1 billion in funds, leveraging a trillion-dollar market cap: Crypto trading strategies are sweeping the stock market

Original Author: Jia Liu

Low float, big narrative, high market cap are becoming the common characteristics of speculation in this financial cycle.

Zhipu’s stock price once surged 25 times less than six months after its Hong Kong Stock Exchange listing. But if you look at its share structure, you'll find a more critical, yet easily overlooked number: in the early stages of listing, the shares truly available for free trading on the market were only about 17.35 million, representing less than 4% of the total share capital. A company with a market cap of trillions of Hong Kong dollars, its daily trading chips pool is actually only on the scale of hundreds of billions of Hong Kong dollars.

This is a typical, but not unique example, and can even be seen as a microcosm of the gameplay in this cycle.

A dozen days ago, SpaceX was listed with a valuation of $1.77 trillion, with only 4.3% of its shares publicly circulating. To accommodate its listing, Nasdaq directly abolished the 10% minimum public shareholding threshold that had been in place for decades. SPCX's market cap peaked at over $2 trillion, but its daily trading volume was only about $100 million.

Cerebras, a US AI chip company, sold only about 15% of its issued shares during its IPO in May, and on its first day, it rose to more than double its issue price. Figma sold new and existing shares totaling less than 10% of its total share capital, surging 250% on its first day.

Low float, big narrative, high market cap. The structure that the crypto market has been playing with for years is now being fully replicated by the traditional stock market. Similar structures are appearing simultaneously in the US, Hong Kong, and A-share markets, with the narrative extending from AI, chips, and large models to stablecoins.

The era of pricing based on financial reports has come to an end again

In February 2000, a sock puppet dog appeared in a Super Bowl ad. It was a 30-second spot that Pets.com spent $1.2 million on. At the time, its annual revenue was less than $6 million, and its losses exceeded $60 million. Nine months later, the company liquidated, and the sock puppet became the most classic tombstone of the internet bubble.

That era's market lesson was written into almost every investment textbook: valuations not backed by revenue are bubbles; narratives cannot replace financial reports.

For the next twenty-plus years, this lesson dominated the market. DCF, PE, PEG, discounted free cash flow – pricing methods based on financial data became orthodox. Buffett was once again hailed as a god after the 2008 financial crisis. "Buying without looking at financial reports" became synonymous with speculation.

But looking at the new tech tracks from 2025 to 2026 today, we discover a fact: the most sought-after companies in these industries are actually losing money.

For example, CoreWeave, an AI computing infrastructure company backed by Nvidia, had revenue of $16 million in 2022 and $5.1 billion in 2025, a more than 300-fold increase in three years. Revenue growth was staggering, but net losses also expanded from $31 million to $1.2 billion. In the first quarter of 2026, the company had revenue of $2.1 billion and a net loss of $740 million, with a debt-to-equity ratio of 10.7. By traditional bank credit standards, such a balance sheet is not healthy. Yet, after its listing, its stock price once surged 190%.

The situation for Nebius is similar. The company, formerly Russia's Yandex, pivoted to AI cloud services after a split. In the first quarter of 2026, revenue was $399 million, up 684% year-over-year, but adjusted net loss was still $100 million. Over the past 12 months, its stock price has surged over 510%.

Let’s turn our attention back to the Chinese market.

Zhipu's full-year revenue in 2025 was 724 million RMB (approximately $100 million), but its net loss was 3.182 billion RMB, 4.4 times its revenue. In other words, for every 1 RMB it earns, it spends far more than 1 RMB on computing power and R&D. MiniMax, another AI company listed in Hong Kong in the same batch, rose 109% on its first day, with subsequent gains exceeding 700%. Its full-year revenue was $79.038 million (approximately 560 million RMB), even less than Zhipu.

Similarly, the first-day gains for Hong Kong-listed GPU company Biren Technology, A-share domestic GPU company Muxi, and STAR Market-listed Moore Threads were 120%, 693%, and 425% respectively. These new stocks with astonishing gains are all in a state of severe losses or no profitability.

If you use PE to look at these companies, many don't even have a premise for calculation because profits are negative. Using PS, Zhipu has a price-to-sales ratio of over 1200x, SpaceX about 95x. Using DCF, a slight change in the discount rate or terminal growth rate can shift the conclusion from 100 billion to 10 billion, making the model's sensitivity so high it loses its guiding significance. Damodaran, the author of the DCF textbook, gave an intrinsic value of $1.2 trillion for SpaceX, 30% lower than its IPO pricing. He himself admits that when dealing with this generation of IPOs, minor parameter adjustments lead to wildly fluctuating results.

Some might say that the early internet era also didn't look at PE; Amazon was unprofitable for twenty years before turning a profit, so this isn't new. True, but there's a key difference between this cycle and the internet era: the market now isn't even pricing using alternative metrics to PE; it's trading pure narrative.

Although internet-era investors didn't look at PE, they looked at user growth, GMV, and page views. Essentially, they were still using a set of quantifiable intermediate metrics to anchor valuations. Today's AI companies also have metrics like ARR, but ARR doesn't explain Zhipu's 1200x price-to-sales ratio. The frenzied speculation in the supply chain has long escaped the gravity of financial fundamentals, pricing all expectations for the next three to five years into the present.

The old pricing framework is beginning to fail with this new class of assets. The investment logic of financial markets and investors worldwide has also undergone tremendous changes.

Model weights, algorithmic capabilities, developer ecosystems, and computing power scheduling capabilities – these are the true core assets of AI companies, but none can be written on a balance sheet. GLM-5.2's programming capability made Vercel's CEO say he was "almost shocked," a statement that won't appear on Zhipu's income statement. CoreWeave sits on over $100 billion in backlog orders, but that doesn't change the fact of its quarterly net loss. Nvidia's GPUs are called the oil of the AI era, and oil pricing never just looks at current quarter production; it also considers reserves, demand curves, and geopolitics.

The core assumption of traditional pricing methods is that future cash flows can be extrapolated from historical financial data. This assumption works very well in industries like consumer goods, finance, and real estate.

But the revenue curve for AI companies is not linear. It depends on leaps in model capabilities, network effects of open-source ecosystems, and sudden shifts in policy and industry cycles. After the release of GLM-5.2, Zhipu's narrative position can change overnight; Llama going open source rapidly amplified Meta's AI influence; US chip restrictions on China turned Biren and Muxi from marginal companies into "domestic substitute leaders." These variables are difficult for any financial model to predict in advance.

At the same time, the market's tolerance for narrative-driven investing is rising because, over the past few years, those who believed the narrative have indeed made money.

Those who bought Nvidia in early 2023 without looking at financial reports made ten times their money. Those who bought Zhipu in early 2026 without looking at financial reports made 24 times their money. When a "wrong" method consistently produces "correct" results, the market will revise its methodology, not the results.

It doesn’t actually take much money to prop up a high market cap

A Nasdaq study looked back at data from 1980 to 2020: in the 1980s, the average float for US IPOs was about 30% of total shares. By 2020, this number had dropped to about 20%.

J.P. Morgan's report from June 2026 gave an even more macro figure: new shares issued in IPOs, plus early investor shares allowed to be sold after the lock-up period, together accounted for only about 1% of the total market capitalization.

The float of IPOs is getting smaller. This is a trend that has persisted for almost thirty years.

Nasdaq also found a clear inverse relationship between float and first-day returns. In years with smaller floats, first-day returns were larger.

Our own compilation of US IPO samples from 2024-2026 shows the same characteristic. Defining low float as "current float / total shares below 30%," within the sample where first-week performance is calculable, 67.4% of low-float IPOs rose on the first day, 65.2% were still up on the third trading day, and 63.6% were still up on the fifth trading day.

For non-low-float IPOs, the corresponding percentages were only 47.9%, 48.9%, and 49.6%.

With fewer shares available to buy, the same buying power has a greater impact, and price elasticity is stronger.

The logic is simple. The same $1 billion in buy orders is a ripple in a $20 billion float but a tsunami in a $3 billion float. Reducing the float from 20% to 3% isn't a linear change; it's a qualitative shift in price elasticity.

Newly listed companies are increasingly inclined towards low float because it results from maximizing the interests of all parties.

Consider the founder first. The smaller the float, the more stable the control. SpaceX's Musk controls about 85% of the voting rights through Class B shares, and the 4.3% public float means external investors have almost no governance influence. He can simultaneously serve as CEO, CTO, and Chairman, can merge xAI into SpaceX without shareholder approval, and can keep the company's strategic direction entirely in his own hands. The smaller the float, the weaker the voice of external shareholders, and the greater the founder's freedom.

Scarcity directly pushes up the market cap number. A company's market cap isn't determined by all its shares, but by the price of the last trade multiplied by the total shares. If only 3% of the chips are trading, and that 3% is driven up to an outrageous price, the entire company's market cap is calculated based on that price.

The book value of the 97% of untraded shares held by founders and early shareholders all inflates accordingly. This inflated market cap can be used for fundraising, as acquisition currency, and to attract talent. SpaceX listed with a $1.77 trillion valuation, a number that appears on all recruitment materials and on the table for all partnership negotiations.

This phenomenon isn't limited to small-cap stocks.

Figma (FIG), a collaborative design software platform, had a float of only 2.36%, rising 250% on the first day, 168.48% on the third day, and 173.7% in a week.

Circle (CRCL), the stablecoin and blockchain financial infrastructure company behind USDC, had a float of 13.68%, rising 168.48% on the first day, 271.77% on the third day, and 278.06% in a week.

Bullish (BLSH), a digital asset trading platform and market infrastructure company, had a float of 19.78%, rising 83.78% on the first day, 87.95% on the third day, and 60.84% in a week.

Cerebras (CBRS), an AI computing infrastructure company, had a float of 13.66%, rising 68.15% on the first day, 60.35% on the third day, and 57.13% in a week.

Now look at the investment banks. The "first-day return" of an IPO is the core metric for underwriting success. Media headlines, client evaluations, and the reputation of the investment bank are all linked to this number. The smaller the float, the easier it is to generate a good first-day return. Goldman Sachs designed a 4.3% float for SpaceX; it rose 19% on the first day, and everyone called it a great IPO. If the float had been 20%, the same buying power spread across five times the shares might have resulted in only a 4% rise, and the media headlines would have been completely different.

The incentive structure of investment banks naturally leans towards low float – the smaller the float, the better the first-day return looks, and the stronger the bank's reputation.

Then there are the cornerstone investors. The cornerstone system in Hong Kong is essentially a trade: "I help you lock up chips; you guarantee me an allocation." The cornerstone investor's benefit is securing a definite IPO allocation (without worrying about reductions or ballot results), with the cost being a 6-month lock-up period. But this cost often becomes a reward – because the cornerstone locks up most of the float, leaving very few shares to trade, making it easy for the stock price to be pushed up.

When the lock-up expires after 6 months, if the stock price has already multiplied due to the low float, the cornerstone's return far exceeds a normal IPO. The cornerstone system aligns the act of "helping the company lock up chips" with "making more money for oneself," making the interests of both parties perfectly consistent.

Zhipu's 11 cornerstone investors (including Gaoyi Asset Management, Taikang Life Insurance, and GF Fund) took 70% of the already limited float, resulting in less than 4% of shares ultimately circulating. All locked up for 6 months. While helping Zhipu lock up its float, they were also manufacturing scarcity premium for themselves.

We can even see a systemic turning point from the Nasdaq exchange itself: the abolition of the 10% minimum public shareholding threshold.

This rule had existed for decades. A listed company needed to have at least 10% of its shares in public hands to ensure sufficient market liquidity and protect the interests of public investors. The S&P 500 is stricter, requiring its constituents to have a minimum level of public float. MSCI requires 15%. The Russell series requires 5%.

The precedent is far-reaching. If Nasdaq can abolish the 10% threshold for SpaceX, what obstacle is there for the next company wanting to list with a 3% float? If the largest US exchange considers low float acceptable, will other exchanges follow suit? Hong Kong's cornerstone system already allows for locking up most IPO chips; if Nasdaq also loosens up, will we see global competition where exchanges compete to be the most friendly to low float to attract the best IPO candidates?

Primary Investment, Secondary Hedging: The Stock Market is Replicating Crypto's Old Playbook

In the 1990s, as the options market matured, the zero-cost collar became a staple for the wealthy. You hold a stock, buy a put option to protect against downside (costs money), and sell a call option to recover the cost (receives money). The two offset each other, locking in a price range for free. Michael Dell used variable prepaid forwards in the late 1990s to monetize a portion of his Dell shares without triggering taxes or reducing his holding count, but he got the cash upfront.

But previously, this was used by a few ultra-wealthy individuals and founders. Now, after SpaceX's listing, wealth management firms are publicly offering these solutions to thousands of employees, on a completely different scale. Wealth managers like Bernstein and Mercer are now directly producing guides teaching SpaceX employees how to execute collars – a level of普及 unheard of before.

A Bernstein report includes a sobering set of data. They looked back at all US IPOs raising over $50 million in the past decade and found that the median return 6 months after the lock-up period ended was a decline of about 10%. One in ten IPOs fell by at least 62% within six months of the lock-up expiry. Their conclusion is direct: if you're a SpaceX employee holding locked-up shares, statistically, the price when you're able to sell will most likely be lower than it is now. So you should use derivatives to lock in gains before the lock-up expires.

Michael Burry, who made billions shorting the US subprime mortgage market in 2008, publicly stated after SpaceX's listing that he had studied put options to short but found the price prohibitively expensive, ultimately doing neither long nor short. Even "The Big Short" finds the cost of shorting too high, proving that too