V.7:3 (82-85): Variable sensitivity stochastics by William Mason
Product Description
Variable sensitivity stochastics
by William Mason
In this article, I am presenting elementary Statistical Analysis of Stocks and Indices (SASI) in an index, three new indicators (SASITOP, SASIBOT and sigma limits) plus variable-sensitivity stochastics based on statistical analysis. SASITOP is very similar to stochastics but uses plus and minus variance (sigma) limits in place of the high and low over the time window. The data is modified for sharper sensitivity. Because SASITOP and SASIBOT are the reciprocal of each other, I will concentrate only on SASITOP in this article and apply it to the Technical Index which measures overall market breadth (see Stocks & Commodities, January 1989) although it may be applied to any index or set of data.
To generate SASITOP:
1. Calculate the statistical sample variation s2. For a finite population, s2 is mathematically described by:
...
where X represents the values in your time series and n is number of observations
Don't panic. Use a spreadsheet like Lotus 1-2-3 which has a built in variance function. The Lotus 1-2-3 variance function has to be modified: s2m = s2[n/(n-1)] to account for a finite population (where n = the time window). Some spreadsheets such as Excel have this modification built in to the variance (VAR) function.
...
In this article, I am presenting elementary Statistical Analysis of Stocks and Indices (SASI) in an index, three new indicators (SASITOP, SASIBOT and sigma limits) plus variable-sensitivity stochastics based on statistical analysis. SASITOP is very similar to stochastics but uses plus and minus variance (sigma) limits in place of the high and low over the time window. The data is modified for sharper sensitivity. Because SASITOP and SASIBOT are the reciprocal of each other, I will concentrate only on SASITOP in this article and apply it to the Technical Index which measures overall market breadth (see Stocks & Commodities, January 1989) although it may be applied to any index or set of data.
To generate SASITOP:
1. Calculate the statistical sample variation s2. For a finite population, s2 is mathematically described by:
...
where X represents the values in your time series and n is number of observations
Don't panic. Use a spreadsheet like Lotus 1-2-3 which has a built in variance function. The Lotus 1-2-3 variance function has to be modified: s2m = s2[n/(n-1)] to account for a finite population (where n = the time window). Some spreadsheets such as Excel have this modification built in to the variance (VAR) function.
...
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