  We have proposed to develop a global hybrid deep learning framework to
predict the daily prices in the stock market. With representation learning, we
derived an embedding called Stock2Vec, which gives us insight for the
relationship among different stocks, while the temporal convolutional layers
are used for automatically capturing effective temporal patterns both within
and across series. Evaluated on S&P 500, our hybrid framework integrates both
advantages and achieves better performance on the stock price prediction task
than several popular benchmarked models.
