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We have proposed to develop a global hybrid deep learning framework topredict the daily prices in the stock market. With representation learning, wederived an embedding called Stock2Vec, which gives us insight for therelationship among different stocks, while the temporal convolutional layersare used for automatically capturing effective temporal patterns both withinand across series. Evaluated on S&P 500, our hybrid framework integrates bothadvantages and achieves better performance on the stock price prediction taskthan several popular benchmarked models.

Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network