Incorporating Transformers and Attention Networks for Stock Movement Prediction

Yawei Li, Shuqi Lv, Xinghua Liu, Qiuyue Zhang and Siew Ann Cheong

Complexity, 2022, vol. 2022, 1-10

Abstract: Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2022/7739087.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2022/7739087.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:7739087

DOI: 10.1155/2022/7739087

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().