Leveraging BERT to Improve the FEARS Index for Stock Forecasting
|Title:||Leveraging BERT to Improve the FEARS Index for Stock Forecasting||Authors:||Yang, Linyi; Xu, Yang; Ng, James; Dong, Ruihai||Permanent link:||http://hdl.handle.net/10197/11363||Date:||12-Aug-2019||Online since:||2020-05-05T13:45:10Z||Abstract:||Financial and Economic Attitudes Revealed by Search (FEARS) index reflects the attention and sentiment of public investors and is an important factor for predicting stock price return. In this paper, we take into account the semantics of the FEARS search terms by leveraging the Bidirectional Encoder Representations from Transformers (BERT), and further apply a self-attention deep learning model to our refined FEARS seamlessly for stock return prediction. We demonstrate the practical benefits of our approach by comparing to baseline works.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACL||Copyright (published version):||2019 the Authors||Keywords:||Recommender systems||Other versions:||https://www.aclweb.org/anthology/W19-5509/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The First Workshop on Financial Technology and Natural Language Processing, Macao, China, 12 August 2019|
|Appears in Collections:||Insight Research Collection|
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