I Suspected Someone Was Propping Up SPCX, But No One Was

2026-08-06 original Engineering

For a while now, I’d been watching one stock’s order book every day.

It was SpaceX (SPCX), which listed on June 12. The IPO price was $135, and net proceeds from the public offering were about $85.7 billion. It was one of the largest capital raises in history.

I had a reason to watch it. On the day of the listing, I wrote an article about the company’s multiple, questioning how a valuation of 245 times the Starlink business’s EBITDA could possibly hold up. Having voiced that much objection, I felt obligated to see how it played out.

After the listing, the stock price fell in almost a straight line, from a June high in the $225 range down to the low $100s by early August. Cut in half.

The decline itself was exactly what I expected. I’d publicly stated my discomfort, so let me say this plainly: no surprise there.

What caught my attention was something else.

The discomfort of the same movement, every single day

The same pattern repeated itself, identically, for several days running.

The open would drop about 5% from the previous close. Once it fell below $110, buying would suddenly kick in. It would recover to $115. Then it would barely move for the rest of the day, drifting to a close. The next day, the same thing.

This didn’t look like natural price movement.

In a stock that kept falling, buying appeared every time it hit a specific price, and it never rose above a certain level. It looked as though someone was “defending this level.”

The hypothesis I came up with was a simple one: maybe someone with an interest in propping up this stock, backed by financial firepower, was buying to support it.

There was a basis for this. This stock’s float was only about 5% of shares outstanding at listing. Relative to daily trading volume, the room for a large player to move the price was comparatively large. A structure where a small amount of money could shape the price did, in fact, exist.

Let me state the conclusion up front: this hypothesis was wrong.

More precisely, the phenomenon was real, but there was no actor behind it. What follows is the record of my re-investigation.

If it were a support operation, it wouldn’t be doing what it’s doing

The first thing that fell apart was the theory of official price support.

Stabilization by underwriters is normally carried out by buying back unexercised portions of the over-allotment. But in this deal, the greenshoe had been fully exercised within a few days of listing, leaving no ammunition to buy back with. On top of that, the stabilization period typically runs about 30 days, and it had already expired by mid-July.

The company’s own buyback lacks motive too. Right after raising $85.7 billion, with quarterly capital expenditure running at roughly $18 billion, there’s no logic for a company to spend cash defending its stock price. It was also inside the blackout period ahead of earnings. And if insiders were buying, Form 4 filings appear within two business days, so that could be checked as well.

That much was elimination by process. The real problem came next.

If there were a support actor, the price wouldn’t move this way.

The level that was supposedly being defended was actually stepping down in stages: $110.85, $109.53, $108.66, then $104.83. One step lower each day.

If a deliberate actor were defending the price, it would stick to a level. A defense line that drops lower every day is not a defense.

There was one more thing. This buyer was also selling at $115.

Capping the rebound was part of the same movement. Buy at the bottom, sell at the top. A support actor wouldn’t sell at the top.

The phenomenon was real. But it wasn’t “a force creating a floor.” It was “a force compressing the trading range.” That’s the core of where the hypothesis went wrong.

The real culprit was directionless inventory adjustment

It clicked into place when I looked at the options market. Let me write this without jargon.

There are three characters: the person who buys the option, the dealer who sells it, and the stock market itself.

The dealer’s business is to earn something like a fee by selling options. It’s not a business of betting on whether the stock goes up or down. That’s the starting point.

Say a dealer sold “the right to buy at $115.” If the stock rises, that’s a problem for the dealer, because the obligation to deliver becomes real. So the dealer buys the stock in advance. But not all of it. If the stock is still at $110, the chance that right gets exercised is low.

So the dealer holds only the amount scaled by that probability. A 30% probability means holding 30%; 50% means 50%; 70% means 70%.

As a result, the dealer buys more each time the stock rises, and sells each time it falls.

Relationship Between Dealer Share Count and Stock Price An S-curve where the number of shares the dealer should hold increases as the stock price rises

Shares to hold (out of 100)

30 shares 50 shares 70 shares 95 shares The higher the price, the more the dealer buys

$110 $115 $118 $125 Stock price

Figure 1: Relationship between shares the dealer should hold and the stock price

There’s a flip side too. A dealer on the other side of “the right to sell at $110” benefits when the stock falls. To stay neutral, the dealer buys stock to offset that gain. The lower it goes, the more gets bought. When it recovers, the dealer sells it back off.

Buy on the way down, sell on the way back up.

This was the pattern I’d been watching for days.

The dealer hadn’t turned bullish or bearish. It was mechanically adjusting inventory to honor its obligations, in response to price. From the market’s point of view, it still looks like buy orders coming in, but there’s no intent behind it.

Supporting at $110 and capping at $115 on the same day isn’t a contradiction either. The $110 right and the $115 right are different products, and which side the dealer sits on differs between them. That’s why behavior changes depending on the price band.

The sign flips depending on the price band Below $110, buying increases as the price falls; above $115, buying increases as the price rises

Buying increases as price rises Hedging the call side. The rise accelerates $115

Buying and selling offset each other Intraday range gets compressed $110

Buying increases as price falls Hedging the put side. The decline gets dampened

Figure 2: Even within the same day, the sign flips depending on the price band

This mechanism isn’t loyal to any price level. It follows wherever the options open interest happens to be concentrated. That’s why the defense line drops a little lower every day.

That said, I haven’t confirmed the actual open interest figures for this stock. I can only say this is the most straightforward explanation consistent with the order book’s behavior.

My sense that “someone is propping up the price” was right as a description of the phenomenon. It was only wrong in assuming the actor was a person.

The earnings report was the test

Explanations of this kind can be stretched to fit almost anything after the fact, so it was necessary to put this one in a testable form.

As it happened, the first post-listing earnings report came on August 4, with a large lock-up expiration scheduled two business days later: 911.5 million shares, 1.4 times the roughly 639 million shares sold in the IPO.

The prediction I made was this:

Even if the numbers are good, the price won’t recover, because what’s determining it isn’t earnings but the supply schedule
If the company made an announcement touching supply, such as voluntarily extending the lock-up, that would change things, but they won’t do that. It would mean forcing early investors and employees, who already have the right to sell, to give up their gains for the sake of outside shareholders
So expect some rallying along the way, but ultimately a decline

The earnings numbers were a massive beat.

Revenue was $7.81 billion (versus estimates of roughly $6.8-6.9 billion). Net loss was $541 million, a loss of 9 cents per share, far better than the expected loss of 26 cents. Operating loss shrank from $970 million in the same quarter a year earlier to $143 million. All three business segments beat consensus. Starlink subscribers doubled year-over-year to 12 million. Full-year guidance was raised for the first time in the company’s 24-year history.

The stock fell 8% in after-hours trading.

It has settled right back at the level it was at the day before earnings.

What lay inside the “AI company” was an equipment leasing business

Why does it fall on a beat? Here another structure comes into view.

This IPO was sold as a “rocket company,” but looking at the revenue mix, communications is 55%, AI is 33%, and space is 12%. Rockets are the smallest piece.

The AI segment that’s getting all the attention had revenue up 247% year-over-year to $2.56 billion. The headline is flashy.

Reading the breakdown, of the $1.82 billion increase, $1.6 billion came from data center rentals. It wasn’t Grok usage growing, and it wasn’t AI products selling. It was revenue from renting out facilities full of GPUs to other companies. Ad revenue from the former Twitter, which sits in the same segment, was down 14% year-over-year to $367 million.

The contract structure matters even more. One major customer is leasing a Memphis data center on a fixed monthly rate, three-year, exclusive basis. Because it’s a fixed amount, it doesn’t grow from there. Revenue steps up in stages at contract inception due to timing recognition, then flattens out afterward.

And because it’s exclusive, the same facility can’t be sold to another customer. To win a new customer, the company has to build new facilities.

Lining up the numbers makes the nature of this clear. The total value of cloud contracts won this quarter was $14.1 billion. AI-related capital expenditure in the same quarter was $15.8 billion. The capital invested exceeds the total value of the contracts won. And that contract value gets recognized over three years.

Comparison of Capital Expenditure and Contract Value Against annualized AI capex of $63.2 billion, expected annual revenue from contracts is $4.7 billion

AI capital expenditure (annualized)

$63.2 billion Annual revenue expected from contracts

$4.7 billion Approximate figure from recognizing the $14.1 billion contract total over three years

Figure 3: Capital invested versus the annual revenue expected from it

There’s no way to grow revenue other than piling on more equipment. This isn’t software economics. It’s real estate leasing economics.

The multiple the market pays for AI companies presupposes software-like gross margins and economies of scale. Given actual capital expenditure of $18.4 billion in the quarter, I think what the market reacted to wasn’t the size of the number itself, but this ratio.

Whether it’s overpriced or not, honestly no one can say for certain. Since the company is unprofitable, P/E doesn’t apply, and the answer changes by orders of magnitude depending on whether you apply a telecom multiple, an AI infrastructure multiple, or a data center real estate multiple. That’s why 34 analysts’ price targets range all the way from $62 to $800.

Uncertainty itself can be an investment opportunity. If you can buy cheaply something no one can price, that’s an edge. What’s harsh here is that this sits at a point where uncertainty is at its maximum while the multiple is also near the highest tier. The same bet can be taken on a target with a clearer business structure, at a much cheaper price.

That said, the communications business is the real thing. Quarterly operating profit of $1.7 billion, up 79% year-over-year. The barriers to entry are physical too. If this were a standalone company, I think it would be worth considering for a long-term hold. The problem is that the value of the communications business alone can’t explain the current market cap.

At the end of my previous article, I wrote, “If there were a stock where you could just buy Starlink, I would buy it.” Two more months of earnings have come out since then, and that wish has only been reinforced. Communications is strong, and it’s the structure wrapped around it that’s setting the price.

Closing

Starting from “someone is propping up the price,” I ended up landing on “a mechanism with no intent was trading in response to price.”

I found this interesting. It felt like looking for a culprit and finding, instead of a culprit, something more like a law of physics.

Similar mechanisms seem to exist throughout the market. Money tied to index rebalancing, risk parity rebalancing, forced liquidations tied to collateral value. In none of these cases is a person making a judgment, yet trades occur as though by an identity equation tied to price.

Mechanisms are easier to read than intentions. Once you know what something is following, you can calculate its next move. If you attribute it to “someone’s intent,” you stop thinking right there.

When you see unnatural regularity, look for constraints first, not an actor. That was the biggest lesson from this one. For someone who used to work in circuits, this should have been a familiar way of thinking all along.

That said, the mechanism itself has its limits too. Option-driven trading volume has a ceiling, and that pressure vanishes at expiration when the open interest disappears. In a phase where hundreds of billions of dollars in supply is coming, it becomes a rounding error.

As of this writing, that verification hasn’t come in yet.

References

This article is not a recommendation to trade any particular stock. Stock price and order book figures are as of the time of observation and may differ by source.

This piece was conceived and directed by Kuzuryu, with the writing done by AI.

Originally published in Japanese at https://clazytech.com/2026/08/1743/. Translated with LLM assistance and reviewed before publication.