Health Is Wealth When Tariffs Are Denting Profit Forecasts

President Donald Trump’s imposition of tariffs on trading partners have moved analysts to reduce forecasts for U.S. companies. Many stocks look vulnerable to declines, while some seem relatively immune.

Since the start of the year, analysts’ expectations for aggregate first-quarter sales of S&P 500 component companies have dropped about 0.4%, according to FactSet. The hundreds of billions of dollars worth of imports from China, Mexico, and Canada the Trump administration is placing tariffs on, including metals and basic materials for retail and food sellers, will raise costs for U.S. companies. That will force them to lift prices, reducing the number of goods and services they’ll sell to consumers and businesses.

This outlook has pressured first-quarter earnings estimates by 3.8%. Companies will cut back on marketing and perhaps labour, but many have substantial fixed expenses that can’t easily be reduced, such as depreciation and interest to lenders. Profit margins will drop in the face of lower revenue, thus weighing on profit estimates. The estimates dropped mildly in January, and then picked up steam in February, just after the initial tariff announcements.

“We are starting to see the first instances of analysts cutting numbers on tariff impacts,” writes Citi strategist Scott Chronert.

The reductions aren’t concentrated in one sector; they’re widespread, a concrete indication that the downward revisions are partly related to tariffs, which affect many sectors. The percentage of all analyst earnings-estimate revisions in March for S&P 500 companies that have been downward this year has been 60.1%, according to Citi, worse than the historical average of 53.5% for March.

The consumer-discretionary sector has seen just over 62% of March revisions to be lower, almost 10 percentage points worse than the historical average. The aggregate first-quarter earnings expectation for all consumer-discretionary companies in the S&P 500 has dropped 11% since the start of the year.

That could hurt the stocks going forward, even though the Consumer Discretionary Select Sector SPDR exchange-traded fund has already dropped 11% for the year. The declines have been led by Tesla and Amazon.com , which account for trillions of dollars of market value and comprise a large portion of the fund. The average name in the fund is down about 4% this year, so there could easily be more downside.

That’s especially true because another slew of downward earnings revisions look likely. Analysts have barely changed their full-year 2025 sales projections for the consumer-discretionary sector, and have lowered full-year earnings by only 2%, even though they’ve more dramatically reduced first-quarter forecasts. The current expectation calls for a sharp increase in quarterly sales and earnings from the first quarter through the rest of the year, but that’s unrealistic, assuming tariffs remain in place for the rest of the year.

“The relative estimate achievability of the consumer discretionary earnings are below average,” Trivariate Research’s Adam Parker wrote in a report.

That makes these stocks look still too expensive—and vulnerable to declines. The consumer-discretionary ETF trades at 21.2 times expected earnings for this year, but if those expectations tumble as much as they have for the first quarter, then the fund’s current price/earnings multiple looks closer to 25 times. That’s too high, given that it’s where the multiple was before markets began reflecting ongoing risk to earnings from tariffs and any continued economic consequences. So, another drop in earnings estimates would drag these consumer stocks down even further.

Industrials are in a similar position. Many of them make equipment and machines that would become more costly to import. The sector has seen about two thirds of March earnings revisions move downward, about 13 percentage points worse that the historical average. Analysts have lowered first-quarter-earnings estimates by 6%, but only 3% for the full year, suggesting that more tariff-related downward revisions are likely for the rest of the year.

That would weigh on the stocks. The Industrial Select Sector SPDR ETF is about flat for the year but would look more expensive than it is today if earnings estimates drop more. The stocks face a high probability of downside from here.

The stocks to own are the “defensive” ones, those that are unlikely to see much tariff-related earnings impact, namely healthcare. Demand for drugs and insurance is much sturdier versus less essential goods and services when consumers have less money to spend. The Health Care Select Sector SPDR ETF has produced a 6% gain this year.

That’s supported by earnings trends that are just fine. First-quarter earnings estimates have even ticked slightly higher this year. These stocks should remain relatively strong as long as analysts continue to forecast stable, albeit mild, sales and earnings growth for the coming few years.

“This leads us to recommend healthcare and disfavour consumer discretionary,” Parker writes.

A 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