We presented the concept of mean reversion in an earlier post, in this article we will present a tweaked version of mean reversion that utilizes Linear Regression Curves instead of moving averages.
Moving average is a lagging indicator so essentially the Linear Regression Curves can potentially provide a better fit of the available data. It is helpful to compare the results of both methods nonetheless – same applies to any tweaked versions tested, it would make sense to first compare to the base version.
Linear Regression Curve is essentially a bundle of numerous lines, but the extreme ends of the lines are hidden (upper and lower bounds), while the mid-point is only shown and is connected respectively to other mid-points across the time series.
Back-test implementation of mean reversion on S&P500 stocks using Linear Regression Curves:
- Estimate the Linear Regression Curve per look-back window
- In each day compare the closing price with the average value estimated;
- If the closing price is greater than the average + n * deviation go short and close when you cross the mean or at a pre-determined time
- If the closing price is less than the average – n * deviation go long and close when you cross the mean or at a pre-determined time