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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. |
