I know this probably is a very newbie question, but I haven't been able to find anything about it elsewhere.
I'm running some OLS regressions on high frequency data of stocks in order to model some imbalances, all done in R and checked with Excel.
But, in my tests, I'm constantly seeing that the residuals of the regressions yield far better results than the regular dependent variable (Y) would when used in the OPOSITE of the original logic. $$ residue < lower threshold (negative int), then, BUY $$ regular meta code would be: $$ \hat y > upper threshold (positive int), then, BUY $$
I tried this because of a mistake in the beginning of my coding and later found out it was a far better predictor in the out of sample analysis I ran. So I kept up testing and this kept showing up in the results.
The possible explanation I thought about this is that, if the residue is large enough, I should expect that the contrary of it to happen next. Like a mean reversal logic of the error. If the error is large enough, it will probably return to the mean, so I use it as the "- (Y hat)"
Is there some kind of explanation to this, is this some kind of statistical technique that I just don't know? Or I'm am I just delusional and this makes no sense at all, using the residuals as a predictor for my model?
Thank in advance
EDIT : What is the expected correlation between residual and the dependent variable? talks about this kind of comparison between residuals and Y.. Not much value in it.
I'll post the other scatter plots of residuals against fitted values and Y against fitted values.