37,439 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
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction
Stock market movement prediction remains challenging due to
random walk characteristics. Yet through a potent blend of input
parameters, a prediction model can learn sequential features more
intelligently. In this paper, a multi-channel news-oriented prediction
system is developed to capture intricate moving patterns of
the stock market index. Specifically, the system adopts the temporal
causal convolution to process historical index values due to
its capability in learning long-term dependencies. Concurrently, it
employs the Transformer Encoder for qualitative information
extraction from financial news headlines and corresponding preview
texts. A notable configuration to our multi-channel system is
an integration of cross-residual learning between different channels,
thereby allowing an earlier and closer information fusion. The
proposed architecture is validated to be more efficient in trend
forecasting compared to independent learning, by which channels
are trained separately. Furthermore, we also demonstrate the
effectiveness of involving news content previews, improving the
prediction accuracy by as much as 3.39%
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