Feature scaling data with missing values
Well -9999 can matter more or less depending on the variability within your data (is -9999 an extreme outlier value, or a moderate value somewhat close to the mean/median>)

Depends a lot on your data.  A value like that can skew it a lot, and can affect modeling quite a bit depending on the technique you use.  Are the NA's biased for a class?  Consider decision trees that classify on optimal splits.  They would detect a bias (e.g. if 90% of your NA's a certain class) and split at -9999 value to manifest that bias.

also check out Pareto scaling, which emphasizes small to medium changes in your data.  Might be along the lines you are looking for.