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Standard
Error
Description
Standard
Error measures how closely prices congregate
around a linear regression line.
The closer prices are to the linear
regression line, the higher the r-squared
value and the stronger the trend.
For
example, if each day’s closing price was equal
to that day’s regression line value, then the
standard error would be zero.
The more variance or “noise” around the
regression value, the larger the standard error
and the less reliable the trend.
Interpretation
High
standard error values indicate that the
security’s prices are very volatile around the
regression line. Changes
in the prevailing trend (over the number of time
periods specified) are usually preceded by a
rapidly increasing standard error.
Standard
error can be used effectively in combination with
the r-squared indicator.
Changes in trend are often signalled by a
high downward moving r-squared, a low upward
moving standard error, or a low upward moving
r-squared and
a high downward moving standard error.
In other words, when the two are at extreme
levels and begin to converge, look for a change in
trend.
Note
that a change in trend does not necessarily mean
that an upward trend will reverse to a downward
trend. Sideways
movement is also considered a "change". |
