Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2022.2106271
A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction
Duo Xu
Zeshui Xu
Shuixia Chen
Hamido Fujita
Sažetak
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%.
Ključne riječi
Stock market prediction; deep learning; temporal causal convolution; crossresidual learning; information fusion
Hrčak ID:
306619
URI
Datum izdavanja:
30.4.2023.
Posjeta: 132 *