  It is a challenging task to predict financial markets. The complexity of this
task is mainly due to the interaction between financial markets and market
participants, who are not able to keep rational all the time, and often
affected by emotions such as fear and ecstasy. Based on the state-of-the-art
approach particularly for financial market predictions, a hybrid convolutional
LSTM Based variational sequence-to-sequence model with attention (CLVSA), we
propose a novel deep learning approach, named dual-CLVSA, to predict financial
market movement with both trading data and the corresponding social sentiment
measurements, each through a separate sequence-to-sequence channel. We evaluate
the performance of our approach with backtesting on historical trading data of
SPDR SP 500 Trust ETF over eight years. The experiment results show that
dual-CLVSA can effectively fuse the two types of data, and verify that
sentiment measurements are not only informative for financial market
predictions, but they also contain extra profitable features to boost the
performance of our predicting system.
