It’s likely some of these Americans might rethink pulling their money if they knew how quickly a portfolio can rebound from the bottom: The market took just 13 months to recover its losses after the most recent major sell-off in 2015. Even the Great Recession — a devastating downturn of historic proportions — posted a complete market recovery in just over five years. The S&P 500 then posted a compound annual growth rate of 16% from 2013 to 2017 (including dividends).

The Dow was already down 20 percent from its September 3 high, according to Yahoo Finance DJIA Historical Prices. That signaled a bear market. In late September, investors had been worried about massive declines in the British stock market. Investors in Clarence Hatry's company lost billions when they discovered he used fraudulent collateral to buy United Steel. A few days later, Great Britain's Chancellor of the Exchequer, Philip Snowden, described America's stock market as "a perfect orgy of speculation." The next day, U.S. newspapers agreed.

Meanwhile, those who are most concerned with corporate coffers, the chief financial officers (CFOs), are the least optimistic about the U.S. economy and, by extension, the stock market. Almost half (48.6%) of CFOs surveyed believe that the U.S. will fall into a recession in 2019, and a whopping 82% of them believe that a recession will occur in 2020. (Source: “Recession Considered Likely By Year-End 2019,” Duke CFO Global Business Outlook, last accessed March 14, 2019.)

What Percentage Did the Market Drop in 1987?

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.[9] Among others, mathematician Benoît Mandelbrot suggested as early as 1963 that the statistics prove this assumption incorrect.[10] 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.[11] 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.[12] George Soros said in late October 1987, 'Mr. Robert Prechter's reversal proved to be the crack that started the avalanche'.[13][14]
On Black Monday, the Dow Jones Industrial Average fell 38.33 points to 260, a drop of 12.8%. The deluge of selling overwhelmed the ticker tape system that normally gave investors the current prices of their shares. Telephone lines and telegraphs were clogged and were unable to cope. This information vacuum only led to more fear and panic. The technology of the New Era, previously much celebrated by investors, now served to deepen their suffering.
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 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.

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.[19][20]