How do you define accuracy? 72% is something I would die for as it is 72% : 38%, almost 2:1 odds. 
I had tried using all sorts of ML and neural networks (CNN, LSTM combos with autoencoder) in financial data. The problem is the bias of the training data. If the training set itself has 70% positive results, the ML algorithm just need to be 50% accurate to yield 70% true positive response. A proper testing framework is very much essential for this. My best algos give about 60-63% accuracy.