著者

呂 良誠 許 俊杰 拜 亦名

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会第二種研究会資料 (ISSN:24365556)

巻号頁・発行日

vol.2022, no.FIN-029, pp.39-46, 2022-10-08 (Released:2022-10-01)

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This paper targets to predict overnight stock movement by taking contextualized news and stock information into account, using the Pre-trained Language Model (PLM) that was recently popular in Natural Language Processing (NLP) field. We proposed a model in which, given a piece of news and a stock code, the model can predict its overnight stock movement by utilizing combined news-stock embedding. Such embedding consists of (1) the contextualized embedding that contains the semantics of such a piece of news produced by a language model trained on a set of news and its paired stock movement. (2) The contextualized embedding is produced by a PLM trained on the information of stocks. Moreover, we introduce news augmentation on multiple pieces of news for the input and study its effect, respectively.