The Cyclical Overearning Trap
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Key Takeaways
Investors have tended to repeatedly mistake cyclical peak earnings for durable earning power, especially in industries like semiconductors, and in doing so have paid for temporary profitability as though it were permanent
The most persuasive signals at the top of a cycle—high margins, seemingly low valuation multiples, and strong share price momentum—have historically been poor indicators of future returns and, in our analysis, have often coincided with elevated risk
Drawing on both historical data and current examples such as memory stocks, we believe over-earning cyclicals have often gone on to underperform and suffer significant drawdowns once margins normalize, making today’s starting odds unattractive, in our view
Observation over many years has taught us that the chief losses to investors come from the purchase of low-quality securities at times of good business conditions. The purchasers view the good current earnings as equivalent to ‘earning power’ and assume that prosperity is equivalent to safety.
— Benjamin Graham
Benjamin Graham wrote that more than 70 years ago, and we think it remains one of the clearest warnings in investing. The mistake he describes is simple and timeless: investors confuse the peak of a cycle with the permanent condition of a business. They see unusually strong results, assume they will last, and pay as if the good times will never end. It happens again and again. One generation forgets what the last one learned. In our view, it is happening now.
Over the past six months, US large-cap semiconductor stocks have dramatically outpaced software as investors have grown increasingly convinced, they know precisely which companies will win from AI. That confidence may prove misplaced, in our view. We see that many of today’s anointed “AI winners” are not steady long-term growers. They are cyclical businesses enjoying unusually strong profits near the top of their cycle, yet their stock prices seem to assume those profits will continue at levels the industry has never sustained before.
Market Believes It May Have the AI Winners and Losers Figured Out
As investors who have lived through cycles, we have seen this story many times before. A cyclical business enjoys a genuine upturn, margins climb to new heights, and the stock is rewarded. Watching this unfold, sell-side analysts often conclude that the strength is not just cyclical, but structural in nature. That’s what can make the top of a cycle so dangerous. Everything lines up to support the bullish case: trailing margins are high, valuation multiples look cheap—but only because earnings are temporarily inflated, and rising share prices seem to validate the narrative. In our view, those signals can be most convincing right before they break.
We make no claim to be able to predict the beginning or end of a cycle. Rather, we aim to provide a dispassionate, data-driven analysis about starting odds. We have studied the historical record of businesses with this same recognizable set of traits, and we believe the historical record carries a clear and uncomfortable message for anyone owning, or considering owning, such businesses today.
The question we would put to any investor is not whether the current upturn is real or not, but rather: how much capital do you want invested in cyclical businesses that are demonstrably over-earning relative to their own history, perhaps fully priced, already rewarded, and operating in sectors where the cycle has historically turned?
CYCLICALITY IS THE AGGRAVATING FACTOR, NOT THE SOURCE
Cyclicality on its own is not a flaw. Many excellent businesses—in semiconductors, energy, industrials, and materials—exhibit cyclical earnings, and we have owned many of them in the past. The hazard, in our view, arises when the market begins to pay for the temporary, mean-reverting portion of a company’s earnings as though it were permanent.
A useful way to think about any cyclical business is to separate its growth into two parts: a normalized stream it can reasonably sustain across a full cycle, and a cyclical residual that exists only because conditions currently favor it. In ordinary times, the distinction feels a bit like splitting hairs. However, in the late stages of a cycle, the residual can become the larger of the two, and the headline valuation, measured against total projected earnings, can become quite misleading.
This is why a low valuation multiple on a cyclical business is often a warning sign rather than an invitation. As Peter Lynch observed decades ago: Low earnings multiples on cyclical companies tend to appear at the moment of maximum danger, not maximum opportunity!
POOR STARTING ODDS
Before turning to any individual company, it is worth asking a simple question: what have the historical odds looked like for cyclical businesses at or near the peak of the cycle?
For purposes of this analysis, we define an “over-earning cyclical” as a company meeting three independently observable conditions, none of which requires a forecast. First, current operating margins sit at least one standard deviation above the company’s own trailing seven-year average—that is, the business is earning well by its own historical standard. Second, the market is already paying a hefty valuation, as evidenced by a trailing 12-month free-cash-flow-to-enterprise-value yield in the bottom quartile of the large-cap universe. Third, the stock has already been rewarded, reflected in price momentum in the top quartile over the trailing six months. To ensure we are capturing genuine cyclicality, our equity universe is restricted to businesses in the top quartile of trailing margin volatility, or to industries long recognized as cyclical—oil exploration and production, semiconductors, and the like.
According to our research, over the past 30 years, roughly 100 companies have met this over-earning cyclical screen at some point. Their forward three-year relative returns reveal a strikingly unfavorable payoff profile. The headline statistic alone is notable: the median company underperforms the market by more than 20% over the subsequent three years.
The Odds Skew Against the Owner
Deep Drawdowns are the Median Experience
Source: GQG Partners LLC (chart). Bloomberg (data). Data universe made up of S&P 500 constituents spanning thirty years ending December 2025. For illustrative purposes only. PAST PERFORMANCE MAY NOT BE INDICATIVE OF FUTURE RESULTS.
BATTLING OVER WATER
But it is the shape of the distribution of outcomes, rather than its midpoint, that should give any investor in such businesses pause. At the 75th percentile, the outcome is only a modest 14% cumulative relative gain over three years, while at the 25th percentile, the outcome is worse than -60%. In other words, when the bet works, it works modestly. When it fails, it fails meaningfully. Nor is this simply an end-point phenomenon: the return path itself reflects poor starting odds from the day the company first appears on the screen. Even at the one-year mark, the median name underperforms by roughly 10%, with the interquartile range still skewed negative with outcomes ranging from about +15% at the high end to -30% at the low end.
While relative underperformance is one real form of opportunity cost, arguably the more insidious characteristic of these businesses is their propensity to experience meaningful drawdowns. Within the first year of meeting our screen, the median name exhibits a peak-to-trough drawdown of at least one-third. Extend the window to three years, and the median peak-to-trough decline exceeds 50%. These are not tail events confined to a handful of casualties; they are the average experience of owning a cyclical business at or near the peak of its cycle.
Consensus Extrapolates the Peak
The defining characteristic of an over-earning cyclical—the mechanism that produces those poor starting odds—is arguably the textbook expression of those four deadly words in investing: this time is different. Said another way, consensus has consistently extrapolated a cyclical peak as if it were a structural plateau.
To demonstrate this point empirically, we examined every business that met our over-earning cyclical screen over the past 30 years, normalizing each company’s realized operating margin so that, as of the moment it qualified for our screen, that margin stood at a standardized 30%. We then looked at what consensus was projecting for those same businesses over the following three fiscal years. Tellingly, margins were expected to expand modestly through the next fiscal year, even after already registering a multi-year peak, and to hold near those elevated levels for the second and third years that followed. The market and the analyst community had become convinced that either the cycle itself had been abolished or that the current one would outlast all previous ones. The first year typically delivered on those projections, while the second and third years did not. By fiscal year three, the median realized operating margin had fallen to roughly 10%, a third of the peak and close to where through-cycle margins tend to settle.
Overearning Cyclicals
This is the heart of the trap. The valuation looks reasonable—even cheap—on a forward-looking basis because it is measured against an earnings stream the business can seldom sustain. The stock’s momentum looks like confirmation. And the consensus view on the stock—the very thing an investor might rely on for comfort—is, in our view, the single least reliable input at precisely the moment it is most trusted.
Fertilizers in 2008 are a textbook illustration of this phenomenon. Potash and phosphate prices rose sharply from 2006 through early 2009, taking the operating margins of producers like Mosaic to levels never seen before. Consensus, observing this, did not treat it as cyclical. Sell-side analysts, economists, and even government bodies argued the price level was structural—driven by emerging-market food demand, the 2007 US biofuels mandate, persistent supply constraints across a consolidated producer base, and rising energy costs that would not retrace.1 Each of these observations was directionally correct, but the conclusion drawn from them—that margins would remain elevated—was not. Within roughly a year of the peak, potash prices had collapsed, margins had normalized, and Mosaic shares were down more than 80% from their high.
Steel suffered a similar fate that same year. In the run-up, producers saw profits rise on the back of a surge in Chinese demand, a synchronized worldwide infrastructure build-out, and consolidation that finally gave the industry some pricing power. Margins and profits reached new heights, and the consensus view, in its usual way, interpreted that strength as a new normal rather than a likely peak.
What came next was a second trap because profits were so uncharacteristically strong, earnings multiples looked low, and the stocks looked cheap right when they were arguably most dangerous. The Steel Producers Index2 began to turn down well before earnings did—but only modestly at first, falling roughly 20% from its high—and through that early decline, consensus was still revising earnings estimates upward.
To an investor at that moment, the setup looked benign. While the market had pulled back somewhat, estimates were still climbing, and the multiple was starting to look genuinely cheap after being down roughly 50% from levels only a couple of months earlier. That, in fact, was the point of maximum danger. The multiple only screened as truly cheap during the initial descent, when falling prices outran still elevated and rising earnings, before reversing entirely once profits normalized.
When Cheap Looks Dangerous
Source: GQG Partners LLC (chart). Bloomberg (data). Data corresponds to the Bloomberg US Steel Producers Index for the time period 3 January 2005 through 31 December 2010. For illustrative purposes only. PAST PERFORMANCE MAY NOT BE INDICATIVE OF FUTURE RESULTS.
This is Peter Lynch’s warning in practice. When the financial crisis finally hit and demand fell away, the steel names went on to fall nearly 90% from their highs—much like fertilizers, in a completely different industry, for much the same reason.
Which brings us to the present. Memory stocks today offer a near-perfect illustration of exactly this dynamic playing out in real time, in our view. Since 2017, Micron, Samsung Electronics, and SK Hynix have exhibited the familiar pattern of a deeply volatile, cyclical margin profile over the long term, followed by current margins that sit well above their long-run average. Yet consensus forecasts assume not only that these elevated margins will persist, but that they will remain elevated over the next several years.
AI-driven demand for high-bandwidth memory (HBM) is genuine and has created a real cyclical tailwind. But the memory industry has historically met strong demand with abundant supply, and we have watched consensus extrapolate a memory peak with too much zeal before. The 2018 cycle is a useful recent example of how quickly such expectations can unravel. The question for an investor today is therefore not whether the demand is real, but whether the margin trajectory reflected in current prices is a credible one. We are skeptical it is.
Operating Margins to Memory through 2029
Chip Margins Appear Unsustainably High
Source: GQG Partners LLC (chart). Bloomberg (data). Data for the fiscal years from 2013 through 2021. Data as of 31 May 2026. Content does not constitute investment advice and no investment decision should be made based on it. For illustrative purposes only. PAST PERFORMANCE MAY NOT BE INDICATIVE OF FUTURE RESULTS.
SITTING THIS ONE OUT
We have seen this film before: fertilizers and steel in 2008, the shale boom in 2014, and memory itself on several occasions in recent history. Each time, the late-cycle narrative was substantive enough to be believed by serious investors, as is the case today with memory stocks. Yet the combination of peak margins, full trailing valuations, and strong recent returns eventually gave way to a margin reset that painfully compressed both the numerator and denominator of the earnings multiple.
To be fair, the bull case for memory in 2026 is arguably somewhat different than in these prior cycles. Bulls argue that AI has turned memory from a secondary component in the computing stack into a critical bottleneck, with HBM becoming essential to AI server performance. Unlike traditional DRAM, leading-edge AI memory must be qualified by cloud service providers and AI customers, which creates higher barriers to entry and stickier customer relationships. The end-market growth profile is also stronger, with memory and storage making up, based on our analysis, 15-20% of the data center bill of materials; and volumes growing at high-teens before pricing.3 Supply may be harder to add, the industry is more consolidated and rational, and capacity has shifted toward HBM and away from DRAM, tightening the entire complex.
That is the best version of the bull argument. In our view, the problem is that it still depends on AI spending continuing to grow from an already extraordinarily high absolute base. If memory demand is increasingly tied to data center CapEx, then memory earnings are ultimately tied to hyperscalers’ willingness and ability to keep funding that CapEx. Recent reports suggest that burden is meaningful. If even Google is reportedly willing to sell shares to Berkshire Hathaway at a 6.5% discount to the recent market price, we struggle to view capital availability as a non-issue.4 At tens of billions of dollars per gigawatt of data center capacity, the bottleneck may shift quickly from chips and power to capital markets.5
We have engaged internally with these arguments at length, but with history as our guide, we are skeptical. These “long-term” supply contracts are among the biggest warning signs. In our view, this kind of scramble to hoard and lock up supply can occur near a cycle’s peak, when conditions are at their strongest. History would suggest that once the cycle turns, such contracts may prove less binding than expected. At current valuations, the market seems to assume that today’s unusually high profit margins will last for the rest of the decade. As we have shown, being late to exit such over-earning episodes has historically resulted in swift and major capital destruction. With our philosophy of compounding and a focus on downside risk management, that is not a bet we are willing to make.
The speculative activity in markets is also hard to ignore. Assets in 2x single-stock leveraged ETFs tied to memory have exploded higher—an example of egregious risk-taking behavior that, in our experience, is less common at the beginning of a cycle. And yet the larger issue, we think, is not just the capital chasing the AI theme explicitly, but the amount of capital exposed to it implicitly. In our view, investors’ exposure to over-earning cyclicals is larger today than at almost any point in recent history. Semiconductors now represent nearly 20% of the S&P 500 and over 25% of the MSCI Emerging Markets Index, versus long-term averages of 3% and 5%, respectively. More strikingly, over two-thirds of the MSCI Korea Index is tied to the fortunes of just two memory businesses: Samsung Electronics and SK Hynix. In other words, these dynamics are not confined to a handful of names investors can simply choose to avoid; they sit at the heart of major benchmarks that most passive allocations are being made against today.
Total Assets in Leveraged Memory ETFs
That brings us back to the key question: how much of your capital—including the parts you may not realize are exposed to the same theme—are you willing to bet on the idea that this time is different?
END NOTES
1Huang, Wen-yuan. “Factors Contributing to the Recent Increase in U.S. Fertilizer Prices, 2002-08.” The US Department of Agriculture February 2009.
2The Bloomberg US 3000 Steel Producers Index.
3Worldwide Semiconductor Trade Statistics (WSTS) and Bernstein Research. Data as of March 2026.
3Royal, James. “Why Alphabet Pivoted to Massive $80 Billion Stock Sale to Fund AI Build-Out – With Berkshire’s Help.” Market Wise. 4 June 2026.
4Michael, Amelia and Cottier, Ben. “Servers account for 60% of the total cost of ownership of a one-gigawatt AI data center.” Epoch AI. 14 May 2026.
DEFINITIONS
Trailing margins
Valuation multiples
Operating margin
Standard deviation
Trailing 12-month free-cash-flow-to-enterprise-value yield
Trailing margin volatility
Opportunity cost
Drawdown
Tail events
PE Ratio
Bloomberg US Steel Producers Index
DRAM
CapEx
Hyperscalers
Leveraged ETF
S&P 500 Index
MSCI Emerging Markets Index
MSCI Korea Index
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