The U.S. equity markets were under strong selling pressure on Wednesday, Nov. 7, 2007. The sell-off was across all the sectors. The S&P 500 Index was down 44.62 points, or 2.94%, to 1,475.65. The Nasdaq was down 76.42 points, or 2.70%, at 2,748.76. The Dow Jones Industrial Average was down 360.92 points, or 2.64%, to end at 13,300.02.
The credit crisis, oil knocking at $100, and the slow down in corporate earnings growth has the market very nervous. The nervousness is clear in the high market swings we are seeing these days. On Tuesday, Nov. 6, 2007 the S&P 500 was up more than 1% and then on the next day was down almost 3%. What affect do these big inverse market moves have on the S&Ps?
Q: What is the historical performance of S&P 500 Index over the next 60 trading days when it is down more than 2.75% on Wednesday and the day before the is up more than 1%; omitting any repeat occurrences within 10 trading days?
A: According to the 10 previous occurrences of this event, EventEdge indicates that the index has shown a very strong bearish edge that peaks 30 trading days after the event. Thus, the projected date for the peak of the bearish edge relative to the most recent occurrence of the event (Wednesday, Nov. 7, 2007) is Thursday, Dec. 20, 2007.
The S&P 500 declines in 100% of the cases (10 of 10) by an average of 9.8% relative to the close on the event date. The overall return of the 10 cases is -9.8%, which, based on the close on the event date (1475.62), provides a target price of 1331.01 on Thursday, Dec. 20, 2007.
This is one of the most compelling conditions I have come across, because of the size of the data and also the simplicity of the conditions. The edge is extremely bearish. It is important to note that both the crash of 1929 and 1987 had similar conditions, a rally on Tuesday and big sell of on Wednesday to start the crash.
If you would like to see more details of this historical edge, go to www.markethistory.com
Travis Nadelhoffer is an analyst with MarketHistory.com and an account representative with Logical Information Machines.
