Stocks Are Wobbling. Follow These 3 Rules for Better Returns.
3 min
Suddenly, stocks look shaky. After briefly touching 40,000 earlier this month, the Dow has since shed more than 1,000 points, as worries flare about where interest rates are headed next . The index posted another loss on Wednesday, down 411 points, or 1.06%, to 38,442.
While volatility can be frustrating. It has always been part of the two-steps forward, one-step back nature of the stock market. So keep in mind: Stocks may still have room to run , and they perform their best when investors feel least confident.
Here are three smart rules for interpreting the current market culled from new stock research.
Don’t assume the market is in a bubble
Anytime the market hits a new high, then pulls back sharply, it’s natural to wonder: Could it be all downhill from here? It isn’t an idle concern. Even after the recent dip, stocks are trading at more than 25 times trailing 12-month earnings, their highest level since 2021, according to FactSet.
Still, investors shouldn’t necessarily assume the market has become irrational, suggests a recent note by Leuthold Group, a stock research firm known for compiling dozens of bespoke indicators to measure market sentiment.
Leuthold recently compared large capitalisation stock prices to four separate valuation thresholds it thinks mark out bubble territory.
The results? This year, prices have approached three of these thresholds—one focused on forecast earnings, one based on average earnings and one based on cash flow. But after getting close, stocks didn’t blow through these thresholds as might be expected during a bubble. Instead, they stalled or pulled back. “‘Resistance’ proves formidable,” the firm concluded, citing a term common in technical analysis.
The fourth valuation threshold, which Leuthold calls “P/E on trailing peak GAAP EPS” has yet to be reached. The indicator compares stock prices not to companies’ most recent earnings, but to the market’s record for earnings, in this case set in the first quarter of 2022.
While stocks are trading at 25 times their peak earnings—a very high figure by historical standards—they are still below the 30 times level Leuthold thinks signals bubble territory. The upshot: “We don’t think U.S. large caps quite qualify as a mania,” writes Chief Investment Officer Doug Ramsey.
Don’t sweat the short-term
It’s natural after a short, sharp pullback to worry where the market is headed next. But trying to make short-term market calls is usually a fool’s errand, according to Trivariate Research, another investment firm.
Trivariate recently tested more than two dozen stock market metrics it says are commonly used to predict short-term stock market declines. These indicators included the S&P 500 put-to-call ratio, mutual fund flows, the futures-based VIX fear gauge, the price of oil and more.
The results were “terrible,” according to the firm. “The factors’ large loss predictions were correct at about the same rate as random selection,” Trivariate said in its note.
The firm found that during many months when signals like the VIX and the Conference Board’s Leading Economic Indicators Index predicted a big drop, the market actually showed bigger-than-average gains. The indicators were signalling volatility not declines, the firm noted.
In another test, a model that Trivariate built based on several other indicators also wasn’t much help either. When the model predicted a large stock market loss, defined as a 2.5% monthly drop, the decline failed to materialise 60% of the time.
Do embrace the uncertainty
While uncertainty isn’t always comfortable, it can be to investors’ advantage. If you are willing to run with it.
Retired Wall Street economist Jim Paulsen points to a metric known as the Monetary Policy Uncertainty Index , which tallies newspaper reports and other data to measure uncertainty about what the Fed will do next.
Since 1985 the index has averaged just under 100, but since 2020 it has been elevated most of the time. It’s currently at 144, a higher level than during about 80% of its history.
Still, Paulsen argues this is good news. He compares the Fed’s Jerome Powell era, where the index has averaged 110, to eras of three earlier Fed Chairs: Ben Bernanke, Janet Yellen, and Alan Greenspan, where it averaged about 75.
Investors have been rewarded for enduring the lack of clarity. The S&P 500 has posted average annual returns of more than 12% during Powell’s term, compared with less than 10% under his three predecessors, according to the note.
“All investors long for clarity,” Paulsen writes. “But the stock market never does that well when you and I are comfortable. The great bulk of the returns generated by the stock market typically occur when most are still in their bunkers waiting for conditions to improve.”
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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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