In binary classification, are there criteria or guidelines available to judge if classification performance of the testset (unseen data) is poor, medium or high? 

I realise that this may depend on the research context, so perhaps there is not a straight answer to my question. For example, in medicine high classification performance may be critical for patient care while in stock markets lower classification performance may be acceptable if that means financial gain. However, if looking at measures of sensitivity, specificity, accuracy (and positive and negative predictive value as well), one wants these values to be as high as possible for the testset. Or otherwise, should the area under the curve of the ROC surpass a certain threshold to be considered sufficient (for ex. 0.8).

My question is: Are there general guidelines available which levels are acceptable or does this depend entirely on the research topic one is working on?