Replace missing values of numerical features with unique numbers
This depends on the exact learning algorithm which is used, but most of the time numerical values are treated... numerically. 

It's easy to see with the case of linear regression: let's say a feature has values between 0 and 100, but with a few missing values. Now replace missing values with -999: the coefficient learned for this feature will be completely different and based on  meaningless indications, so the performance will decrease.

This idea might work in the case where the numerical values are 'binned' into several classes, i.e. transformed into ordinal/categorical variables.

But the best way to use such values is to use an algorithm and framework which can deal with missing values in the first place.