{"type":"ScholarlyArticle","title":"Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction","authors":[{"name":"Wei Li","slug":"wei-li","given":"Wei","family":"Li","affiliation":"MOE Key Lab of Computational Linguistics , School of EECS , Peking University"},{"name":"Ruihan Bao","slug":"ruihan-bao","given":"Ruihan","family":"Bao","affiliation":"Mizuho Securities Co. , Ltd"},{"name":"Keiko Harimoto","slug":"keiko-harimoto","given":"Keiko","family":"Harimoto","affiliation":"Mizuho Securities Co. , Ltd"},{"name":"Deli Chen","slug":"deli-chen","given":"Deli","family":"Chen","affiliation":"MOE Key Lab of Computational Linguistics , School of EECS , Peking University"},{"name":"Jingjing Xu","slug":"jingjing-xu","given":"Jingjing","family":"Xu","affiliation":"MOE Key Lab of Computational Linguistics , School of EECS , Peking University"},{"name":"Qi Su","slug":"qi-su","given":"Qi","family":"Su","affiliation":"MOE Key Lab of Computational Linguistics , School of EECS , Peking University"}],"abstract":"Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price.\r\nAs no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement.\r\nOne big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News.\r\nAnother challenge is that the stocks in the market are not independent, which is omitted by previous works.\r\nTo make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. \r\nExtensive experiment results show that our model outperforms the  baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.","doi":"10.24963/ijcai.2020/626","url":"https://ijcai.org/proceedings/2020/626","pdf":"https://ijcai.org/proceedings/2020/0626.pdf","markdown":"https://ijcai.org/proceedings/2020/626.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/626","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":626,"pages":{"first":4541,"last":4547},"keywords":["AI for trading: General","AI for trading: AI for predictive trading"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/23991/file_23991.m3u8","length_sec":279,"provider":"hls"}],"references":[{"position":1,"raw":"[Bollen et al., 2011] Johan Bollen, Huina Mao, and Xiao- Jun Zeng. 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