A graph-based approach to multi-source heterogeneous information fusion in stock market.
The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technolo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0272083 |
| _version_ | 1811188792015454208 |
|---|---|
| author | Jun Wang Xiaohan Li Huading Jia Tao Peng |
| author_facet | Jun Wang Xiaohan Li Huading Jia Tao Peng |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations. |
| first_indexed | 2024-04-11T14:24:10Z |
| format | Article |
| id | doaj.art-321059e66a054d9bbc56d0578cc9892d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T14:24:10Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-321059e66a054d9bbc56d0578cc9892d2022-12-22T04:18:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027208310.1371/journal.pone.0272083A graph-based approach to multi-source heterogeneous information fusion in stock market.Jun WangXiaohan LiHuading JiaTao PengThe stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations.https://doi.org/10.1371/journal.pone.0272083 |
| spellingShingle | Jun Wang Xiaohan Li Huading Jia Tao Peng A graph-based approach to multi-source heterogeneous information fusion in stock market. PLoS ONE |
| title | A graph-based approach to multi-source heterogeneous information fusion in stock market. |
| title_full | A graph-based approach to multi-source heterogeneous information fusion in stock market. |
| title_fullStr | A graph-based approach to multi-source heterogeneous information fusion in stock market. |
| title_full_unstemmed | A graph-based approach to multi-source heterogeneous information fusion in stock market. |
| title_short | A graph-based approach to multi-source heterogeneous information fusion in stock market. |
| title_sort | graph based approach to multi source heterogeneous information fusion in stock market |
| url | https://doi.org/10.1371/journal.pone.0272083 |
| work_keys_str_mv | AT junwang agraphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT xiaohanli agraphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT huadingjia agraphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT taopeng agraphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT junwang graphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT xiaohanli graphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT huadingjia graphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket AT taopeng graphbasedapproachtomultisourceheterogeneousinformationfusioninstockmarket |