Deep learning for event-driven stock prediction
- Economics and Econometrics
- Electrical and Electronic Engineering
- Management Science and Operations Research
- Journal
- International Conference on Artificial Intelligence
In The Last Decade
doi.org/w11704944 →Countries where authors are citing Deep learning for event-driven stock prediction
This map shows the geographic impact of Deep learning for event-driven stock prediction. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep learning for event-driven stock prediction with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep learning for event-driven stock prediction more than expected).
Fields of papers citing Deep learning for event-driven stock prediction
This network shows the impact of Deep learning for event-driven stock prediction. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep learning for event-driven stock prediction.
About Deep learning for event-driven stock prediction
This paper, published in 2015, received 344 indexed citations . Written by Xiao Ding, Yue Zhang, Ting Liu and Junwen Duan covering the research area of Economics and Econometrics, Electrical and Electronic Engineering and Management Science and Operations Research. It is primarily cited by scholars working on Management Science and Operations Research (265 citations), Artificial Intelligence (120 citations) and Finance (106 citations). Published in International Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w11704944.