Nononsense, I once had a fairly accurate (and probably totally spurious) linear estimator of the S&P 500 based on the yield, the 90 day T-Bill rate, and M3 (you remember what THAT was, haha!). I got there by correlating the S&P with every bit of data I could find, like the size of the Ukrainian potato harvest and the price of rubbers at the corner gas station. Only the aforementioned three had convincingly high correlation coefficients.
So when I got interested in NN's (any correlation THERE?), the first thing I read is that they run cross-correlations on everything in sight to figure out what to plug into the NN. After I picked myself up off of floor and changed my shorts, I kept reading. Oh! To first approximation it's a linear equation. Duh! Oh! It might go nonlinear. Uh huh. Finally, it might become multiplicative. Oh, yeah, the price of rubbers times the potato harvest. Bye bye!
Nowadays anyone who can research and READ (excluding most of ET) can get the answer from Katz and McCormick's awfully-titled "The Encyclopedia of Trading Strategies'. It ain't a pretty conclusion, glad I stuck with Kalman filtering. Joe.