  In recent years, machine learning and deep learning have become popular
methods for financial data analysis, including financial textual data,
numerical data, and graphical data. This paper proposes to use sentiment
analysis to extract useful information from multiple textual data sources and a
blending ensemble deep learning model to predict future stock movement. The
blending ensemble model contains two levels. The first level contains two
Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and
one Gated Recurrent Units network (GRU), followed by a fully connected neural
network as the second level model. The RNNs, LSTM, and GRU models can
effectively capture the time-series events in the input data, and the fully
connected neural network is used to ensemble several individual prediction
results to further improve the prediction accuracy. The purpose of this work is
to explain our design philosophy and show that ensemble deep learning
technologies can truly predict future stock price trends more effectively and
can better assist investors in making the right investment decision than other
traditional methods.
