V.7:5 (140-142): Public/member short selling by Arthur Merrill
Product Description
Public/member short selling
by Arthur Merrill
The statistics on shorting by professionals and by the public can be combined into revealing and successful indicators. The basic figures are published, two weeks late, in Barron's and The Wall Street Journal. The New York and American stock exchanges report total short sales and the portions of the total made up by specialists, floor traders and other members for their own account.
These three subcategories can be added together to yield "total members," or shorting by the smart money. Total members can be subtracted from total short sales to yield "public short sales," or not-so-smart shorts.
Analysts John McGinley and Walter Deemer have promoted a simple comparison of the two groups using a ratio of public short sales to member short sales. When this index has a high value, the public is more pessimistic than the professionals. This is a bullish sign. To smooth out the fluctuations, I use a 22% exponential moving average. (To calculate, add 22% of each new statistic to 78% of the preceding average to yield a new average.)
The statistics on shorting by professionals and by the public can be combined into revealing and successful indicators. The basic figures are published, two weeks late, in Barron's and The Wall Street Journal. The New York and American stock exchanges report total short sales and the portions of the total made up by specialists, floor traders and other members for their own account.
These three subcategories can be added together to yield "total members," or shorting by the smart money. Total members can be subtracted from total short sales to yield "public short sales," or not-so-smart shorts.
Analysts John McGinley and Walter Deemer have promoted a simple comparison of the two groups using a ratio of public short sales to member short sales. When this index has a high value, the public is more pessimistic than the professionals. This is a bullish sign. To smooth out the fluctuations, I use a 22% exponential moving average. (To calculate, add 22% of each new statistic to 78% of the preceding average to yield a new average.)
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