More data = more noise?
It is well-known, particularly in econometric circles, that throwing more data at a problem doesn't necessarily improve your chances of successfully predicting an outcome, and will frequently have the opposite effect.
In a nutshell, by funnelling additional information into a predictive scheme you may well just be adding useless noise. This is clear, for example, in linear regression modelling. If you throw in a new variable as a regressor you now have more model parameters to estimate, and the estimation accuracy (of all parameters) suffers. This will frequently (but not always) result in larger residual errors and thus poorer predictive performance.
In financial prediction, knowing what to throw into the mix is absolutely crucial. There is a large statistical literature on the subject, but in practice there is also a large element of voodoo.
Biting the hand that feeds IT
