To Find Winning Stocks, Investors Often Focus on the Laggards. They Shouldn’t.

A lot of investors get stock-picking wrong before they even get started: Instead of targeting the top-performing stocks in the market, they focus on the laggards—widely known companies that look as if they are on sale after a period of stock-price weakness.

But these weak performers usually are going down for good reasons, such as for deteriorating sales and earnings, market-share losses or mutual-fund managers who are unwinding positions.

Decades of Investor’s Business Daily research shows these aren’t the stocks that tend to become stock-market leaders. The stocks that reward investors with handsome gains for months or years are more often already the strongest price performers, usually because of outstanding earnings and sales growth and increasing fund ownership.

Of course, many investors already chase performance and pour money into winning stocks. So how can a discerning investor find the winning stocks that have more room to run?

Enter “relative strength”—the notion that strength begets more strength. Relative strength measures stocks’ recent performance relative to the overall market. Investing in stocks with high relative strength means going with the winners, rather than picking stocks in hopes of a rebound. Why bet on a last-place team when you can wager on the leader?

One of the easiest ways to identify the strongest price performers is with IBD’s Relative Strength Rating. Ranked on a scale of 1-99, a stock with an RS rating of 99 has outperformed 99% of all stocks based on 12-month price performance.

How to use the metric

To capitalize on relative strength, an investor’s search should be focused on stocks with RS ratings of at least 80.

But beware: While the goal is to buy stocks that are performing better than the overall market, stocks with the highest RS ratings aren’t always the best to buy. No doubt, some stocks extend rallies for years. But others will be too far into their price run-up and ready to start a longer-term price decline.

Thus, there is a limit to chasing performance. To avoid this pitfall, investors should focus on stocks that have strong relative strength but have seen a moderate price decline and are just coming out of weeks or months of trading within a limited range. This range will vary by stock, but IBD research shows that most good trading patterns can show declines of up to one-third.

Here, a relative strength line on a chart may be helpful for confirming an RS rating’s buy signal. Offered on some stock-charting tools, including IBD’s, the line is a way to visualize relative strength by comparing a stock’s price performance relative to the movement of the S&P 500 or other benchmark.

When the line is sloping upward, it means the stock is outperforming the benchmark. When it is sloping downward, the stock is lagging behind the benchmark. One reason the RS line is helpful is that the line can rise even when a stock price is falling, meaning its value is falling at a slower pace than the benchmark.

A case study

The value of relative strength could be seen in Google parent Alphabet in January 2020, when its RS rating was 89 before it started a 10-month run when the stock rose 64%. Meta Platforms ’ RS rating was 96 before the Facebook parent hit new highs in March 2023 and ran up 65% in four months. Abercrombie & Fitch , one of 2023’s best-performing stocks, had a 94 rating before it soared 342% in nine months starting in June 2023.

Those stocks weren’t flukes. In a study of the biggest stock-market winners from the early 1950s through 2008, the average RS rating of the best performers before they began their major price runs was 87.

To see relative strength in action, consider Nvidia . The chip stock was an established leader, having shot up 365% from its October 2022 low to its high of $504.48 in late August 2023.

But then it spent the next four months rangebound—giving up some ground, then gaining some back. Through this period, shares held between $392.30 and the August peak, declining no more than 22% from top to bottom.

On Jan. 8, Nvidia broke out of its trading range to new highs. The previous session, Nvidia’s RS rating was 97. And that week, the stock’s relative strength line hit new highs. The catalyst: Investors cheered the company’s update on its latest advancements in artificial intelligence.

Nvidia then rose 16% on Feb. 22 after the company said earnings for the January-ended quarter soared 486% year over year to $5.16 a share. Revenue more than tripled to $22.1 billion. It also significantly raised its earnings and revenue guidance for the quarter that was to end in April. In all, Nvidia climbed 89% from Jan. 5 to its March 7 close.

And the stock has continued to run up, surging past $1,000 a share in late May after the company exceeded that guidance for the April-ended quarter and delivered record revenue of $26 billion and record net profit of $14.88 billion.

Ken Shreve is a senior markets writer at Investor’s Business Daily. Follow him on X @IBD_KShreve for more stock-market analysis and insights, or contact him at ken.shreve@investors.com .

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