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There’s a rough vocabulary investors use to describe how far the market has fallen. A pullback is a drop of around 5%, common enough that it barely makes the news. A correction is a decline of 10% or more, which happens on average about once a year in the U.S. stock market. A crash is something sharper and faster: a drop of 20% or more happening over days or weeks rather than months, often accompanied by panic and heavy trading volume. If the decline drags on and stays below that 20% threshold for an extended period, it’s usually called a bear market instead.

The word “crash” gets used loosely in headlines, but the real ones share a signature: speed. The 1929 crash wiped out about 25% of the market’s value in four trading days. The 1987 crash, known as Black Monday, took 22% off the Dow Jones Industrial Average in a single session, still the worst one-day percentage drop in its history.

A lot of crashes follow the same basic pattern: prices climb well beyond what a company’s actual profits or growth prospects can justify, fueled by optimism, easy credit, or simple fear of missing out. Eventually, enough investors start to doubt the story, selling begins, and the drop that follows can be just as fast as the climb that preceded it.

The dot-com crash of 2000 is a clean example. Internet companies with no profits and, in some cases, barely any revenue were trading at enormous valuations because investors assumed the internet would make traditional business fundamentals irrelevant. When that assumption cracked, the Nasdaq index fell by roughly 78% from its peak in 2000 to its low in 2002. Many of the companies that drove the boom simply ceased to exist.

Borrowed money tends to make crashes worse. Investors who buy stocks “on margin,” meaning with money borrowed from a broker, are required to maintain a minimum amount of equity in their account. When prices fall sharply, brokers issue margin calls demanding more cash or collateral. Investors who can’t meet the call get their shares sold automatically, often at the worst possible moment, which pushes prices down further and can trigger a fresh round of margin calls for other investors.

This dynamic played a real role in the 1929 crash, when margin buying was widespread and only required a small fraction of a stock’s price as a down payment. It also showed up in 2008, though in a different form: banks and financial institutions were so leveraged against mortgage-backed securities that even a moderate decline in housing prices was enough to threaten their solvency, which in turn threatened the entire financial system.

Markets are made up of people, and people don’t always act on cool calculation once prices start falling fast. Watching an investment lose value by the hour creates real psychological pressure to sell before things get worse, even if the sale locks in a loss that might have reversed given time. When enough investors act on that instinct simultaneously, the selling itself becomes the news, prompting still more investors to sell, in a feedback loop that can detach prices from anything resembling the underlying value of the businesses involved.

Modern markets add a mechanical version of this same feedback loop. A meaningful share of trading today is done by computer algorithms that react to price movements automatically, sometimes within milliseconds. On October 19, 1987, program trading systems designed to limit losses by automatically selling as prices fell are widely believed to have accelerated the Black Monday crash, since each wave of automated selling triggered price drops that set off the next wave.

Not every crash starts inside the financial system. Sometimes an outside event forces a sudden repricing of risk across the entire economy. The COVID-19 crash in February and March of 2020 is the clearest recent example: the S&P 500 fell about 34% in just over a month as it became clear that lockdowns would shut down large parts of the global economy almost overnight. There was no speculative bubble to burst and no obvious excess of borrowed money driving it; the shock was simply that fast and that broad.

Exchanges have added mechanical safeguards specifically designed to slow crashes down. Circuit breakers now automatically halt trading on major U.S. exchanges if the S&P 500 falls 7%, 13%, or 20% in a single session, giving investors a pause to reassess instead of trading straight through a panic. These halts were actually triggered multiple times during the March 2020 crash, the first time they’d been used since being introduced after the 1987 crash.

Regulators have also tightened margin requirements and increased oversight of the kind of leverage that made 1929 and 2008 so severe, though neither leverage nor panic has ever been fully engineered out of financial markets, and probably can’t be.

Every crash in market history has eventually been followed by a recovery, though the length of that recovery has varied enormously: the market took about 25 years to fully recover from the 1929 crash on an inflation-adjusted basis, while it took a matter of months to recover from the 2020 COVID crash. That range is exactly why financial advisors tend to discourage trying to time a crash, either by predicting it or by selling everything once one starts. Selling during a crash converts a paper loss into a real, permanent one, and history suggests that investors who stay invested through the volatility generally come out ahead of those who panic-sell near the bottom and then hesitate to get back in.

If you’re thinking about how a market downturn might affect a specific goal, our Investment Calculator and ROI Calculator can help you see how contributions and time horizon change the picture, separate from any single year’s swings.

None of this means volatility should be ignored. A crash arriving the year before you planned to retire and start drawing down a portfolio is a genuinely different problem than one arriving twenty years before retirement, which is part of why many advisors recommend shifting toward a more conservative mix of investments as a goal gets closer, rather than trying to guess when the next downturn will hit.

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