{"type":"ScholarlyArticle","title":"``The Squawk Bot'': Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering","authors":[{"name":"Xuan-Hong Dang","slug":"xuan-hong-dang","given":"Xuan-Hong","family":"Dang","affiliation":"IBM T.J. Watson Research , Yorktown Heights , NY 10598 , USA"},{"name":"Syed Yousaf Shah","slug":"syed-yousaf-shah","given":"Syed Yousaf","family":"Shah","affiliation":"IBM T.J. Watson Research , Yorktown Heights , NY 10598 , USA"},{"name":"Petros Zerfos","slug":"petros-zerfos","given":"Petros","family":"Zerfos","affiliation":"IBM T.J. Watson Research , Yorktown Heights , NY 10598 , USA"}],"abstract":"Multimodal analysis that incorporates time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy rather than on understanding the association between the two data modalities. In this work, we address the important problem of automatically dis- covering a small set of top news articles associated with a given time series. Towards this goal, we pro- pose a novel multi-modal neural model called MSIN that jointly learns both the numerical time series and the categorical text articles in order to unearth the correlation between them. Through multiple steps of data interrelation between the two data modalities, MSIN learns to focus on a small subset of text articles that best align with the current performance in the time series. This succinct set is timely discovered and presented as recommended documents for the given time series, offering MSIN as an automated information filtering system. We empirically evaluate its performance on discovering daily top relevant news articles collected from Thomson Reuters for two given stock time series, AAPL and GOOG, over a period of seven consecutive years. The experimental results demonstrate MSIN achieves up to 84.9% and 87.2% respectively in recalling the ground truth articles, superior to SOTA algorithms that rely on conventional attention mechanisms in deep learning.","doi":"10.24963/ijcai.2020/634","url":"https://ijcai.org/proceedings/2020/634","pdf":"https://ijcai.org/proceedings/2020/0634.pdf","markdown":"https://ijcai.org/proceedings/2020/634.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/634","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":634,"pages":{"first":4597,"last":4603},"keywords":["AI for trading: AI for novel financial models","Foundation for AI in FinTech: Intelligent financial recommendation","Foundation for AI in FinTech: Data mining and knowledge discovery for FinTech"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/27024/file_27024.m3u8","length_sec":906,"provider":"hls"}],"references":[{"position":1,"raw":"[Akita et al., 2016] Ryo Akita, Akira Yoshihara, Takashi Mat- subara, and Kuniaki Uehara. 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