There are many negative results in clinical medicine. For example, all drugs that don't work in a phase III trial deserve their own publication. This is a costly failure for pharma, but less costly than failing post-marketing and being sued by everyone.
Anyway, the term negative results is rather vague. A negative result coming from a well-designed and powered experiment can be very exciting (say, not finding the Higgs boson despite adequate design) because it makes us reconsider current theories. In my domain, for example, showing beyond reasonable doubt that smoking does NOT cause cancer would be a result of profound significance in preventive medicine. This kind of negative result is interesting, but rare. On the other hand, most of the time when a result goes against a very well established theory, the method is probably flawed, or underpowered or the interpretation is incomplete. This is the frequent kind of negative result, the one that most PhD's fear. There is yet another kind of negative result, also frustrating, when your new code/algorithm proves to be inferior to the competition. At least in this case you do contribute something new that might be of use in specific circumstances or in designing a better version in the future.
So, what I'm saying is that most of the time unexpected negative results come from bad methodology, which is why everyone hates them. True negative results are great but require extreme rigor and luck.