Of course, with any trading strategy or methodology, there are a number of criticisms that can be levied.
Because the trading results for MAAD ultimately rely on the net up/down count of the top 20 Most Actives, the maximum fluctuation in MAAD necessarily is capped at 20 issues, even though there are no such limitations on price movement or volume. An example of this phenomenon can be found in the October 1987 Crash, when MAAD apparently did not reflect the unusual magnitude of the price decline. In this example, however, MAAD correctly identified that decline as potentially no more than an intermediate-term pullback within the context of a major cycle advance. This indication was in sharp contrast to widespread fear at the time that the Crash of ’87 was the beginning of a new bear market and an economic depression.
In addition, it could be asserted that the 20-fold increase in exchange volume over the past 50 years could have made it increasingly difficult for MAAD to adapt. To the contrary, MAAD has adjusted well because volume, as such, is less important to MAAD than the direction of the top 20 volume leaders insofar as the MAAD net issues count is concerned.
Another criticism of the procedure could be that collecting Most Actives data on a dollar volume basis makes more sense than using share volume. For example, on Dec. 9, 2011, Bank of America, quoted at 5.72 on 1.3 billion shares, traded more share volume than the S&P SPDR exchange-traded fund (ETF) quoted at 126.05 on 1.09 billion shares, yet the dollar volume was only $7.525 billion for BAC versus $108.097 billion for SPY. Such differences average out, however, because the majority of Most Actives issues tend to move in the same direction at the same time. As more dollar volume data become available in the future, we will be able to gain greater insights into the share volume vs. dollar volume question.
A better measure
For decades, the advance/decline line has been used to measure market breadth and to identify trends and trend divergences. In this two-part series, we sought to demonstrate that conventional A-D data, such as those reported by the New York Stock Exchange, are seriously flawed both in terms of the raw data and the subsequent results derived from that data. But when 20 Most Actives data are inserted into an A-D line that is massaged in an objective strategy trading the S&P 500, the Most Actives outperformed the NYSE series on every quantifiable level.
There is no practical way to fix an A-D line that uses conventional stock exchange A-D data. The biggest fault of the indicator is that it relies on unweighted data that are used in a cumulative A-D line that is compared to a weighted index like the S&P 500. And the process of excluding data, such as bond funds, preferred issues, closed end mutual funds and ETFs in favor of pure equities in the hope of repairing the A-D line, only will create a diminishing pool of issues from which to measure market health. Volume must be the sole determinant of inclusion, not asset purity based on subjective or arbitrary rules.
When Most Actives data are substituted for flawed A-D data, that change not only improves trading results, but also gives analysts a better reflection of big money tendencies because that segment tends to be, in effect, “the market.”
This research was limited to Most Actives monthly data and long-term trends evident over the past 50 years. These findings should stimulate additional research into applications of the Most Actives on all cycles, including daily and weekly data, as those shorter time frames offer opportunities for more frequent data sampling that can be rich in analytical possibilities.