
IBM suffered its worst stock crash in 115 years on July 14 after missing revenue guidance by 3.7%, erasing $40 billion(約6.4兆円) in market value.
Economist Steve Hanke warns this exposes a hidden "earnings bubble" in AI markets—where reported profits may be inflated by easy credit from private banks rather than sustainable business growth—a more dangerous mispricing than the valuation bubble most investors have been watching.
Unlike traditional valuation bubbles, earnings bubbles are detected only after stocks crash, because analysts revise profit forecasts reactively, leaving little early warning.
What happened
IBM's stock plummeted 25% on July 14 after reporting second-quarter revenue of $17.2 billion(約2.8兆円)—a 3.7% miss versus consensus of $17.9 billion(約2.9兆円)—and adjusted EPS of $2.93 versus expected $3.02, with guidance signaling only 1% growth instead of the 5% market expected. The crash erased roughly $40 billion(約6.4兆円) in market value, marking the worst single-day decline in IBM's 115-year history.
Why it matters
Economist Steve Hanke argues the real danger to markets is not a valuation bubble (where prices are too high relative to earnings) but an earnings bubble—where the reported profits themselves may be inflated or unsustainable by easy credit from private banks. IBM's relatively modest miss triggering a historic crash suggests the market may have abruptly stopped believing the profit-growth narrative underlying AI stocks, even though S&P 500 valuations sit at 22x forward earnings, below the 25x-plus threshold typically flagged as bubble territory.
What to watch
Unlike valuation bubbles, earnings bubbles are hard to detect early because analysts typically cut profit estimates only after stocks have already fallen. If IBM signals the start of broader earnings disappointment across the sector, the rest of earnings season will reveal whether this is a single-stock anomaly or evidence that the market's tolerance for earnings misses has permanently shifted.
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The IBM crash on July 14 arrives at a moment of sharp contradiction in financial markets. While JPMorgan and Goldman Sachs reported record profits—JPMorgan's $21.2 billion(約3.4兆円) net income the highest in U.S. banking history, Goldman's net earnings up 84% to $6.4 billion(約1兆円)—IBM's relatively modest 3.7% revenue miss triggered a historic sell-off. This juxtaposition has led economist Steve Hanke to articulate a thesis gaining traction among analysts: markets are mispricing not stocks' valuations but the earnings that justify those valuations.
The traditional AI bull case rests on two pillars: first, that leading AI companies like Nvidia and Alphabet generate real cash flow unlike profitless dot-com names, and second, that S&P 500 valuations sit at 22x forward earnings, below the 25x-plus threshold typically associated with bubble territory. BCA Research's Peter Berezin has argued for months that today's AI trade is "primarily an earnings bubble rather than a valuation bubble." The danger of such bubbles is their invisibility. Valuation bubbles announce themselves through stretched P/E ratios; earnings bubbles hide within reported profit figures that may be inflated by cyclical capex surges or easy credit. Analysts historically cut profit estimates only after stock declines have already begun, leaving investors without early warning. When earnings bubbles burst, they leave behind real excess capacity—data centers, chip fabs—rather than merely erasing paper gains.
IBM's own aftermath illustrates this detection lag. BofA and UBS trimmed earnings estimates only after the stock had already crashed 25%, with BofA cutting its price target from $330 to $280 and UBS lowering 2026 EPS forecasts while holding its $236 target. The Street's split response—BofA maintaining a Buy rating while HSBC downgraded to Reduce—suggests genuine uncertainty about whether IBM represents a sector-wide crack or an isolated execution miss. The answer will emerge across the rest of earnings season.
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