Support Vector classifier perform well with input features rather than transformed features in contrast to ANN-BP, random forest (other classifiers) I am working on stock data with 5 raw features (OHLCV). Using few transformations used by technical analysts, have created 20 more features giving different kinds of indications. 
When trying to predict the direction of the next few days, the behaviour shown by SVM is opposite to other classifiers. (SVM is giving better results when 5 raw inputs are considered while other methods (as expected) give better results when generated features are being used)

I completely understand that market data is very random and noisy. This is a theoretical exercise being done to understand the behaviours of different ML methods.
Thanks for the answer and it completely makes sense that results will be driven by data set in question!!

Is there any theory that says that SVM gives better results as compared to other ML algos like NN and RF when features are lower in number?
Another reason that I can think the reason for this behaviour is attributed to the information loss during transformations which were done.

Though SVMs results almost remained the same but for other algorithms improvement was significant(8-10%). Means transformed data showed patterns that RF and NN were able to recognize and do the classification while it hardly affected SVM outputs.