Stock market crashes are social phenomena where external economic events combine with crowd behavior and psychology in a positive feedback loop where selling by some market participants drives more market participants to sell. Generally speaking, crashes usually occur under the following conditions: a prolonged period of rising stock prices and excessive economic optimism, a market where P/E ratios (Price-Earning ratio) exceed long-term averages, and extensive use of margin debt and leverage by market participants. Other aspects such as wars, large-corporation hacks, changes in federal laws and regulations, and natural disasters of highly economically productive areas may also influence a significant decline in the stock market value of a wide range of stocks. All such stock drops may result in the rise of stock prices for corporations competing against the affected corporations.
Research at the Massachusetts Institute of Technology suggests that there is evidence the frequency of stock market crashes follows an inverse cubic power law. This and other studies such as Prof. Didier Sornette's work suggest that stock market crashes are a sign of self-organized criticality in financial markets. In 1963, Mandelbrot proposed that instead of following a strict random walk, stock price variations executed a Lévy flight. A Lévy flight is a random walk that is occasionally disrupted by large movements. In 1995, Rosario Mantegna and Gene Stanley analyzed a million records of the S&P 500 market index, calculating the returns over a five-year period. Researchers continue to study this theory, particularly using computer simulation of crowd behaviour, and the applicability of models to reproduce crash-like phenomena.
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%.
The crash on October 19, 1987, a date that is also known as Black Monday, was the climactic culmination of a market decline that had begun five days before on October 14. The DJIA fell 3.81 percent on October 14, followed by another 4.60 percent drop on Friday, October 16. On Black Monday, the Dow Jones Industrials Average plummeted 508 points, losing 22.6% of its value in one day. The S&P 500 dropped 20.4%, falling from 282.7 to 225.06. The NASDAQ Composite lost only 11.3%, not because of restraint on the part of sellers, but because the NASDAQ market system failed. Deluged with sell orders, many stocks on the NYSE faced trading halts and delays. Of the 2,257 NYSE-listed stocks, there were 195 trading delays and halts during the day. The NASDAQ market fared much worse. Because of its reliance on a "market making" system that allowed market makers to withdraw from trading, liquidity in NASDAQ stocks dried up. Trading in many stocks encountered a pathological condition where the bid price for a stock exceeded the ask price. These "locked" conditions severely curtailed trading. On October 19, trading in Microsoft shares on the NASDAQ lasted a total of 54 minutes.
What about corporate America? According to a New York Times survey of business leaders, almost half of the respondents believe that the U.S. could face a recession by the end of 2019. And if not in 2019, then in 2020. (Source: “A jarring new survey shows CEOs think a recession could strike as soon as year-end 2019,” Business Insider, December 17, 2018.)
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.
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The crash followed an asset bubble. Since 1922, the stock market had gone up by almost 20 percent a year. Everyone invested, thanks to a financial invention called buying "on margin." It allowed people to borrow money from their broker to buy stocks. They only needed to put down 10-20 percent. Investing this way contributed to the irrational exuberance of the Roaring Twenties.
By the end of the weekend of November 11, the index stood at 228, a cumulative drop of 40% from the September high. The markets rallied in succeeding months, but it was a temporary recovery that led unsuspecting investors into further losses. The Dow Jones Industrial Average lost 89% of its value before finally bottoming out in July 1932. The crash was followed by the Great Depression, the worst economic crisis of modern times, which plagued the stock market and Wall Street throughout the 1930s.
Research at the New England Complex Systems Institute has found warning signs of crashes using new statistical analysis tools of complexity theory. This work suggests that the panics that lead to crashes come from increased mimicry in the market. A dramatic increase in market mimicry occurred during the whole year before each market crash of the past 25 years, including the recent financial crisis. When investors closely follow each other's cues, it is easier for panic to take hold and affect the market. This work is a mathematical demonstration of a significant advance warning sign of impending market crashes.