V.7:11 (405-407): Trading windows for technical indicators by Frank Tarkany
Product Description
Trading windows for technical indicators
by Frank Tarkany
In a number of articles over the past three years, Stocks & Commodities' authors have developed evidence that changes in securities prices are generated by a process that is random, stationary and dependent. In English: today's prices depend to some extent on past prices.
Frank Tarkany presented evidence in previous issues that prices are non-random and dependent. The next questions are, "If prices are non-random and dependent, what is their nature and over what period of time do past prices affect today's prices and future prices?"
It turns out that it is easier to answer the second question than the first. Statistical techniques explained in past issues can identify the period during which non randomness and dependence hold. These periods are "trading windows," lengths of time within which technical tools sensitive to time can be expected to operate more efficiently. Persons using averages or stochastics, or any other indicator with a time parameter, should want to know how far into the past they should go in calculating their indicator—and how far into the future it might be indicative of price performance.—Editor
What's the window within which you can trade the Dow Jones Industrial Average short-term and expect its price to relate to past prices? In the Dow's case, it's between 19 and 25 days (four or five weeks), remarkably close to the 21-day range market practitioners use as a rule of thumb. For long-term traders, the Dow window exists from 140 to 149 days (29 to 31 weeks).
In a number of articles over the past three years, Stocks & Commodities' authors have developed evidence that changes in securities prices are generated by a process that is random, stationary and dependent. In English: today's prices depend to some extent on past prices.
Frank Tarkany presented evidence in previous issues that prices are non-random and dependent. The next questions are, "If prices are non-random and dependent, what is their nature and over what period of time do past prices affect today's prices and future prices?"
It turns out that it is easier to answer the second question than the first. Statistical techniques explained in past issues can identify the period during which non randomness and dependence hold. These periods are "trading windows," lengths of time within which technical tools sensitive to time can be expected to operate more efficiently. Persons using averages or stochastics, or any other indicator with a time parameter, should want to know how far into the past they should go in calculating their indicator—and how far into the future it might be indicative of price performance.—Editor
What's the window within which you can trade the Dow Jones Industrial Average short-term and expect its price to relate to past prices? In the Dow's case, it's between 19 and 25 days (four or five weeks), remarkably close to the 21-day range market practitioners use as a rule of thumb. For long-term traders, the Dow window exists from 140 to 149 days (29 to 31 weeks).
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