This weighting problem was evident during the 2000-02 bear market when large-cap tech stocks, which constituted big portions of the major indexes such as the S&P 500 and Nasdaq, declined sharply. Because stocks other than tech issues reflected more NYSE advances than declines, the NYSE A-D line rose. MAAD remained in sync with the major indexes, reflecting big money bearishness and declined (see “MAAD and the S&P 500,” below).
Third, as the number of listed stocks has expanded steadily over the years, the comparability and consequent reliability of the NYSE A-D line has been compromised by its progressive, upward bias. Today, there are nearly 3,200 NYSE issues traded while in 1961 there were 1,200. As a consequence, the upward drift in NYAD may be the result of an increase in the number of shares listed rather than long-term market optimism.
The problem with the A-D line is not that it’s a bad theory or a poor design. In fact, the concept of an indicator that accurately reflects a majority market vote is an analytical plus. But as long as the A-D line continues to rely on flawed data, it will remain problematic. That fault was especially noticeable when NYAD predicted a premature stock market top in April 1998 nearly two years before the eventual bull market high in early 2000. Again, it failed by predicting a bottom almost two years prior to the bear market lows of early 2002 (see “Inconsistent performer,” below).
Enter the 20 Most Actives. Reported daily and weekly, the Most Actives are the top-tier issues that big money chooses to buy and sell. Large investors necessarily gravitate to high-volume, large-cap securities because in those issues they receive the best trade executions. Conversely, big money cannot effectively trade small-cap issues because of a lack of liquidity.
Unfortunately, data collection for the Most Actives can be challenging to the extent that there is no readily available historical database, and retrieval via an electronic source, as opposed to manual data collection, can create corroboration issues. For example, we may not know if the Most Actives data we have collected electronically is correct if we have no reference point, such as the weekly Barron’s Most Actives list.
In addition, to create a historically reliable Most Actives list, the series must include all issues previously traded, thus making the data free of survivor bias. If an issue is no longer exchange-listed, some electronic databases do not retain that issue and its volume, and the creation of MAAD becomes difficult if not impossible.