I'm using SVM binary classification to predict movement of NASDAQ stock prices. My question is regarding cross-validation. I will divide the training data into V subsets. Training will be performed on (V-1) subsets and then prediction on the V-th subset is recorded. This will be done V times.
(1) Is the best measure of accuracy the average of all V outcomes? Or perhaps the minimum?
(2) Should subsets be equal length? Random?
(2a) Can subsets overlap?
(3) Do I need to ensure that each subset has at least some minimum percentage of each class outcome (1 or 0) for the results to be valid?