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.