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Stock Trend Prediction using Financial Market News and BERT

Topics: Business Intelligence Applications; Context Discovery; Data Analytics; Deep Learning; Feature Selection; Information Extraction; Knowledge Discovery in Databases; Machine Learning; Neural Networks; Pre-Processing and Post-Processing for Data Mining; Statistical Methods; Web Mining

Authors: Feng Wei and Uyen Trang Nguyen

Affiliation: Department of Electrical Engineering and Computer Science, York University, 4700 Keele Street, Toronto, Canada

Keyword(s): Language Model, Information Extraction, Neural Networks, Natural Language Processing, Financial Market, Stock Market, Financial and Business News.

Abstract: Stock market trend prediction is an attractive research topic since successful predictions of the market’s future movement could result in significant profits. Recent advances in language representation such as Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) models have shown success in incorporating a pre-trained transformer language model and fine-tuning operations to improve downstream natural language processing (NLP) systems. In this paper, we apply the popular BERT model to leverage financial market news to predict stock price movements. Experimental results show that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system and existing work.

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Paper citation in several formats:
Wei, F. and Nguyen, U. (2020). Stock Trend Prediction using Financial Market News and BERT. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR; ISBN 978-989-758-474-9; ISSN 2184-3228, SciTePress, pages 325-332. DOI: 10.5220/0010172103250332

@conference{kdir20,
author={Feng Wei. and Uyen Trang Nguyen.},
title={Stock Trend Prediction using Financial Market News and BERT},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR},
year={2020},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010172103250332},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR
TI - Stock Trend Prediction using Financial Market News and BERT
SN - 978-989-758-474-9
IS - 2184-3228
AU - Wei, F.
AU - Nguyen, U.
PY - 2020
SP - 325
EP - 332
DO - 10.5220/0010172103250332
PB - SciTePress

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