Oct 13, 2003

If such a concept as “maximum overbought” exists, we are seeing it now. On a 10-day moving average basis, which is by far my preferred way of looking at it, market breadth is as stretched as it gets, especially so far from a selling panic that usually creates extreme overbought breadth readings after the market snaps back.

As of today’s close, our NYSE daily cumulative TICK is the highest in its history, going back to 1999. The same indicator for the Nasdaq is its highest since March 2002. The 10-day average of the NYSE up issues ratio (i.e. advancing issues as a percentage of total advancing and declining issues) is at 62%. The 10-day average of the up volume ratio is at 65%. The 10-day average of the new high ratio (i.e. new highs as a percentage of new highs and new lows) is at 96%. All of these readings are in the top 5% since 1965, and the up issues and up volume ratios are in top 2%.

I am well aware of the argument that decimalization makes historical breadth comparisons difficult, and in fact I addressed that in the comment on October 6^th. I think accurate comparisons can still be made as long as current readings are adjusted on the days the market moves the most. For example, on 10/01 and 10/13, there were more than twice as many advancing issues as declining issues. Even though the market didn’t gain much on those days, let’s still adjust the breadth figures. As I showed in the October 6^th comment, we would have to reduce the number of advancing issues by around 20% in order to accurately compare them to historical, pre-decimalization figures. So, if we cut the number of advancing issues on those two days by 20%, then the 10-day up issues ratio drops to 61.5% from 62.3% - a difference of 0.8%. Certainly not a dramatic change.

I wanted to see how the market performed after getting other such “trifectas” of overbought readings. I combined all three ratios (up issues, up volume and new highs) to create a sort of Super Breadth indicator and looked at the other times we reached such overbought levels as now. The table below shows this data for the S&P 500 since 1965. I was only interested in distinct occurrences, so I eliminated any readings that happened within 3 months of another reading. The returns below are given as of the first day the Super Breadth indicator reached as extreme a reading as we’re seeing now. There were 21 distinct occurrences. The figures in the table below are in percents.

	5 DAYS LATER	10 DAYS LATER	20 DAYS LATER	30 DAYS LATER	60 DAYS LATER
Avg Ret	0.4	1.2	1.6	2.2	2.1
% Pos	62	67	67	67	57
Max Gain	4.9	5.2	8.5	10.5	20.6
Max Loss	-4.6	-4.6	-5.8	-9.3	-15.3
Avg M.G.	1.0	1.9	3.2	4.3	6.4
Avg M.L.	-0.7	-0.9	-1.4	-1.9	-3.5

Avg Ret: The percentage gain or loss in the S&P the given number of days later.

% Pos: The percentage of time the S&P was higher the given number of days later.

Max Gain: The maximum paper gain one would have enjoyed within the given number of days.

Max Loss: The largest paper loss (drawdown) one would have suffered within the given number of days.

Avg M.G.: This figure is an average of the maximum paper gain across all occurrences.

Avg M.L.: This is the average maximum loss (drawdown) one would have suffered across all occurrences.

Overall, this type of extreme overbought condition didn’t lead to anything spectacular in either direction. I would certainly say that the results are more positive than negative, but it’s nothing I would stake money on. For example, if we look out 30 days, the S&P 500 was higher 67% of the time (14 out of the 21 instances). On average, it was a little over 2% higher than when breadth first became extremely overbought. Out of all 21 occurrences, the most positive one showed a gain of 10.5% within those first 30 days (this actually happened twice, in February 1975 and August 1982). The most negative instance showed a loss of 9.3% within the first 30 days, which occurred in June 1975. Perhaps the most telling statistics are the “Avg M.G.” and “Avg M.L.” which show the average maximum gain and average maximum loss across all 21 occurrences. As we can see from the table, the average maximum gain was twice as large as the average maximum loss (or drawdown) across nearly every time frame.

Worth noting is that the best longer-term gains came when the market was immediately positive, and most of the worst losses came when the S&P began to dip right after the overbought readings. The table below shows in a quick glance how the S&P performed after 30 days, using the first 5 days as a guide.

	If the market DID dip 1% in the first 5 days	If the market DID NOT dip 1% in the first 5 days
Avg Ret	0.2	4.0
% Pos	50	82
Max Gain	10.5	10.5
Max Loss	9.3	4.1
Avg M.G.	2.9	5.8
Avg M.L.	-2.9	-0.4

It is clear from this table that how the market first reacts to these overbought readings can be very telling. For example, if the S&P sold off by 1% or more in the first 5 days, then there was only a 50/50 chance that it would end up higher after 30 days. However, if it didn’t sell off in the first week, then there was an 82% that it would be higher after 30 days. The other statistics back up the claim that the first week can be key.

Again, this is mildly positive for the market, and supports the study I showed this weekend which looked at times when the S&P has been more than 5% above its 200-day average for more than 100 consecutive days. In general, both suggested that such extended and overbought conditions tend to continue for some time more than immediately fall apart.

Clearly, the broader market is extremely extended in the short-term. When we have conditions such as this, there is a good chance that we can see breakouts fail. My favorite strategy for trading these situations is to see if the market trades above previous resistance (particularly on a gap open), then falls back below a previous high. With such overbought conditions and an obvious failure signal, there is usually enough follow-through to profit. This is more of a daytrading-type strategy, so it can be very quick, but it also has a high probability of succeeding. So, if the market gaps open tomorrow, which it looks like it very well might on the back of the INTC earnings release, and it opens above 1050ish, then falls back below 1049, I intend to use that as a short-term sell signal (with a stop loss order above the opening prices or Globex highs if trading the futures). If the trade goes in my favor by more than 3 points or so, I move the stop loss to breakeven and keep trailing it down if it continues to go in my favor. This is not a recommendation to trade, it is meant only as an example of how I define high-probability, low-risk trade ideas using sentiment as a setup and price as a trigger.

On a longer-term time frame, should we make and hold new highs, I cannot justify remaining in short positions. While the outright speculation from those most wrong most often (e.g. the smallest of options traders as reflected in the ROBO put/call ratio, and the Rydex mutual fund timers) inherently wants me to go the other direction, the price persistency the broader market has displayed can continue for a long time, even in the face of overbought conditions. So, for longer-term trades my preference is to wait for some type of price confirmation (e.g. a violation of the low of the high bar of the move – which would be 1040 on the S&P right now) before becoming aggressive in short positions.

This disclosure is not intended as trading advice in any form. It is meant as a note to subscribers that the author may have a position directly affected by the market outlook reflected in the commentary. Although the author takes great pains to remain objective in any commentaries, it is only fair that readers should know that the author may have taken positions in accordance with his market outlook. Positions can and do change at any time, without notice to the reader.