There is no numerically specific definition of a stock market crash but the term commonly applies to steep double-digit percentage losses in a stock market index over a period of several days. Crashes are often distinguished from bear markets by panic selling and abrupt, dramatic price declines. Bear markets are periods of declining stock market prices that are measured in months or years. Crashes are often associated with bear markets, however, they do not necessarily go hand in hand. The crash of 1987, for example, did not lead to a bear market. Likewise, the Japanese bear market of the 1990s occurred over several years without any notable crashes.
The mathematical description of stock market movements has been a subject of intense interest. The conventional assumption has been that stock markets behave according to a random log-normal distribution. Among others, mathematician Benoît Mandelbrot suggested as early as 1963 that the statistics prove this assumption incorrect. Mandelbrot observed that large movements in prices (i.e. crashes) are much more common than would be predicted from a log-normal distribution. Mandelbrot and others suggested that the nature of market moves is generally much better explained using non-linear analysis and concepts of chaos theory. This has been expressed in non-mathematical terms by George Soros in his discussions of what he calls reflexivity of markets and their non-linear movement. George Soros said in late October 1987, 'Mr. Robert Prechter's reversal proved to be the crack that started the avalanche'.
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The following day, Black Tuesday, was a day of chaos. Forced to liquidate their stocks because of margin calls, overextended investors flooded the exchange with sell orders. The Dow fell 30.57 points to close at 230.07 on that day. The glamour stocks of the age saw their values plummet. Across the two days, the Dow Jones Industrial Average fell 23%.