Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

This paper proposes Simultaneous Trainings of Identical Neural Networks (STNN) that aims to predict when sufficient data is not available for training neural networks (NN). While predictive applications of neural networks are growing, a common assumption in the NN algorithms is to have a...