March 24, 2017
For as long as there have been Advance-Decline (A-D) data that people have been interested in following, there have been criticisms of that very A-D data for including “the wrong sorts” of issues. Back in 1962, Joe Granville and Richard Russell both pointed to the big divergence between the NYSE A-D Line and the major averages like the DJIA. That divergence preceded a 27% decline in the DJIA, so in that moment the A-D Line suddenly became much more interesting to a lot of people.
But critics noted then that the NYSE-listed issues contained utilities and insurance company stocks which were “interest rate sensitive”, and which were supposedly contaminating the data. The same criticism persists today, but now it is leveled against preferred stocks, bond related closed end funds (CEFs), and other issues that trade like stocks on the NYSE. Those other issues make up about 40% of the issues, although they trade only a small fraction of the volume.
Many analysts assert that one should preferentially follow the A-D numbers for “common stocks”, sometimes referred to as “operating companies only”. This distinction supposedly filters out those damned contaminants. So everyone would supposedly be happier and better off if they just followed the right sorts of data, and ignored those “others”.
The problem is that the purified A-D data are actually not always better. The chart above shows that when the Common Only A-D Line disagrees with the NYSE Composite Index, it is usually the price index that ends up being right. This is a big problem. The whole reason for hiring an A-D Line to work for you is to give you a different answer from what the price indices are saying. Having an index give you the same message that prices give you is useless. And having data that give you the wrong answer at a pivotal time is worse than useless.
The one type of issue on the NYSE that is most often blamed for contaminating the A-D data is the closed end bond funds. They only make up about 7% of the listed issues, and hardly trade any of the volume, but they are continually trotted out as the “usual suspects” for messing up the A-D data.
This is problematic both from the standpoint of raw prejudice, and more importantly because it is just not true. These issues tend to be the better canaries in the coal mine, warning of trouble ahead of when such warnings come from the common-only A-D data or other indications.