The Sudden Unraveling of Wall Street’s Momentum Trade
Wall Street’s hottest momentum trade has reversed sharply, as former winners tumble and heavily shorted stocks surge.
3 min
Wall Street’s hottest trade has gone ice cold.
For years, it paid off to buy stocks that were rising in price—and bet against struggling shares. The momentum trade was especially profitable this year, as investors piled into hot stocks including Micron Technology, Nvidia, Advanced Micro Devices and other artificial-intelligence darlings while wagering against those likely to be hurt by the embrace of AI.
The S&P 500 Momentum Index soared 44% in the second quarter, its best quarterly performance on record, and it surged 133% over the past five years, nearly double the broad market’s performance.
Mega funds and rookie investors alike piled into the trade, some using leverage and options contracts in an effort to amplify their returns, propelling the underlying shares higher.
“It is a self-fulfilling prophecy,” said Matthew Tym, managing director at Cantor Fitzgerald, of the trade.
Suddenly, the trade is a loser. The momentum index has tumbled more than 9% since July 1, lagging behind the S&P 500’s 2.8% gain. The index—which tracks stocks in the S&P 500 based on a “momentum score”—is on track for the biggest quarterly underperformance in 25 years. July was the second-worst month for the momentum trade in around 40 years, according to Bank of America estimates; the only month worse was April 2009, in the teeth of the global financial crisis.
Hedge funds that bought momentum shares while shorting low-momentum stocks suffered even more. At the same time, a basket of the most popular stocks held by hedge funds tracked by Goldman Sachs recorded its biggest one-month underperformance in July relative to the S&P 500 in more than 20 years, according to the bank’s analysts.
Momentum trading is based on a rather simple observation: Investments that go up tend to keep outperforming; those that underperform often remain laggards. This kind of trading might seem too simple a stock-picking strategy to work. Yet it often has.
“For decades, it didn’t take a lot of sophistication to run a momentum strategy and make a decent living at it,” says Agustin Lebron, senior researcher at EquiLibre, a trading firm.
Part of the reason: It takes a while for corporate and other information to spread to various investors, so they slowly build positions, producing buying momentum.
“A huge pension fund can’t flip around its positions in a day,” says Lebron. “Behavioral biases also account for some of the effect, as well—people tend to sell their winners too early and hold losers too long.”
Fans of the strategy point to the human tendency to extrapolate from past results—and chase investment returns—noting that momentum patterns have been evident in markets for decades, even centuries. They also say that some of the worst months for momentum strategies are during longer periods of outperformance.
Some have been doing the trade by buying the strongest investments in a sector while shorting the weakest; others lean in to rising markets or asset classes. Still others use a quantitative approach or turn to banks or others who sell ways to make distinct wagers on momentum as a “tradable factor” or a “thematic basket.”
The fans remain believers. “Any strategy has disappointing periods,” says Antti Ilmanen, global co-head of the portfolio solutions group at AQR Capital Management.
The surge in Moderna and other biotech stocks helped crush the momentum trade. These shares were among the most heavily shorted in recent years, but positive news on a cancer vaccine from Moderna and Merck sent those stocks flying, crushing some quant and other hedge funds. Moderna is up around 150% so far this month.
These traders had an especially rough day on Aug. 19, which Goldman Sachs told its clients was the worst day for “systematic long-short managers” in more than two years. About half of the losses were because of momentum trades, the bank said.
Some traders have begun to short, or bet against, the very stocks that propelled the momentum trade earlier this year. Net short positions in futures tied to the Nasdaq-100 index among speculators recently climbed to some of the highest levels of the past two decades, according to data from the Commodity Futures Trading Commission.
The about-face is a sign of how markets have become more treacherous for investors, even as indexes keep climbing. Part of the issue: the recent meltdown of Situational Awareness, a hedge fund that had piled into some of the most popular momentum shares, including chip stocks. After a period of market tumult, Nvidia shares rocketed almost 9% after its earnings, showing how quickly sentiment can shift.
Some investors say the run-up in share prices driving tech stocks higher reminds them at times of the dot-com frenzy decades ago.
Mike Ogborne, the founder of San Francisco-based Ogborne Capital Management, said he has grown more cautious on technology stocks and is keeping more of his portfolio in cash than he typically does.
And he is nervous about the surge in spending by technology giants and quarterly capital expenditures that keep rising.
“It is a little bit like Cinderella and the clock striking midnight. You don’t know when midnight is going to come around,” Ogborne said. “They don’t send a memo around telling you when the capex cycle is over.”
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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