English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we propose AGKN (attention-based graph learning kernel network), a novel framework to incorporate information of correlated firms of a target stock for its price prediction in an end-to-end way. We first construct a stock-axis attention module to extract dynamic and asymmetric spatial correlations through the kernel method and a graph learning module into which more accurate information can be integrated. An ensemble time-axis attention module is then applied to learn temporal correlations within each stock and market index. Finally, we utilize a transformer encoder to jointly attend to obtain information from different levels for correlations’ aggregation and prediction. Experiments with data collected from the Chinese stock market show that AGKN outperforms state-of-the-art baseline methods, making up to 4.3% lower error than the best competitors. The ablation study shows that AGKN pays more attention to hidden correlation between stocks, which improves model’s performance greatly.

Using Kernel Method to Include Firm Correlation for Stock Price Prediction