I am trying to build a machine learning system for financial price prediction. I am using a 3 layer MLP (a deep network) with 3 outputs (buy,hold,sell).
I am using different features such as price and volume. In order to take into account past behavior, I concatenate the price and volume data into one vector. The first part of the vector is the last N return rate, the second part is the last N transaction volume.
This results in a large input vector (>50). I wonder if the bad results I get are related to the current selection of the input vector.
Any help on that?