Thoughtpwr wrote:
What is R-Squared measuring? Thanks, I am looking for confirmation of my own analysis. Thx, EJ
Goodness of fit... R^2 measures how well the regression equation fits the data in the look back window.
Regression analysis is a statistical method for fitting and equation to (typically) measured data. The most common equation being fit is a line y = mx + b athough it could be a polynomial, power function, sine wave etc. For a line, given a set of at least two (x,y) data points, using (typically) least squares regression you can find the two unknowns: m (slope) and b (intercept). "Least squares" implies choosing (calculating actually) m and b such that the sum of the squares of the deviations: (actual value (i.e. measured) minus equation predicted)^2 are minimized.
So whats R^2? is a measure of the "goodness of fit" for the solution found by the method of least squares. Its a number that ranges between 1 (a perfect fit... perfect correlation) and zero (a terrible fit no correlation). The other thing UFO is showing is the slope of the regression: m. If its positive, prices (in the window used to determine the equation) are trending up. It its negative, prices ( in the window used to determine the equation) are trending down.
Lest think about this for a minute... the results are highly dependent on what "look back" window is choosen. Lets say price was cycling in a perfect sine wave and you had your regression set to the exact number of bars in the cycle. What would happen? The slope (m) would stay constant ... zero and the correlation coefficient r^2 would stay the same something less than 1 meanwhile price would be swinging above and below the line defined by the regression equation. Potentially profitable trades would be missed.
Another statistical method I like better is Pearson's Rank Correlation. It makes for a very smooth yet responsive momentum measure. To each his/her own.