2 papers
cs.LG2019
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Michael Poli, Jinkyoo Park, Ilija Ilievski
Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwa…
q-fin.GN2019
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
Jinho Lee, Raehyun Kim, Yookyung Koh +1
We applied Deep Q-Network with a Convolutional Neural Network function approximator, which takes stock chart images as input, for making global stock market predictions. Our model…