. Insurance customers do not have to evaluate an issuer’s financial strength, because regulators do. 

This is exaggerated — some customers and their advisers surely do consider the financial strength of insurance companies, and safer companies probably have a lower cost of capital than bare-regulatory-minimum companies — but it is a useful approximation.

So one bad thing that could happen is: An insurance company wants to raise $1 billion of annuity money, it tells its regulators “we earn $300 million a year so we can easily cover those annuity payments,” [6]  the regulators are like “you sure can, no problem here,” the insurance company sells $1 billion of annuities at market rates, but in fact the insurance company makes $0 per year and can’t make any payments on the annuities. It defaults, it becomes insolvent, it is seized by regulators, and a state guarantee fund pays out some but not all of the money owed to customers. Self-evident badness! 

And then another bad thing that could happen is: Raise $1 billion of annuity money, say “we make $300 million a year,” regulators say “sure,” but it turns out you actually earn $100 million per year. But it’s fine, and you make all the required payments on the annuities. The annuities were, ex ante, riskier than the regulator thought. Had the regulator known the true state of affairs, it wouldn’t have let you sell all the annuities; it would have required you to have more capital against your asset base. Ex post, everything was fine. But the regulation is risk-based, and if the regulator doesn’t have accurate disclosures then policyholders and state guarantee funds are taking more risk than they want.

The point here is that it is not the customers who were misled by the wrong disclosures; the customers didn’t read the disclosures. The regulator was misled. And, similarly, when the disclosures are corrected, what happens is not that the market price of the annuities drops; the annuities don’t trade or have a market price. What happens instead is that the regulator demands more capital, to reflect the higher-than-expected risk.

I’m just using the simplest possible bad thing here, the company saying that it has more money than it actually does. In the real world, there are subtler — and less bad — forms of badness. The company could say “we hold $2 billion of investment-grade corporate debt to back our  insurance obligations,” but actually some of that debt was downgraded, or the ratings agency had incomplete data or conflicts of interest when it assigned those investment-grade ratings. Or regulators might have questioned those investment-grade ratings if they had known that actually the corporate debt was issued by affiliates of the insurance companies. That’s all stuff that makes the insurance riskier ex ante, but in a diffuse, hard to measure way. If the company had disclosed everything to everyone perfectly, it probably would have been required to have more capital to back its insurance obligations. But it’s probably fine! The insurance will probably get paid! 

Anyway:

TWG Global, the holding company at the heart of Mark Walter’s empire, hit back against what it called “multipronged attacks” on its business as US prosecutors continue to probe the firm.

The company is working with both the US Department of Justice and the Securities and Exchange Commission to resolve their inquiries, according to a statement Wednesday. Its insurance business has also submitted plans to its regulators to try to eliminate any concerns they have, the company said.

“Despite what has been reported, there has been no fraud,” TWG said. “There is no victim here. No one has been harmed, and no one has claimed they were harmed.”

We talked a bit about the TWG situation yesterday: TWG’s insurance companies “ disclosed more than $20 billion of loans that should have been labeled as affiliated transactions, but weren’t,” which probably means they should have had more regulatory capital than they did; they are now working with regulators to fix the problem. From the statement:

As it relates to Group 1001 [TWG’s insurance group], at its core this is a regulatory matter with a straightforward plan that has been submitted to its regulator to promptly eliminate all of the affiliate exposure at the Group 1001 insurance companies.

There is no victim here. No one has been harmed, and no one has claimed they were harmed. …

Affiliated transactions are commonplace in the insurance industry, widely permitted subject to applicable regulatory requirements, and a part of the insurance industry’s normal course of business.

Affiliated transactions should be properly disclosed, but to state that they “generally” have the potential to “loot” the insurer is untrue.

The reality is that Group 1001 has invested in real assets that are performing well; the insurance companies have recognized significant income from the investments and no policyholders have lost money because of these transactions.

As part of the plan, TWG is proposing to purchase the affiliated assets from the insurance companies, reflecting its confidence in the quality and performance of those assets.

It’s probably fine!

The Wall Street Journal has a good profile of Leopold Aschenbrenner, the 24-year-old founder of Situational Awareness, the hedge fund whose semi-collapse we have discussed around here. Notably, Aschenbrenner got married earlier this month, just days after selling most of his public stock portfolio to Citadel to meet margin calls. The Journal story includes this story from the wedding:

When it was time to toast the newlyweds, one speaker thanked Citadel CEO Ken Griffin for making it all possible. 

Just great wedding-speech roast material. Also this can’t be the first time that Griffin has been thanked “for making it all possible” in a wedding speech; if you have further examples please email me.

Anthropic Expected to Tell Investors It Sees Over $30 Trillion in Potential Revenue. The multiplying risks of financing data centres. Mark Walter’s Troubles Are Disrupting the Insurance World’s Hottest Trade. Insurers Want a Bigger Slice of the Bank Risk Transfer Boom. How four Russian bankers made millions from EU sanctions. Meta Says It Will Pay Up to $18 Billion Over Social Media Claims. Vanguard Buys Wealth Management Platform Altruist in $4 Billion Deal. Unlikely Winners of the Data Center Boom: Sound Consultants. Americans Say They Feel Guilty About Spending Money on Fun.

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Who should be making this loan?

When LeBron James signed up to lead the Los Angeles Lakers to NBA glory with a $154 million contract in 2018, it wasn’t the biggest deal he did that year.

Just months before he joined, a limited liability company he controls borrowed almost $300 million from a pair of Midwestern life insurers advised by an arm of Guggenheim Partners, according to insurance industry records reviewed by Bloomberg.

The previously unreported bonds, which are due in 2049, were structured to provide immediate cash to James and backed by a stream of future revenue tied to his earnings outside basketball such as a lifetime Nike Inc. sponsorship, people with knowledge of the matter said.

The burst of lending began before Guggenheim leader Mark Walter started acquiring the storied basketball team. In an abrupt turn this month, the billionaire mogul agreed to sell the Lakers amid a federal probe into parts of his business empire. There’s no indication that the loans to James have anything to do with those inquiries.

LeBron James is a guy. He has large and reasonably predictable future cash flows. You can put those cash flows into a box and issue bonds with a senior claim on them. James turns his future cash flows into $300 million upfront, and the bondholders get their $300 million back, with interest, over 30 years. Absolutely standard financial stuff, though applied to the cash flows of a guy rather than a corporation or shopping mall or data center.

Who are the right buyers for the LeBron bonds? They are quite long-term (30 years). They are, presumably, illiquid: $300 million is a lot for one guy to borrow, but it’s not a huge debt complex for the institutional credit markets, it’s a somewhat complex situation, his financials are not publicly disclosed, and there is unlikely to be a deep liquid secondary trading market in LeBron bonds. Nor is “loans to athletes backed by Nike sponsorships” a huge asset class, though Bloomberg notes that “athletes and artists are increasingly using future earnings like royalties and licensing deals to structure deals that help them unlock immediate capital.”

And so if James went to a bank and asked to borrow $300 million for 30 years, the bank might get nervous. Banks are funded by deposits, and making 30-year commitments to illiquid investments is not really the safest use of their money. Similarly, a bond mutual fund might have a hard time buying these bonds: Mutual fund customers can put money in or take it out at any time, so the funds might need to buy or sell their holdings, and small weird illiquid bespoke bonds are not ideal for that.

What you want instead is a buyer with its own long-term locked-up capital. Classically those buyers are endowments and pension funds, pools of money with predictable long-term liabilities. If you know you have to pay out pensions over 30 years, you can easily lock up some of your money in 30-year LeBron bonds. And because these bonds are illiquid — because they can’t easily be sold to retail investors or mutual funds or banks or hedge funds — they should pay a higher expected return than regular bonds, which should make them attractive to pensions.

One useful model is that the postmodern version of the “pension fund” is the “alternative asset manager investing the money of life-and-annuity insurance companies.” That is: A pension fund traditionally provides a steady predictable long-term stream of income to retirees, but the US has largely moved away from traditional defined-benefit pensions. But pension funds are great investors, with long time horizons and a willingness to buy weird illiquid stuff to achieve long-term returns, and so the modern financial industry misses them and wants to recreate them. Annuity companies basically sell private pensions — you give them money, they promise you a steady predictable long-term stream of income in retirement — and hand the money over to alternative asset managers, who invest it in weird illiquid stuff to achieve long-term returns.

This annoys people, because (1) the stuff is weird and illiquid and (2) the customers are ordinary retirees who can’t afford to lose money if the weird stuff doesn’t work out. You could argue, though, that it’s sort of inevitable:

• Weird illiquid stuff should pay more than ordinary liquid investment-grade bonds.
• Insurers who buy weird illiquid stuff should get higher returns than insurers who stick to ordinary bonds.
• Therefore they can offer cheaper life insurance or higher annuity payments.
• Therefore they can out-compete other insurers for customers.
• Therefore all annuity companies will eventually invest at least a large slug of their money in weird illiquid stuff.

You can take that argument too far: Insurers could do really risky stuff to earn high returns, out-compete safer insurers for customers, put lots of money into really risky stuff and then blow up, leaving the customers with nothing. It’s not like the customers are independently evaluating the insurers’ investments. The solution to this is basically regulation: Regulators are supposed to keep an eye on insurers and make sure that they invest in reasonably safe stuff, largely investment-grade credit instruments. But, with the right structure, a LeBron bond could be an investment-grade credit instrument, even if it’s a weird and illiquid one.

This is the basic story behind the rise of private credit. The Financial Times describes the situation:

Historically low interest rates were weighing on life insurance and annuity providers [after 2008], making it hard for them to earn enough on investments to meet future commitments to policyholders.

The difference between what insurers could earn holding staid, high-quality corporate and government bonds and what they owed policyholders had crumpled, making their traditional business model all but obsolete. …

“You cannot run an insurance company successfully and profitably if your only access is what exists in the public market,” Marc Rowan, Apollo’s chief executive, told an industry conference last year.

One other thing to notice about the LeBron bonds, though, is that LeBron James is very famous and cool. People want to hang out with him. People want his autograph. Nike wants to pay him hundreds of millions of dollars to be associated with him. This should, you might think, lower the expected return of the LeBron bonds. Like, the interest rate on the LeBron bonds should, theoretically, be something like:

• the 30-year risk-free rate, plus
• some credit spread reflecting the riskiness of the expected cash flows, plus
• some additional premium reflecting the illiquidity of the bonds, minus
• some discount reflecting the fact that it’s LeBron James and people want to go around saying “oh yeah LeBron owes me money.”

But there is a sort of market segmentation problem: 

1. The people who think LeBron James is cool and want to be associated with him are essentially people, individuals, and it’s hard to get individual investors to lock up their money for 30 years.
2. The people who want to lock up their money for 30 years to earn an illiquidity premium are essentially pension funds and annuities, and their goal is to maximize economic returns, not to hang out with LeBron James.

If the LeBron bonds were priced with a discount reflecting James’s coolness, pensions and quasi-pensions wouldn’t rationally buy them: The pension fund doesn’t derive any benefit from that coolness.

But in the real world, there are principal-agent problems. Somebody derives a coolness benefit from lending annuity money to LeBron James. Somebody — not a dispersed pool of retail annuity buyers, but a person — is sourcing and negotiating this loan on behalf of a life insurer. That person is handing James a big check and shaking his hand and saying “pleasure doing business with you,” and is having more fun than the person handing over a similar-sized check for a pool of auto-loan receivables. Does this lower James’s borrowing cost, at the expense of the life insurance firm? Man I have no idea; just something to think about.

Mark Walter has been in the news a lot recently, and not because of the LeBron bonds. Walter is the co-founder and chief executive officer of Guggenheim Partners, an alternative asset manager, and a pioneer in the business of acquiring insurance companies to provide capital to private investments. [1] He also has a personal holding company, TWG Global, which owns among other things (1) shares of Guggenheim, (2) two life-and-annuity insurance companies, Delaware Life Insurance Co. and Clear Spring Life and Annuity Co. and (3) stakes in the Los Angeles Dodgers, the Los Angeles Lakers and Chelsea FC. The US Securities and Exchange Commission and federal prosecutors are looking into those insurance companies, which apparently loaned money to Walter’s other businesses without disclosing it. Bloomberg News reports:

Prosecutors and regulators are examining how his insurers failed to disclose that loans they made were channeled to his other pursuits. After receiving subpoenas, those insurers disclosed more than $20 billion of loans that should have been labeled as affiliated transactions, but weren’t. …

It’s not illegal for an insurance company to lend money to a related party. But capital rules pressure life insurers to hold mostly investment-grade credit, and it can be difficult to get insurance regulators to accept an investment-grade stamp on a related-party deal.

So the insurers apparently loaned money to nominal third parties, which then loaned it back to Walter affiliates:

In recent years, Walter’s insurance companies loaned more than $1 billion to newly formed LLCs set up as subsidiaries of [Scott] Szykowny’s small trading firm, Hudson Trading, Bloomberg reported this month, citing people familiar with the matter.  …

It was a great deal for Szykowny: By lending the money out to Walter’s businesses at a higher interest rate than he borrowed, he collected a spread while putting little or none of his own money at risk, those people said.

Eventually the insurers reclassified the loans, and there does not seem to be any allegation that they have not performed; TWG is buying some of them back and replacing them with unaffiliated assets. At a high level, this stuff is directionally fine:

• Walter is a successful alternative asset manager.
• The insurance companies have long-term predictable liabilities and should invest in illiquid alternative assets.
• Everyone else is doing it: Lots of other alternative managers have their own insurance companies that invest in affiliated transactions, because good alternative asset managers should be making investment decisions for insurance companies.

But the lack of disclosure is bad, and seems to mean that the insurers had too little regulatory capital; they need more risk-based capital against affiliated loans (which these were) than against unaffiliated investment-grade ones (which is what they said these were). And the lending to affiliates through non-affiliates looks pretty weird.

Also, though, he does own a lot of sports teams. Fewer, now: This month, Walter sold the Lakers to raise money. I have not seen any suggestion that the Delaware Life loans were, like, “they loaned Mark Walter $6 billion to buy the Lakers.” But money is fungible, and Walter selling the Lakers to raise money to restructure some of his insurance companies’ investments suggests something in a vaguely related direction. My Bloomberg Opinion colleague Paul Davies writes: “Watching the rapid unwinding of ties between Mark Walter, his investment firm and its insurance companies has me asking: Why were the customers financing his trophy assets?”

And don’t the LeBron bonds suggest an answer? Life insurance customers should be financing strange illiquid assets, because they are best suited to hold that risk. But if you’re a guy with the discretion to invest billions of dollars of insurance-company money into whatever strange illiquid assets you think are the best, and if no one is keeping an eye on you, you might end up picking the ones you think are the coolest instead.

The Archegos situation was, roughly:

1. Archegos Capital Management, Bill Hwang’s family office, borrowed a lot of money from several banks to buy about a dozen stocks.
2. Archegos bought a lot of those stocks, often becoming the biggest holder of those stocks and buying large chunks of the daily volume.
3. Those stocks went up a lot, largely because of Archegos’s concentrated buying.
4. This gave Archegos big mark-to-market profits, which it used to borrow more money to buy more of the stocks.
5. Eventually the stocks started going down, Archegos got some margin calls, and it couldn’t meet them.
6. Its banks got together to discuss an organized unwind of the trade, in which they would seize the underlying stocks and work together to sell them in an orderly fashion that wouldn’t spook the market.
7. But then they didn’t, and raced to sell them instead, leading to collapses in the prices of the stocks.
8. At the end, Archegos was worth $0 and several of its lenders had lost billions of dollars, though others did fine.

There are some obvious similarities to the situation at Situational Awareness last month:

1. Situational Awareness LP, Leopold Aschenbrenner’s artificial intelligence-focused hedge fund, borrowed a lot of money from several banks to buy large positions in some AI stocks. 
2. Situational Awareness bought a lot of those stocks, becoming for instance a cornerstone investor in SK Hynix Inc.’s US offering last month.
3. Those stocks went up a lot while Situational Awareness was buying them.
4. This gave Situational Awareness big mark-to-market profits, and it does look like it used those profits to borrow more money to buy more of the stocks. 
5. Eventually the stocks started going down, Situational Awareness got some margin calls, it met them for a while, but the pressure built..
6. Situational Awareness and, one assumes, its banks discussed an organized unwind of the trade, in which it would sell some of the underlying stocks in an orderly fashion that wouldn’t spook the market.
7. That worked: Situational Awareness sold most of its public stock positions to Citadel last month in a block trade at a discount. 
8. At the end, Situational Awareness continues in operation, seems to be up for the year despite the drawdown, and can easily raise more money. Citadel has worked out of most of its Situational Awareness positions, apparently at a profit. None of its lenders lost any money.

The end of this story — Steps 7 and 8 — is very different from the end of the Archegos story. But the Archegos story was quite bad! Hwang went to prison for market manipulation and deceiving his banks, and several banks were quite embarrassed and lost billions. You might reasonably ask questions like “how close was this to Archegos?” and “did the banks just get lucky here?” And the New York Times reports:

The Securities and Exchange Commission recently sent subpoenas to banks that handled the hedge fund’s calamitous trading and that fed it borrowed money to supersize its bets, according to three people briefed on the outreach who were not permitted to discuss it publicly.

The subpoenas asked for details on the timing of Situational Awareness’s trades and for its communications with lenders about the money it was borrowing, also known as “leverage,” two of those people said. The subpoenas additionally warned the banks to preserve any information regarding the San Francisco hedge fund.

The S.E.C. oversees financial markets with an eye toward protecting small investors, and has brought civil cases regularly against investment firms that produced large losses. Any investigation into Situational Awareness would be at its earliest stages, and it’s no guarantee that it would lead to fines or other punishment. The hedge fund has not been accused of wrongdoing.

We’ll see what they turn up, maybe, but I think the answer is: This was not particularly close to Archegos. 

The biggest bad thing about Archegos is that, by being the biggest buyer of his stocks for a long time, Hwang manipulated their prices up. Aspects of this are mysterious to me — for one thing, I have no idea what his endgame was; for another thing, the evidence of manipulative intent was a little thin — but it seems likely that the value of Archegos’s portfolio was inflated by Archegos’s own frenzied buying, and also that Archegos did not have much of a fundamental reason for buying so much at those prices.

Situational Awareness also clearly had an impact on the prices of its portfolio. It was a big buyer of its stocks, but Aschenbrenner was also an influential and widely followed AI investor, and the stuff that he bought went up in part because he bought it. Also, when he was selling stuff in July to meet margin calls, the stuff went down, presumably because of his selling (and the momentum traders who magnified it). But the context is that Situational Awareness was buying lots of AI stocks in a giant AI boom; it seems silly to think that, like, SK Hynix was up because of Situational Awareness’s buying. Also Situational Awareness had pretty well-documented, fundamental, non-manipulative reasons for buying AI stocks hand over fist. There’s a whole manifesto. 

A related … bad? weird? … thing about Archegos is that, as its stocks’ values soared, it kept borrowing more money to plow back into the same positions. This drove me crazy at the time; I wrote:

One thing about margin lending is that if you borrow money to buy stocks, and your stocks go up, you automatically deleverage. If you use $15 of your own money and borrow $85 from your broker to buy $100 worth of stock, you have 85% leverage; if the stock then goes up to $200, you are down to 42.5% leverage. You still owe your broker $85, but now you have $200 worth of stock. If the stock then falls by 25% to $150, that’s fine: You are still in the black, and your broker still has ample security for its loan.

The incredible thing about Bill Hwang is that he made enormous levered bets on risky stocks, and those bets worked out perfectly and made him immensely wealthy in the course of a year or two, and he seems to have plowed every cent of it back into increasing those levered bets.

This was a mistake not just by Archegos, but also by its lenders: Lending Archegos 80% of the value of its stocks at the beginning of the trade is one thing, but lending it 80% of the value of the stocks after they had doubled in price is much worse. If they fall back to their original price, the loan is underwater!

There were some suggestions that Situational Awareness did something similar, and I wrote a few weeks ago: “Isn’t it a little weird that Aschenbrenner was up 1,000% in two years and still running at something like 4x leverage? … ‘Our bets have paid off lavishly, so we need to borrow ever more money to keep on the same level of leverage’: What, no, why?” 

But in fact it seems like Situational Awareness did lower its borrowing as it made money: Its lenders understood that, as Situational Awareness’s stocks shot up, they couldn’t keep lending the same percentage of their value. International Financing Review reports that “banks’ prime brokerage units passed their sternest test since the collapse of Archegos Capital Management in 2021 as they successfully navigated the dramatic fall from grace of AI-focused hedge fund Situational Awareness in late July”:

Dynamic margining practices automatically delevered Situational Awareness’s portfolio as AI share prices soared earlier in the year, sources said. That mechanically lowered the loan-to-value of banks' financing facilities giving them a greater cushion against potential losses.

Prime brokers also ensured they had additional protection on top of collecting large sums of margin as AI stocks started to slide. Chief among these protections was recourse to both Situational Awareness’s public and private investments including its prized Anthropic stake, sources said, meaning banks could always seize and liquidate those holdings if worst came to worst.

Such practices have been industry standard for years among the top lenders. However, several firms have also been updating models and risk protocols in recent years specifically to deal with the emergence of concentrated AI funds, sources said. 

 “There was never any risk of a credit loss,” said the senior bank trader. “[There was] adequate margin, the LTV was low and [there was] recourse to Anthropic.”

As hedge-fund blowups go, the Situational Awareness one looks pretty benign. Situational Awareness took money from investors who could afford to lose it, and invested it in accordance with its well-understood mandate, which was basically “get super long AI.” It borrowed some money from banks to do this, but the banks understood the risk and properly managed it. Situational Awareness made tons of money while its get-super-long-AI thesis was working, for basically good and correct fundamental reasons; then it lost much (not all!) of that money back when that thesis stopped working (temporarily?). Its blowup probably did further bring down the prices of its stocks — there was some contagion — but that was managed reasonably well; the sale to Citadel prevented a disorderly liquidation. Sometimes hedge funds lose money in suspicious ways, but sometimes they just lose money in regular ways.

If you went to a bank and asked for a loan against your shares of a ball bearings company, the bank would probably think about the value of your ball-bearings shares, and their volatility, and how long it would take to liquidate them. If you owned half a day’s volume of a large profitable public ball bearings company whose stock traded a lot with very little volatility, the bank would probably lend you a large portion of the market value of the shares. If you owned 60% of a small unprofitable private ball bearings company, the bank would probably lend you a lot less. For one thing, it would be hard for the bank to assess the value of your shares: Sure you could point to financial statements and funding rounds, but the market price of the stock is uncertain. For another thing, if you don’t pay back the loan and the bank needs to seize and sell your collateral, how could it sell it? It can’t just pound the stock out on the exchange; it would need to find a private buyer, an uncertain and risky process.

And there are various intermediate states. A newly public company will probably be more volatile than one that has been public for years; also, your shares in a newly public company might be subject to lockup restrictions that make it harder for the bank to sell them if you run into trouble.

And so if you own locked-up shares in a highly volatile, unprofitable, newly public company, you might expect banks to be reluctant to lend you money secured by those shares.

But you don’t own shares in a ball bearings company, do you? And if you own shares in a volatile, unprofitable, newly public giant frontier AI lab, banks will do anything for you. The Financial Times reports:

JPMorgan Chase is relaxing its approach to lending money against shares held by employees and early investors in companies that have recently gone public, as the US bank seeks to win clients from emerging tech giants.

JPMorgan’s typical policy is not to accept as collateral shares in a company that has gone public within the past 135 days. However, it told bankers ahead of SpaceX’s blockbuster initial public offering in June that it would lend against shares in Elon Musk’s rocket and AI company sooner, according to people familiar with the matter.

Bankers inside JPMorgan expect the lender to have a similar approach when Anthropic, the maker of the Claude chatbot, goes public, though no final decision has been made. JPMorgan earned $75mn from its role on the SpaceX listing.

JPMorgan’s move underscores the efforts asset managers are making to win business from the huge wealth being generated by the AI boom.

One point here is that, when trillions of dollars of AI wealth are being created, wealth managers need to compete to manage it, and you need to take some risk to be in the game.

The other point is that the risk here is, like, “we lend money to AI employees secured by their shares, they default in the next few months, we have to seize their shares and sell them, and the market for them has dried up.” I think it is quite rational for banks to think that that risk is very low — perhaps not for newly public companies in general, but for giant AI labs in particular.

Also, though, I wrote a few weeks ago about a margin loan to SoftBank secured by its stake in OpenAI:

“Borrowing against OpenAI to buy more OpenAI” is a decent description of the global economy right now. If OpenAI’s valuation collapses, then the banks that gave SoftBank this margin loan will be in bad trouble, but so will all the other banks. Might as well also do the margin loan.

“Ooh, if SpaceX and Anthropic collapse, we’ll lose some money on our private-wealth margin loans to early employees,” sure, but really that would be the least of your problems.

Broadcom Credit Risk Soars on Mega AI Debt Financing Backstops. Mexico Bonds Trade Like Junk After $130 Billion Bailout of Pemex. SoftBank Plans Record Retail Bond Issuance Amid AI Push. Thoma Bravo Conceded 40 Deal Sweeteners as Debt Talks Heat Up. Private Equity Finds New Way to Ride Out Cash Crunch. Private equity growth funds attract record first-half inflows as sector rebounds. OpenAI Claims Its New Chips Can Outperform Nvidia Processors in Tests. First Brands forced into liquidation by bankruptcy court. Bitcoin Tops $80,000 as Bullish Mood Returns to Crypto Market. Deutsche banker charged with embezzling €600,000 from wealthy clients. Lego invests in software-enabled bricks and other products to power growth run. A Billionaire CEO, a Hamptons Town and the Endless Fight Over a Seafood Shack. Scientists who turned to OnlyFans to fund marmot research receive crypto boost.

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Traditionally there are two main theories of corporate governance:

1. Shareholders are the owners of the firm, and they should have the final say in how it is run. Of course a board of directors and a chief executive officer run the company day to day, but the shareholders have the ultimate power to vote out the board and management if they’re doing a bad job.
2. Actually the shareholders are just suppliers of capital, and really the firm belongs to the visionary founders or their heirs. The company’s value comes from its long-term commitment to its mission, and you can’t trust outside shareholders — index funds, short-termist activist hedge funds, etc. — to be committed to that mission. The only way to preserve what makes the company special is to give the founders the final say over how it is run, even if they do not own most of its economic value. 

Both theories have their points, and my view is that each is appropriate for some companies. Even if you are an outside shareholder, you might rationally prefer that some of your companies be run entirely by their visionary founders, unconstrained by shareholders: The founders might just be better than the shareholders.

Recently, though, there is a third theory, mostly for giant artificial intelligence labs. The third theory is that AI is so powerful, transformative and potentially dangerous that AI firms must be run for the benefit of humanity. They shouldn’t be controlled by shareholders or by founders. They should be controlled by a wise body of philosophers with no economic stakes, whose only goal is maximizing the benefit of AI for humanity. Where you find these philosophers is an interesting question. Some AI theorists. Some effective altruists. Larry Summers. I don’t know.

The third theory was tested back in 2023, when OpenAI’s nonprofit board fired its visionary founder-CEO, Sam Altman, for like two days. That did not work! It turns out that if you are raising tens of billions of dollars from investors for a trillion-dollar business, you cannot actually give the final say over that business to a wise body of philosophers with no economic stake in the business. When I put it like that it seems obvious, but for a while OpenAI was pretty confusing.

Still, a big part of the pitch that the AI labs make to investors and employees is along the lines of “this stuff is too powerful to be left to index funds.” An AI lab that went public with single-class stock and no special control rights — one whose directors were just answerable to shareholders — would be suspicious; nobody would believe it was serious about AI. “AI is too important to be left to shareholders,” everyone thinks, even the shareholders.

The obvious approach — the one taken by SpaceX — is to fall back on founder control: You can’t trust shareholders to develop AI in a responsible way, and you can’t trust disinterested philosophers to maximize value, but you can definitely trust Elon Musk to do both. Arguably there are problems with this theory. 

I suppose the state-of-the-art answer is, like, both founder control and wise-disinterested-philosopher control? Not sure how that works, but the Information reported last week:

Anthropic has been preparing to give CEO Dario Amodei and other co-founders a class of stock with extra voting power to help insulate them from outside shareholder pressure, two people familiar with the matter said.

It would be the first time Anthropic’s leaders have extra voting power, which has become common practice for tech founders. It is all the more relevant for Anthropic because its co-founders hold relatively small ownership stakes in the company compared to other tech founders, another person said.. The company also is planning to maintain its existing body of nonshareholder trustees with a special class of stock to elect the majority of members to the company’s board of directors, a more unconventional buffer against outside shareholder influence. …

And Anthropic has given significant but narrow governance powers to its Long-Term Benefit Trust, a group of advisers who aren’t employees or investors. The trust, created in 2023, has one major formal power today: It can elect the majority of Anthropic’s seven-person board of directors. The company granted it those powers through Class T shares that have no economic power.

These trustees include former Federal Reserve Chair Ben Bernanke. The trust is down to just three members, from its usual five, after Mariano-Florentino Cuéllar, a former member of the California Supreme Court, exited this month to become Anthropic’s chief global affairs officer.

Sure, I mean, I have to say, being a disinterested Anthropic philosopher-overlord sounds cool and perhaps crucial for the future of humanity, but being an Anthropic executive sounds more lucrative. Anyway I guess the hierarchy at Anthropic is:

1. The founders have the most control;
2. The disinterested philosophers have some control; and
3. The outside shareholders have no control.

Which seems roughly correct, possibly as a governance matter and certainly as a marketing matter.

Leveraged exchange-traded funds give investors (usually) two times the daily performance of some underlying stock or index. A 2x ETF on Strategy Inc., for instance, might take $100 of investor money, borrow $100 and buy $200 of Strategy stock. If the stock goes up 3% in a day, the ETF will have $206 worth of stock, giving its investors a 6% return on their money ($106 of equity). But now it is only 1.94x levered, [1] and to give investors the same 2x exposure the next day, the ETF will have to buy more stock: It needs to own $212 of stock to give investors 2x the daily return on their $106 of equity. So the ETF will buy another $6 worth of stock, typically at the close of trading. Similarly, if the underlying stock goes down, the ETF will have too much leverage, and will have to sell some stock to maintain its 2x ratio. 

We have talked about this before: Leveraged ETFs, by their design, buy stock when it goes up and sell it when it goes down, creating “volatility drag” for their investors. Here, though, are two points about this daily activity:

1. It is predictable: You can generally see how much money the leveraged ETF has, and you can see how much the underlying stock has gone up or down, so you can predict how much stock the leveraged ETF will need to buy or sell at the close. This means that there is a trade to be done. If you are a hedge fund, and you know at 3 p.m. that a leveraged ETF will need to buy $100 million worth of some stock at the close, you can buy $80 million of that stock now to sell to the ETF at the close. This is, in a sense, a short-term version of the index rebalancing trade: Leveraged ETFs have a lot of predictable demand for a stock at a fixed time (the close), and arbitrageurs can smooth out this demand by buying stock ahead of the close to deliver to the ETFs.
2. It is (predictably) variable: The amount of stock that the leveraged ETF will have to buy (or sell) is a function of how much the stock has gone up (or down). If you are a hedge fund, and you know at 3 p.m. that a leveraged ETF will need to buy $100 million worth of stock at the close, you can buy $80 million of that stock now, which will push up the price, which will mean that the ETF needs to buy, like, $120 million of it. So buy $40 million more! Now the price has gone up more, so you can buy even more, etc. 

There are a number of ways to characterize this trade. One is that it is a sort of liquidity provision trade: You know that there will be a big spike of demand at the close, so you buy stock efficiently during the day to deliver into the demand at the close, smoothing out stock prices just like index arbitrage does. If nobody did this, then there would be big price jumps in the closing auction, as the leveraged ETF would have to buy a ton of stock at the close with no one to sell it, and the ETF would have a harder time achieving its return target. (In fact most leveraged ETFs use swaps or other derivatives, rather than borrowing cash to buy the underlying stock, and the swap counterparty might hedge its own closing price risk by trading during the day. [2] ) 

Another characterization, which we discussed recently, is that it is an “intraday momentum” trade: Hedge funds and trading firms observe that some stocks exhibit a lot of intraday momentum (if they go up in the morning, they tend to go up more in the afternoon), so they buy those stocks when they go up and sell them at the close. This has the effect of buying stock during the day to deliver to ETFs at the close, but perhaps not the intent. Momentum traders might not be thinking about leveraged ETFs at all; they might not know that leveraged ETFs exist. They just know that when some stocks (Strategy, SK Hynix) go up during the day, they tend to close even higher, so they buy those stocks.

A third characterization is that it is, you know, “front-running” or “preying on” the leveraged ETFs: If you know they have to buy a lot at the close, and you know that the higher the stock goes the more they will have to buy, then you can go ahead and buy a lot of stock to push up the price and sell to them. You are transferring value from them to you; you are pushing up the price, knowing that they have to pay it.

Here’s “Preying on Leveraged ETFs,” by Yinhong Zhao of Princeton:

We argue that arbitrageurs preying on the closing rebalances of leveraged exchange-traded funds (LETFs) contributed to the Korean market's extreme volatility in 2026. An LETF's mandated daily rebalance is sized by the day's return, which generates an upward-sloping demand at market close. Arbitrageurs therefore pre-position, enlarge the fund's order, and liquidate into the demand they have induced. Consistent with this mechanism, Korean stocks tracked by LETFs reverse about 60% of their first-day response to pre-open U.S. news by the next close, a phenomenon not exhibited in any control groups. Our quantification implies that self-referential rebalance raised SK Hynix's annualized volatility from 84.8% to 136.7% over nine weeks and transferred 19% of terminal wealth from the products' predominantly retail holders.

From the paper: 

A large mandated purchase at the close implies a predictable closing return, allowing arbitrageurs to profit by buying earlier in the day. Their trades bring forward the price impact and further increase the required rebalance, causing the closing price to overshoot and subsequently reverse. The LETF thus buys high and sells low in each cycle. This mechanism compounds the familiar “volatility decay” of LETFs and transfers the incremental losses to the counterparties whose trading magnifies the price movements.

And:

No market exhibited a comparably large loop gain before Korea introduced single-stock LETFs in 2026. Their listing was the largest in Korean ETF history: combined assets began at KRW 4.3 trillion and reached KRW 14 trillion within three weeks. During the post-launch period, the worldwide mandated rebalance in SK Hynix averaged 22.4% of the stock’s total daily traded value and reached 50.4% on the peak day. The resulting loop gains have no U.S. counterpart. The values for the treated Korean stocks exceed that of MicroStrategy, the most extreme U.S. single-stock complex, by more than a factor of two and exceed those of the U.S. index complexes underlying the benign evidence by an order of magnitude.

One thing that you might take away from this is that moves in leveraged ETFs (and in their underlying stocks) are exaggerated: If the stock goes down during the day, this dynamic will push it down more, it will close below its “correct” value, and it will rally the next day. (“Korean stocks tracked by LETFs reverse about 60% of their first-day response to pre-open U.S. news by the next close.”) This suggests that there’s a lot of intraday momentum but also a lot of next-day reversion, so maybe you should buy the dip. The Financial Times reports:

Investors poured billions of dollars into risky leveraged funds tracking semiconductor companies during July and August, even as those products suffered heavy losses, as they tried to position for a possible rebound in the sector.

The biggest leveraged exchange traded fund tracking chip stocks — called Direxion Daily Semiconductor Bull 3X Shares — attracted almost $7bn of net inflows during July and the first two weeks of August, according to Morningstar data. …

“Buying the dip and selling the rip” has been a popular strategy in many leveraged ETFs, said Ben Snider, US equity strategist at Goldman Sachs, referring to how investors have been buying as the market falls and then selling when stocks rally.

Arguably leveraged ETFs help create the dips and rips, and can be used to reverse them.

Most insider trading cases are dumb, but it’s hard to know what that means. Perhaps it means that most insider traders are dumb: A junior banker texts his buddy about a deal he’s working on, the buddy buys short-dated out-of-the-money call options on the target, the deal is announced, the buddy makes like $50,000, he texts the banker “hey bro thanks for the tip where should i send your bag of cash ;)” and the banker replies “stop texting about this, i do not want to go to prison for this criminal insider trading we are doing lol.” 

Or perhaps it means that most insider traders are smart, and only the dumb ones get caught. If you have never bought options before, and suddenly you put all your money into short-dated out-of-the-money call options on a merger target, probably an alarm bell rings at the US Securities and Exchange Commission, and they show up and ask to look at your phone, which is inevitably full of texts with a banker on the deal. But maybe you don’t do that. Maybe you regularly get insider tips in person and trade on them and make lots of money and don’t get caught because (1) you do a lot of legitimate trading, so your successful insider trades don’t look suspicious and (2) you don’t put the crimes in writing.

Anyway here’s an SEC case from last week against Gavin Wolfe, a retired investment banker who allegedly made $18.5 million insider trading on a merger, and Jason Satsky, his former colleague who worked on the merger and allegedly tipped him. Here is the complaint. What is interesting here is that:

• These guys were not junior analysts: Satsky was global head of energy and utility investment banking at Bank of America, and Wolfe was also a managing director at BofA before he retired to “manag[e] his own investments and operat[e] businesses that he owns or controls.”
• They did large size: Wolfe ran a portfolio “valued at approximately $260 million,” and bought $53 million of stock (not call options!) in the merger target, making $18.5 million of profit when the merger was announced.
• They did not go around emailing about it: The SEC doesn’t quote any messages between them before Wolfe bought the stock, [3] and there is no direct evidence that Satsky told Wolfe anything at all. Instead, the evidence is that “the two men attended a nationally televised [Duke] basketball game together at Madison Square Garden,” [4] and that “Within minutes of the November 9 basketball game ending, after spending approximately three hours with Satsky at Madison Square Garden, Wolfe created a calendar entry for himself at 12:12 a.m. that read ‘SJi and njr,’ using the New York Stock Exchange ticker symbols for South Jersey [Industries Inc., the merger target] and another company. He scheduled the entry for 9:15 a.m. that morning, November 10, 2021.” And then in fact he started buying the target’s stock.

One possibility here is that these are careful experienced professionals who insider traded without creating dumb obvious evidence, and were nonetheless caught by the SEC’s increasingly sophisticated enforcement apparatus. Another possibility is of course that they are just friends who watched some basketball together, and that a former utility investment banker running a $260 million personal portfolio might have had his own reasons for investing in a local utility company. Bloomberg’s Nicola White and Ava Benny-Morrison report:

In a Friday statement, Satsky’s lawyer, Bob Anello, denied the allegations. “The enforcement action brought by the SEC is unfounded,” Anello said. “Jason did not provide Gavin Wolfe, or anyone else, with material nonpublic information regarding South Jersey Industries Inc. He did not breach any duty of confidentiality, and the SEC has no evidence that he did so because it did not happen.”

Wolfe’s lawyer, Reed Brodsky, also denied the allegations. “The SEC is pursuing this case despite being unable to identify what was allegedly disclosed, or how it was disclosed, while ignoring sworn, immunized testimony and contemporaneous documents confirming that Mr. Wolfe bought South Jersey shares based on an independent investment thesis,” Brodsky said.

Sure sure sure “he bought stock in a merger target the day after sitting with the target’s banker at a basketball game” sounds bad, but it doesn’t prove anything. And there doesn’t seem to be any other proof! Either it’s an innocent coincidence or it’s pretty good insider trading.

One concern about the rise of artificial intelligence is that it will displace entry-level employees in professional services. AI, in this theory, can do the basic tasks of a junior banker or lawyer or consultant, but it does not have the nuanced understanding or personal client relationships of a senior rainmaker. So you could have a viable business made up of rainmaking senior partners who bring in the business and AI agents who actually do it. But these businesses have historically operated on an apprenticeship model, and if there are no junior employees — or if there are fewer junior employees who are not putting in long hours of model-building and presentation-formatting — then where will future senior rainmakers come from?

At this point, though, these worries seem overblown, in part because, what, the senior rainmakers are going to learn how to use AI agents to build financial models? Come on. They’re busy making rain. You still need junior employees, but their job has changed. Instead of building financial models or drafting presentations, their job is to supervise the AI agents, and to teach the senior partners how to supervise the AI agents. Bloomberg’s Nil Codina Martinez and Zainab Haji report:

Banking interns typically spend the summer learning the ropes. But the industry’s push to adopt artificial intelligence means some have even found themselves doing a bit of teaching of their own this year.

A private banking intern at one lender in London said they were given the task of improving their team’s embracing of AI.. That included building new agents to streamline to-dos and giving senior bankers demonstrations on how to use the technology in drop-in help sessions — a process they compared to teaching parents how to use a phone..

The role reversal is an extreme sign of how the entry-level banking experience is shifting as banks look to put Gen Z’s digital proficiency to use. Bloomberg News spoke to nearly a dozen summer analysts and interns who said that AI has altered what’s expected of them even in non-tech related programs.

Presumably this is not a stable equilibrium, and if this year’s interns do a good enough job of automating everything and/or teaching senior bankers how to automate everything, there will be no need for interns next year. But there is probably some lag, and AI is moving fast enough that next year’s interns will have all new ways to automate things.

Here is a Wall Street Journal article about how including quirky interests on your résumé is good actually:

In a cutthroat white-collar talent market, where AI looms as a job-security threat, some recruiters are encouraging job seekers to get ahead by emphasizing decidedly human qualities like a canasta addiction or a weakness for pickling vegetables.

Advocates say an offbeat pastime can tempt a glassy-eyed recruiter to reread your application and serve as an icebreaker in interviews. [Legal recruiter Kate] Reder Sheikh said more than half the associates she placed last year listed hobbies—a jump from a few years ago. …

Fred Cibelli, a technology principal at EY in New York, said that, assuming the candidate fulfilled the job requirements, he would want to hear about their regard for olive oil in an interview. “There’s only so many times you can talk about what’s going on with open versus closed models in AI,” he said. He has noticed a small increase in hobbies appearing on LinkedIn profiles and résumés lately and said he would like to see more.

Every once in a while I hear from a friends or former colleagues that they get résumés with “Money Stuff” in the “interests” line, and I want to encourage that. Those people get hired, probably, though this is not career advice.

Juicy Yields Draw Junk Bond ‘Tourists’ to High-Grade AI Debt. Nvidia’s Trillion-Dollar Chip Market Has Friends and Foes Closing In. Anthropic’s best AI model struggles to attract users as cheaper tools thrive. Paramount Prepares to Begin Early Settlement Talks With California Officials. Heirs to the Jack Daniel’s Fortune Are Fending Off a Takeover — and a Rogue Cousin. Hedge fund Saba takes on Baillie Gifford in new board battle. Startup Founders Are Working Harder Than Ever to Keep Up With Their AI Agents. Elite Private Schools Offer Financial Aid to Families Making $500,000. The age of the populist financial scam. PublicSquare “has also shut down a television show that executives hoped would drive conservatives to its marketplace and sold off a diaper brand marketed to antiabortion consumers.” I Can’t Stop Watching This Amazon Delivery Drone Drop Someone’s Package in Their Pool.

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Programming note: Money Stuff will be off on vacation next week, back on August 24. 

We have talked a number of times around here about businesses hedging real-world risk on prediction markets. Some business faces some risk from some real-world event: “If Zohran Mamdani is elected mayor, my business of selling Manhattan pied-à-terres will dry up,” or “if Cardi B doesn’t perform at the Super Bowl, I will sell less Cardi B-themed merch,” or, overwhelmingly, “if my local sports team does not make the playoffs my bar will not sell enough beer to pay the rent.” Prediction markets like Kalshi offer bets on those events, with real-time prices that seem to efficiently reflect their actual probabilities.

So you can go to Kalshi and buy, effectively, insurance. If you will make $100,000 selling beer if your team makes the playoffs, and $0 if it doesn’t, and Kalshi’s odds show that it has an 80% chance of making the playoffs, you can buy $100,000 of “No” contracts for $20,000 (20%). If the team makes the playoffs, you lose your $20,000 but make $100,000 on beer; if it doesn’t, you sell no beer but make an $80,000 profit on the hedge. Either way, you get $80,000: You’re hedged.

Two points about this. First, prediction markets are mostly for sports gambling, so many of these stories are about businesses hedging real-world sports risk. If you’ve got a European soccer club that might get relegated, that’s perfect for prediction-market hedging. But prediction markets are very invested in demonstrating that they are not purely sportsbooks, so when Kalshi helps a business hedge a non-sports risk I get a lot of emails.

Second, I have been imagining that you “go to Kalshi” and buy a big chunk of contracts to hedge your business risk. In practice, the actual exchange-traded liquidity in many of these contracts is limited: If you want to hedge your US presidential election risk, you can probably buy a lot of size without moving prices much, but the actual volume for even soccer relegation contracts is pretty small relative to the economic impact  of relegation. If you went to buy millions of dollars of those contracts, you would move the price against yourself so much that the insurance wouldn’t be worth it.

And so often the way these hedges work is as block trades, or, if you prefer, over-the-counter insurance contracts. That is:

• A business wants to hedge some event.
• Kalshi lists a contract on the event implying that its probability is 20% or whatever.
• You could not go buy $10 million of that contract on Kalshi, at 20 cents on the dollar or maybe at all.
• But you can go buy $10 million of that contract from Susquehanna International Group, the big proprietary trading firm that has made a push into sports and prediction markets. (Or perhaps some other big prop trading firm, but so far mostly Susquehanna.)
• Some broker will set you up with Susquehanna, who will sell you the contract.
• The pricing of the contract will be more or less the result of bilateral negotiation between you and Susquehanna, but it will probably be based on the Kalshi pricing. Susquehanna makes markets on Kalshi, it uses Kalshi market prices as an indicator of probability, and it will probably base its over-the-counter trading prices on those public market prices. (Here is an Odd Lots episode with Susquehanna’s head of prediction markets, who makes this point: Susquehanna will trade large blocks using the exchange pricing as an anchor.)

You could imagine a future in which prediction markets are so deep and liquid that it would be no problem to just log into Kalshi and buy $10 million of insurance against random events by clicking a button. But that would be weird. Who would be providing millions of dollars of resting liquidity, at low bid/ask spreads, on all sorts of weird events? If you are just bopping around trading a few hundred dollars’ worth of “Ipswich Town is relegated from the Premier League,” and then all of a sudden someone wants to buy $10 million worth, surely you are getting adversely selected.. The far more sensible thing is for most prediction markets to keep working like this: A public market trading relatively low dollar amounts but with pretty efficient pricing, and then bilaterally negotiated over-the-counter contracts for businesses that want to hedge in big size. [1]

What about a public market with a zero dollar amount? That is: What if you had some event risk that you wanted to hedge, but that wasn’t covered by a contract on Kalshi? (What if your bar will sell $1 million of beer if the local Little League team wins the county playoff, and $0 if it won’t?) You might call up Susquehanna anyway. “Hey Susquehanna,” you could say, “I want to buy insurance against this weird niche risk. Could you price it for me?” Susquehanna is very much in the business of pricing and holding weird niche risk. Often Kalshi market prices are an input to its pricing, but maybe that’s not strictly necessary. Susquehanna could go research the underlying event, figure out the probability of it happening, add some spread for itself, and quote you a price. You could pay Susquehanna a $100,000 premium for a contract that pays you $1 million if the event occurs and $0 if it doesn’t. It’s a pure over-the-counter bilateral prediction market, just you against Susquehanna.

Or almost. Here’s this:

Up until now, there was no way to directly cover the risk that a state law might shutter your goat herding business. Prediction markets changed that. 

Northern California rancher Tim Arrowsmith owns a company called Western Grazers. The business provides targeted grazing to reduce wildfire risk by clearing dry brush in hard-to-reach areas. Arrowsmith has over 4,000 grazing goats and employs eight herders who work around the clock to manage the goats. 

A recent legislative interpretation eliminated California’s longstanding wage framework for goat herders, which had been designed for the realities of around-the-clock livestock care. (Interestingly, sheep herders were spared from this interpretation of the law). If California doesn’t re-examine this law by the end of August, Arrowsmith’s labor costs could rise by roughly four times, putting his business, the jobs of his longtime herders, and an important wildfire prevention service at risk.

Arrowsmith turned to Castle, a startup that leverages financial products to mitigate risk for businesses. Castle worked with Susquehanna, one of the world’s largest proprietary trading firms, to create a hedge: they translated a highly specific regulatory exposure into a tradable market by defining objective settlement criteria and structuring the contract. Castle provided the coverage, used Kalshi, the largest federally regulated prediction market in the US, to list the market, and transferred the risk to Susquehanna, which priced the contract and provided the liquidity for the trade. …

The contract pays up to $500,000 if California does not fix the goat-herder wage rules by the end of September 2026. Arrowsmith paid a fixed premium for the protection. If lawmakers restore the previous rules or pass a similar solution, the contract expires with no payout. That is the outcome Tim is hoping for. ...

“Businesses face risks that traditional insurance often can't cover,” said Lucas Cavalieri, CEO of Castle. “A rancher should be able to hedge a bill just like a farmer hedges wheat.”

“Prediction markets are becoming a practical risk management tool, not just for institutions but also for small businesses facing real-world uncertainty,” said Eric Passmore, Senior Trader, Prediction Markets, at Susquehanna. 

“Before Kalshi, there was no practical way to hedge against a specific legislative outcome like this,” said Nicolas Hull, Director of Business Development at Kalshi.. “We hope businesses of every size and industry can use Kalshi to manage risk. This is just the beginning.”

That’s the Kalshi press release. Here is a good CNBC story with more of the background. Basically goat herders are on the job 24 hours a day, and now you have to pay them at least minimum wage for that whole time, which makes them expensive.

Here is the actual Kalshi goat herding contract. I want to make a few points about it. First, it was listed last week, purely for this trade.. This is not a case where Kalshi had an active market in California goat herding legislation probabilities. This is a bilateral trade between the goat guy and Susquehanna, which was then listed on Kalshi. CNBC explains the pricing:

Led by Susquehanna senior trader Eric Passmore, the firm established pricing and the contract terms on Kalshi. Constructing those provisions came after deep research on California’s goat herding business, speaking with industry professionals and connecting with Arrowsmith, Passmore said. 

The contract has a $500,000 payout that Arrowsmith paid a 10% premium or $50,000 on.

Second, as of about noon today, Kalshi showed a volume of about $500,476 on this contract. It was listed purely for the one guy’s over-the-counter trade, but now it is listed, so if you want to bet on California goat legislation now you can. Intriguingly, the contract has traded (in tiny size) at lower probabilities than 90%, meaning that arguably Susquehanna significantly underpriced this risk. The goat herder put one over on Susquehanna!

Third, the contract specification is long. The essential point of the contract is that it “Resolves Yes if California authorizes an alternative wage or provides qualifying relief from goat herder wage and overtime obligations before Oct 1, 2026,” and there are any number of ways in which that could happen. There could be a new statute, or a new state regulation, or a court order, or “an executive order or proclamation issued by the Governor of California.” Whatever it is must reduce the monthly cost of employing goat herders to no more than the cost of employing sheep herders.

This is not a simple yes/no question about whether a law will pass; this is a quite detailed contract specification designed to insure this guy’s actual risk. The goal is to have very little basis risk between the contract and the guy’s actual business. It is, as it were, loss-based insurance, not purely  parametric insurance.

That is a weird thing to trade, if you’re not a goat herder! If you’re just logging on to Kalshi to speculate, you might speculate on whether the California legislature will pass a law, but it’s a little weird to speculate on whether this guy’s goat herding costs will come down in any of a number of legal ways. This is not a product that Kalshi is listing because it thinks there will be a ton of retail investor demand. This is a product that Kalshi listed for the one trade, and that is optimized for that trade.

Fourth, why did Kalshi list it at all? If this is a bilateral trade between the goat herder and Susquehanna, why does it need to be publicly listed on Kalshi? (“Kalshi took a ‘backseat’ in facilitating the hedge,” notes CNBC.) Some possible answers:

1. Publicity. I was sent this press release three times. Kalshi wants to be seen to be in the business of hedging real world risk, and no one can resist a goat herding contract.
2. Neutrality. You could imagine Susquehanna and the goat herder negotiating the terms of the insurance contract, signing it, and then working in good faith to pay claims or not depending on how the event resolves. But that is a potentially complicated and adversarial process, and there’s a risk that (1) the goat herder’s costs don’t come down but (2) Susquehanna doesn’t pay anyway, because of some gap or dispute about the contract terms. But if you get Kalshi to write and list the contract and determine its resolution, maybe you trust Kalshi more: Kalshi is not on either side of the trade, and its incentive is to produce fair outcomes so that it can attract more business. Kalshi can serve as a neutral referee for bets like this.
3. Laying off. In theory, now that the event is listed on Kalshi, Susquehanna could lay off some of its risk by buying “No” contracts. Or Arrowsmith could take some immediate profit by selling his “No” contracts, which he bought at $0.10, for $0.20. In practice, the goat herder pay market is never going to be liquid enough for that to matter, but other contracts might be.
4. Regulation. Insurance is a highly regulated business, and Susquehanna can’t actually sell Arrowsmith something called “insurance” against his goat cost risk. It is selling him an event contract, which is a kind of “swap” under the US Commodity Exchange Act. That law says that “It shall be unlawful for any person, other than an eligible contract participant, to enter into a swap unless the swap is entered into on” a registered exchange. (An “eligible contract participant” basically means a large institutional trader; the goat herder wouldn’t qualify.) That is, it’s actually illegal for Susquehanna to do this trade as a pure bilateral over-the-counter contract. But if you do it as a bilateral contract that prints on Kalshi, it’s fine.

That last answer is probably the most important. As I have noted before, the definition of a “swap” is quite broad, and if you take it literally you might conclude that every sort of bet has to be traded on a registered prediction market like Kalshi. Arguably, now, anyone who wants to offer a business a weird bespoke event hedge like this has to trade it on Kalshi. 

The way financial regulation works in the US is that a regulator proposes a new rule, and then the general public has the opportunity to submit comments. The comments might support the rule, or oppose it, or suggest changes. Then the regulatory agency has to consider the comments and come up with a final rule. The comments are not binding — if there are 100 comments in favor of the rule and 900 against, it’s not like the regulator has to withdraw the rule — but they have some effect on how the rule is perceived. “SEC Expected to Change Quarterly Earnings Rule Despite Public Backlash,” the Wall Street Journal reported last month: It can do that, sure, but it looks bad.

Many comments will naturally come from financial firms whose businesses will be affected by the new rule. They tend to submit informed, articulate, helpful comments written by expensive lawyers; they will naturally be  self-serving, but they will often make the rules better. But many rules are intended to protect ordinary investors, and so there is some desire to get feedback from ordinary investors. 

How would a regulator get feedback from ordinary investors? One possibility is that an ordinary investor would learn about the rulemaking, go to the regulator’s website, read the proposed rule, have some thoughts, and submit a comment. This is not impossible, but it would be a little weird. Who does that? We talked in May about a good comment on the quarterly earnings rule submitted by “/r/wallstreetbets,” a Reddit retail investing community; in a sense they are ordinary investors, but they’re unusually dedicated and online. Ordinary ordinary investors are less likely to submit comments.

And so the normal way to get feedback from ordinary investors works something like this:

• Some financial firm, trade group, lobbying organization, etc. writes a comment letter reflecting what they think an ordinary investor might say. “This new rule will help me save more money for retirement,” or whatever.
• They go out and find some ordinary investors.
• They try to get the ordinary investors to submit the comment that they wrote. “This new rule will help you save more money for retirement, so you should submit a comment letter saying ‘this new rule will help me save more money for retirement,’ under your name,” they say.

This is to some extent a volume game: If you get 200,000 people to submit a comment opposing a rule, that makes it easier to argue that ordinary investors oppose the rule. Will most of those comments be form letters containing exactly the language that you wrote? Sure. Will that be embarrassing, undermining the perception that ordinary investors actually care? Oddly, no; everyone understands that this is the way the sausage is made.

Will some of those form letters be submitted by dead people, or otherwise attributed to people who did not actually submit them? Oh sure, sure, whatever. None of this is real. It’s not voting. The comments are not binding.. The comments are valuable (1) to the extent they make convincing arguments and (2) otherwise as a sort of vague directional indicator of quantity. If only 199,000 of the 200,000 people who oppose a rule actually exist, that doesn’t change the analysis much.

Still it is embarrassing. We talked a few years ago about a proposed US Securities and Exchange Commission rule cracking down on proxy advisers, which was supported by heartfelt comments from ordinary investors, many of whom either didn’t actually submit the comments under their names, or allowed their names to be used without reading the comments. I wrote that, if you favor limiting the power of proxy advisers, you can just say that, but:

It’s better if you can find an 83-year-old Army veteran to say “how disgusted I am that my financial investments are being used as a political pawn,” or a retired Marine veteran” to say “I didn’t invest my money to have others make any kind of political statement.. I invested my money to be able to leave something for my children and grandchildren in order to make their lives a little better,” or a hard-working single mother to say “Let the folks who work at proxy advisory firms bring about social change on their dime, not mine,” or a retiree to say “Call me naïve but I certainly didn’t sign up for groups with their own agenda voting my proxy!”

Obviously you can’t find people to say that, because no normal human actually pays attention to or cares about any of this. But if you work on one side of the issue, you can convince yourself that normal people should care about it, and then you can imagine what they might say if they did care, and then you can write it down, and then as a final, only-somewhat-fraudulent step, you can go find an ordinary person to sign the letter you wrote.

If they’re dead it’s harder, but not impossible. We have talked about proposed rules to allow more private assets (and crypto) in 401(k) retirement accounts, which is something that (1) the financial industry really really really really really really really really wants and (2) some ordinary investors might want, hey why not. So a lot of ordinary investors submitted comments supporting the rules, and some of them were fake or dead, Bloomberg’s Noah Buhayar and Jeff Kao report:

Heath Oderman was surprised to learn that a comment supporting a plan to get more Americans’ retirement plans invested in private equity and other “alternative” assets had been submitted to the federal government in May under his mother’s name.

She couldn’t have done it, he said, because she died in December.

“That’s not my mom,” he wrote in an email to Bloomberg News after being alerted to the comment, which was submitted to the US Department of Labor on May 3 under the name Danna Oderman. “The language is nothing she would ever have used while on this Earth.” …

The comment was one of nearly 12,000 showing signs that they may have been manufactured to resemble grassroots support for the controversial measure. … Bloomberg attempted to contact dozens of the people whose names were attached to the 12,000 comments and found five cases in which people said they — or their family members — did not submit them. 

So some fakes, but against a pretty low baseline of reality:

More than 92% of the submissions, both for and against the proposal, followed some sort of form letter.

The most popular of these was a petition that urged the government to scrap the proposal, arguing it would expose people to “expensive fees and dangerous levels of risk.” That message was sent more than 30,000 times, representing about two-thirds of all submissions.

Ninety-nine percent of these commenters included an email address and location in their submission, and many had personalized messages or signatures at the end of the form letter. Bloomberg attempted to contact three dozen of them. Most didn’t respond. But Judith Bergson, a social worker in Massachusetts, confirmed that she sent the comment and would be “terribly upset” if private investments were put into her retirement plans. “I’m definitely a person,” she added in an interview.

The advocacy group Americans for Financial Reform drafted the form letter that Bergson signed and worked with partner groups to disseminate it, said Ericka Taylor, the organization’s co-executive director. 

Incidentally, I have some firsthand experience with having financial regulatory comments submitted under my name! The SEC is considering new rules about sports gambling exchange-traded funds. I have written about this, because it is wild. At the end of one column on the topic, I wrote:

A couple of weeks ago, we discussed the US Securities and Exchange Commission’s “request for public comment on exchange-traded funds (ETFs) seeking to invest in innovative asset classes or engage in novel investment strategies” (sports gambling). You can submit comments here. I suppose this is my submission.

Here are the comments that have been submitted on that proposal.  One is from me. It used to contain the text of that column, though now it reads “Copyrighted material redacted” with a link to my column. I did not actually submit it, and I am not entirely sure how it ended up on the SEC’s website. I can’t really complain: I did write it, and I said “I suppose this is my submission,” so someone submitted it as a comment. And I like to think that the SEC will take it into consideration in writing rules about sports gambling ETFs.. But the point is that there’s a comment on an SEC rule submitted under my name but that I did not submit. Could happen to anyone, really.

I have, a couple of times, espoused a tidy theory that hedge funds can smooth out lockup expiries for big initial public offerings. The theory is:

• When a company is added to an index, there is quite predictable demand for the stock, on a fixed date, from index funds. There is a big business, done mostly by multistrategy hedge funds, of smoothing out this demand. Basically the hedge funds buy the stock before the index rebalancing date, and then sell it to the index funds right on the rebalancing date. Therefore the stock shouldn’t go up too sharply when it joins the index; the hedge funds effectively average it in.
• When a newly public company has a lockup expiry, there is predictable supply: A bunch of early investors are finally allowed to sell, so more shares are available than were before. This also happens at a fixed date, and hedge funds can similarly smooth out the supply. Basically they can sell short the stock before the lockup release date, and then buy it from the insiders on the lockup release date. Therefore the stock shouldn’t go down too sharply when the lockup expires; the hedge funds can average it in.

This is only a loose analogy. For one thing, it is relatively easy to buy stock ahead of an index rebalancing; it is harder to short stock ahead of a lockup release. Shorting stock requires you to borrow it and pay a fee to your stock lenders, and when a company is newly public, with only a limited float, it is often hard to borrow. If there aren’t a lot of shares available to borrow, or if the fees are high, hedge funds will not be able to pre-sell all of the locked-up stock.

For another thing, index demand is quite mechanical: You can more or less count how many shares the index funds will need to buy, and then buy those shares to sell to them. The index funds are rules-based and price-insensitive, and there is a lot of data (from previous index adds, from quarterly rebalancings) about how they behave. Lockup-release supply is much less mechanical: You can count how many shares will be released from the lockup, but it is hard to guess how many shares will actually be sold. Lots of early investors are true believers and won’t sell when their lockups expire; others are price-sensitive and will sell at high prices but not low ones; others plan to dump all their stock but are on vacation on the lockup release day and only get around to it next week. You can sort of guess “well, when the lockup expires, some shares will come up for sale,” but it is hard to quantify precisely.

SpaceX had its first post-IPO lockup release on Aug. 7, and on Aug. 6 I wrote:

Really the expected price of SpaceX stock tomorrow, after the first phase of the lockup is released, should equal the closing price of the stock today: This is a well-anticipated event, and all of those short sellers have essentially been selling tomorrow’s shares today. That said, the variance around tomorrow’s price is high.

In fact SpaceX closed that day at $114.92 and opened the next day at $114.965, so in the narrowest possible sense I was right, but more broadly I think it is fair to say that the SpaceX lockup release was not especially smoothly averaged into the price. In fact, it seems to have been overdone: The stock got as low as $104.84 on Aug. 3, it rallied into the close on Aug. 6, and it has traded up ever since. Bloomberg’s Carmen Reinicke reports:

Since SpaceX went public in June, Wall Street had been dreading Aug. 6, when the first lockup preventing early investors from selling the stock expired and millions of shares were set to flood the market.

They needn’t have worried.

SpaceX shares have been on a tear since the lockup ended, soaring 35% in just five sessions, adding roughly $500 billion in market capitalization, and vaulting back over their $135 initial public offering price. The surge bucked concerns about an impending wave of selling and gave investors optimism that future expirations won’t be as painful as feared.

“We’ve gotten through the big hurdle, which was the unknown,” said Andrew Plum, managing partner and investment committee head at Loxahatchee Capital, which holds SpaceX shares. “It’s like the market discounts a negative event, maybe too much, as it’s waiting for that information to come to pass. And then it’s usually not as bad as what was discounted or as expected.”

One way to phrase “the market discounts a negative event, maybe too much” might be “shorts sold more stock ahead of the lockup release than was actually sold in the lockup release.”

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