Forecasting Financial Markets using Deep Learning

Abstract

Forecasting the behavior of financial markets represents an area of interest for many traders and investors due to the potential increase of capital which an accurate forecast can provide. The main objective of this paper is to predict the market behavior using Deep Learning techniques. We propose a stacked LSTM (Long Short-Term Memory) architecture on which we conducted several experiments on cryptocurrency and forex datasets. Our study reveals that in the context of financial markets, a high accuracy of a forecasted asset does not imply that the forecasted value will contribute positively to a profitable trading system.