Stocks & Commodities V. 22:12 (68-72): Breadth Statistics: What Do They Tell You? by Tom McClellan

Stocks & Commodities V. 22:12 (68-72): Breadth Statistics: What Do They Tell You? by Tom McClellan
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Stocks & Commodities V. 22:12 (68-72): Breadth Statistics: What Do They Tell You? by Tom McClellan

Let’s revisit the advance/decline line.

Advance/decline (A/D) statistics have been used since 1926, when they were first analyzed by Col. Leonard Ayres and James Hughes of the Cleveland Trust Co. In the early 1960s, the use of the advance/decline line gained more attention with the writings of Richard Russell, Joseph Granville, and others who were able to demonstrate its effectiveness when it showed a diverging condition during the 1961–62 market top.


There are different ways to construct A/D lines. The most common method is by summing up the daily breadth (advancing issues minus declining issues), which means that the value of the A/D line changes by each new day’s breadth reading. Although this calculation is simple, it poses problems for long-term comparisons. Over the years, there has been an increase in the number of issues traded. A difference of 100 in A/D would have a significant meaning in the 1930s, when there were only 600 or so issues traded. But now, with more than 3,400 stocks traded on the New York Stock Exchange (NYSE), that difference would not mean as much. Because of this, a “normal” A/D line may have mismatched amplitudes of strong or weak breadth days in long-term comparisons.

One way to get around this is by using a ratio instead of the raw breadth statistics. You can calculate this ratio using the formula:

A/D ratio = [(A-D)/(A+D)] x 1000

Although the number 1000 is used here, you can use other values as long as you are consistent throughout the data.

From Figure 1, you can see that for periods of up to a couple of years there is not much difference in appearance between the normal A/D line and the A/D ratio. Because of this, many technical analysts use the raw data for short- to intermediate-term analysis (days to weeks).

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