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A Tensor-based eLSTM Model to Predict Stock Price Using Financial News

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Item Summary Tan, Jinghua Wang, Jun Rinprasertmeechai, Denisa Xing, Rong Li, Qing 2019-01-02T23:55:18Z 2019-01-02T23:55:18Z 2019-01-08
dc.identifier.isbn 978-0-9981331-2-6
dc.description.abstract Stock market prediction has attracted much attention from both academia and business. Both traditional finance and behavioral finance believe that market information affects stock movements. Typically, market information consists of fundamentals and news information. To study how information shapes stock markets, common strategies are to concatenate various information into one compound vector. However, such concatenating ignores the interlinks between fundamentals and news information. In addition, the fundamental data are continuous values sampled at fixed time intervals, while news information occurred randomly. Such heterogeneity leads to miss valuable information partially or twist the feature spaces. In this article, we propose a tensor-based event-LSTM (eLSTM) to solve these two challenges. In particular, we model the market information space with tensors instead of concatenated vectors and balance the heterogeneity of different data types with event-driven mechanism in LSTM. Experiments performed on an entire year data of China Securities markets demonstrate the supreme of the proposed approach over the state-of-the-art algorithms including AZfinText, eMAQT, and TeSIA.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Machine Learning and Network Analytics in Finance
dc.subject Decision Analytics, Mobile Services, and Service Science
dc.subject LSTM, Event-driven, Tensor, Media-awre, Stock movements
dc.title A Tensor-based eLSTM Model to Predict Stock Price Using Financial News
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2019.201
Appears in Collections: Machine Learning and Network Analytics in Finance

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