V.17:4: Letters

V.17:4: Letters
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The editors of S&C invite readers to submit their opinions and information on subjects relating to technical analysis and this magazine. This column is our means of communication with our readers. Is there something you would like to know more (or less) about? Have you run across trading techniques, services or products that have proved useful? Tell us about it. Without a source of new ideas and subjects coming from our readers, this magazine would not exist.

Address your correspondence to: Editor, STOCKS & COMMODITIES, 4757 California Ave. SW, Seattle, WA 98116-4499, or E-mail to editor@traders.com. Letters published may be edited for length or clarity. The opinions expressed in this column do not necessarily represent those of the magazine.—Editor



Brian J. Millard’s February 1999 S&C article “Moving Averages, First Principles” on moving averages fails to address important issues that arise when moving averages (MA) are used in cyclical models. E. Slutsky’s 1937 article “Econometrica” told us that the moving average transformation may induce the appearance of cycles where none exist in random data.

One practical approach may be to use a second, different transformation to determine if a cycle with the same characteristics (wavelength, amplitude, phase) can be discerned in the data. Examining the closing price at the end of each week or the average closing price (or first differences, as Millard suggests) in each week are possible alternatives. Another strategy for those with deeper mathematical skills is to apply power spectrum analysis, which decomposes the variation in data into the sum of cycles, each with differing characteristics. Spectral analysis may confirm the existence of cycles with characteristics similar to those shown using the MA transformation.

Further, cycles sometimes appear in data transformed by MAs because of just a few outliers or extreme values above and below the trendline. Plotting the raw data against the moving average data may reveal whether just a few datapoints are involved or whether more pronounced, smoother cycles exist in the raw data. If in fact the cycles are caused by just a few outliers, then entry and exit strategies must be more carefully considered. To the extent that more pronounced cycles can be discerned in the untransformed data, timing may be easier.


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