19 research outputs found
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
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.Comment: 25 page
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
We consider direct modeling of underlying stock value movement sequences over
time in the news-driven stock movement prediction. A recurrent state transition
model is constructed, which better captures a gradual process of stock movement
continuously by modeling the correlation between past and future price
movements. By separating the effects of news and noise, a noisy random factor
is also explicitly fitted based on the recurrent states. Results show that the
proposed model outperforms strong baselines. Thanks to the use of attention
over news events, our model is also more explainable. To our knowledge, we are
the first to explicitly model both events and noise over a fundamental stock
value state for news-driven stock movement prediction.Comment: 12 page
ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors
We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-specific financial news events detection classifiers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to filter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classifiers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-specific financial events detection task. Besides, we create a domain-specific events synonyms dictionary