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Linear Regression Slope
Description
The Linear Regression method provides several useful outputs for
technical analysts, including the Slope. The Slope shows how much
prices are expected to change per unit of time. Some may remember
this as “rise over run.”
Interpretation
It is helpful to consider Slope in relation to
r-squared. While Slope gives you the
general direction of the trend (positive or negative), r-squared
gives you the strength of the trend. A high
r-squared value can be associated with a high positive or
negative Slope.
When the Slope of the trend first becomes significantly positive,
you could open a long position. You could sell, or open a short
position when the Slope first becomes significantly negative. You
should refer to the table below to determine when a trend is deemed
“significant.” For example, if the 14-period Slope has recently
turned from negative to positive (i.e., crossed above zero), you may
consider buying when r-squared crosses
above the 0.27 level.
To determine if the trend is statistically significant for a given
x-period linear regression line, plot the r-squared indicator and
refer to the following table. This table shows the values of
r-squared required for 95% confidence level at various time periods.
If the value is less than the critical values shown, you should
assume that prices show no statistically significant trend.
You may even consider opening a short-term position opposite the
prevailing trend when you observe the Slope rounding off at extreme
levels. For example, if the Slope is at a relatively high level and
begins to turn down, you may consider selling or opening a short
position.
There are numerous ways to use the linear regression outputs of
Slope and r-squared in trading systems.
For more detailed coverage, refer to the book The New Technical
Trader by Tushar Chande and Stanley Kroll.
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