A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
This work addresses stock market forecasting for investors and analysts by improving prediction accuracy through better news representation, though it is incremental as it builds on existing embedding and LSTM methods.
The authors tackled stock market prediction by developing a Distributed Representation of News (DRNews) model to create news vectors with contextual and cross-documental information, and applied it in attention-based LSTM networks for short-term movement prediction and crises early warning, resulting in substantial enhancements over five baseline models.
In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.