Last week, we discussed what happens when after a period of low Volatility that is hit with a sudden market break. The short version is "Volatility increases."
The immediate response — buy the dip — was the knee jerk response. That is at its essence a single variable analysis (If X, then Y). The more elements we control for, the more accurate and predictive any analysis is likely to be. Let’s consider both the context of the drop, and market internals.
Mike Panzner played with the data a bit, looking at rolling 5 day periods where there have been SPX declines of 5% or more, relative to recent highs. (Talk about great timing; His book Financial Armageddon was released on March 1, 2007 — the day after the big crack — its now #52 on Amazon).
Here’s Mike’s analysis:
Over the past 30 years, there have been 99 5-day declines of 5% or more in the S&P 500, out of a total of 7,563 5-trading-session spans. In other words, they are relatively rare, occurring only 1.31% of the time.
Yet, big downside runs don’t necessarily represent the no-brainer buying opportunities that some bulls claim.
If, for example, you split the list into 5-day declines that occurred when the last day of the span was within 10% of a 52-week high, as opposed to when it was not, there is a divergence in the subsequent one-month returns.
At those times when 5-day decline have occurred near market peaks, the median performance 20 trading sessions later has been 1.60%, which is only marginally higher than the median 20-day subsequent return for all trading sessions over the three decades. Median performance for all days (T+20 trading days) is 0.99%.
However, when that is not the case — when the market has already been under pressure — the median subsequent 20-day return has been 4.53%, which represents a substantial measure of outperformance.
In general, then, when the market gets pounded for 5 days, the odds that you can make money from the long side over the course of the following month appear to be substantially higher when the market has already been suffering beforehand.
Consequently, those who argue that the 5.19% 5-day slide in the S&P 500 (through yesterday), which occurred not long after the market hit a new 52-week high, is a major buying opportunity may be in for some disappointment.
In other words, the relative market period preceding the break — are we in Bull market, near a top, or at the end of a long selloff — has significance to subsequent market action post break. By adding a 2nd variable (5% five day drops relative to recent highs) should provide more accurate guidance than merely controlling for a single variable ("Buy all 5% drops").
click for larger graph
Courtesy Michael Panzner
Also worth adding into the equation are market internals. Birinyi Associates did just that, looking at subsequent returns after the S&P500 reached extreme levels. They found that "in each period shown, if the market showed losses after the first week, it remained in the red for the two week and one month periods as well."
click for larger graphs
Courtesy of Birinyi Associates
Note that in the following table, whether we are in a Bull or Bear trends does have a high degree of correlation to subsequent outcomes.
Consider: From 1997-99, most dip buying following A/D extremes was rewarded; Doing the same thing during the bear market — in this case, 2001 thru January 2003 — was not. Since then, From September 2003 to October 2005 was quickly profitible; May 2006 was not.
Courtesy of Birinyi Associates
Blame the professors: Just as the option backdating scandal started with academic researchers noting mathematical anomalies, so too might the next brewing scandal: the I/B/E/S Analyst ratings back dating scandal.
According to a Barron’s article by Bill Alpert (buried on page 39), several professors have discovered what they describe as 54,729 non-random, ex-post changes out of 280,463 observations — a little over 19.5% of analyst recs (abstract below):
"The professors found
almost 55,000 changes that had been made in the I/B/E/S database of
stock-analyst recommendations maintained by Thomson, the Stamford,
Conn., firm that is a leading vendor of financial data. The alterations
made Wall Street’s record of recommendations look more conservative –
hiding Strong Buy recommendations and adding Sell recommendations from
1993 to 2002. That is a period for which Wall Street has drawn heat and
government sanctions for touting Internet bubble stocks.
As a result of the changes, the stock picks shown in
the database would have created annual gains that were 15% to 42%
better than the originally recorded recommendations, using a trading
strategy based on analysts’ recommendations."
The firms were the most significant participants in the data backdating were also the firms who had the closest relationship between banking and research and were the hardest hit by the Spitzer enforced settlement.
From page four of the academic working paper notes exactly how significant this was:
"Why do the historical data now look different than they once did? The contents of the database changed at some point between September 2002 and May 2004, a period that not only coincided with close scrutiny of Wall Street research by regulators, Congress, and the courts, but also saw a substantial downsizing of research departments at most major brokerage firms in the U.S.
The paper outlines four types of data changes: 1) non-random removal of analyst names from historic recommendations (anonymizations); 2) the addition of new records not previously part of the database; 3) the removal of records that had been in the data; and 4) alterations to historical recommendation levels.
The net result of this was to make many specific trading strategies appear better in retrospect than they actually were. Buying top rated stocks and shorting lowest rated stocks, based on the changed data, now perform 15.9% to 42.4% better on the 2004 revised data than on the 2002 tape, the professors state.