A representation learning framework for stock movement prediction

The learning of high-quality stock representations is one of the keys to predicting stock movements effectively. Current studies have been negatively impacted by stochasticity in stock prices, resulting in inadequate representation learnt by the models. We present an end-to-end stock movement prediction framework (CLSR) utilizing contrastive learning to exploit the correlation between intra-day data and enhance stock representation in order to improve the accuracy of stock movement prediction. In addition, a hybrid encoding network is developed to extract long-range dependencies and local contextual features in stock data, making the feature representation more complete. To further improve the prediction accuracy of the model, historical state information is added to the intra-day stock data. Our experiments on CSI-500 show that the proposed method outperforms state-of-the-art solutions. The proposed method is also validated by analyzing the representation space thoroughly.