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.

It builds word vectors to represent word meanings. And it learns these meanings solely by the surrounding words. You can then use these word vectors as the input to make machine learning algorithms perform better and find interesting abstractions. What happens if we apply Word2Vec to the stock market? In Word2Vec the window for each word is the surrounding words.