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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option.<\/jats:p>\n          <jats:p>Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered.<\/jats:p>\n          <jats:p>To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance.<\/jats:p>","DOI":"10.1145\/3611311","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T03:53:20Z","timestamp":1690516400000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Dynamic Attributes-driven Graph Attention Network Modeling on Behavioral Finance for Stock Prediction"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7229-7200","authenticated-orcid":false,"given":"Qiuyue","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Management Science and Engineering, Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1237-6035","authenticated-orcid":false,"given":"Yunfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9184-6540","authenticated-orcid":false,"given":"Xunxiang","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2174-7889","authenticated-orcid":false,"given":"Shilong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Insurance, Shandong University of Finance and Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6365-6221","authenticated-orcid":false,"given":"Caiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5048-8145","authenticated-orcid":false,"given":"Peide","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Shandong University of Finance and Economics, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2004.04.003"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102452"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269269"},{"key":"e_1_3_2_5_2","first-page":"55","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 33rd Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The 11th Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event","author":"Cheng Rui","year":"2021","unstructured":"Rui Cheng and Qing Li. 2021. 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