Black swan event.
In plain English
Black swan, a term popularized by Nassim Nicholas Taleb, describes an outcome outside the range past data suggested was possible, with severe consequences and a tidy explanation attached only in hindsight. The label matters because risk models built on historical distributions systematically understate the odds of extreme outcomes. Financial returns have fatter tails than a normal distribution implies, so results described as one in a thousand years arrive far more often than that. The useful response is structural rather than predictive, such as sizing positions so that no single outcome can end you. Calling something a black swan after the fact is often an excuse rather than an analysis.
01Why it matters
If a plan only survives ordinary years it is not much of a plan, and the events that decide long-run outcomes are usually the ones no model priced in.
02The math, step by step
Say a model assumes daily moves follow a normal distribution, which would make a 5 percent single-day drop roughly a one in ten thousand event. Drops of that size have occurred many times in market history. The model was not slightly wrong. It was wrong about the shape of the distribution.
Illustrative example. The amounts here are hypothetical, chosen to show how the math works, not real quoted rates or figures.
03What this is NOT
It is not every selloff. A recession that many analysts warned about is not a black swan, however painful it is. The term requires that the outcome sat outside what the prevailing models treated as possible, which is a much smaller set of events.
04Receipts
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Plain-English answers from our glossary. Receipts included. Never advice.
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