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05.08.202612:45 Forex Analysis & Reviews: Drops of 2.5% could be enough: how algorithms can crash market in one day

Relevancia 03:00 2026-08-06 UTC--4
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While investors and traders continue to flock into long positions in euphoria and push stock indices to record highs, Michael Burry — the investor famed for accurately predicting the 2008 mortgage crisis — is betting against the US stock market rally and warning of a risk of a crash "on the scale of 1987."

Exchange Rates 05.08.2026 analysis

He argues the danger lies not only in overheated valuations of AI-related companies but in a much less visible — yet potentially more dangerous — mechanism: funds that automatically adjust their stock allocations based on volatility. Michael Burry estimates the aggregate size of such strategies at roughly $500 billion.

The logic of the risk is mechanical rather than psychological, which makes it especially worrying. While markets are calm, these funds gradually increase their exposure to risky assets. But once stocks start to fall, the algorithms simultaneously and synchronously trim positions, amplifying the sell-off instead of dampening it. Burry's calculations suggest that a drop of just 2.5% in the S&P 500 could force these funds to cut stock allocations from about 77% to 50%. That triggers a classic chain reaction: selling pushes volatility higher, higher volatility provokes further selling, which in turn hits stop orders and accelerates the decline.

Michael Burry sees a similar mechanism as having intensified the crash on Black Monday, October 19, 1987, when the Dow Jones plunged 22.6% in a single session — the largest one-day fall in its history. The historical parallel is important: the 1987 crash was driven less by fundamentals than by technical portfolio strategies that, in a panic, sold stock simultaneously and amplified the collapse rather than mitigating it. Burry's warning essentially points to the re?accumulation of a structurally similar mechanism in today's system — now embodied in modern volatility-targeting funds rather than the portfolio-insurance schemes of the 1980s.

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