Stock Trend Prediction using Financial Market News and BERT

Feng Wei, Uyen Trang Nguyen

2020

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.

Paper Citation

in Harvard Style

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) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 325-332. DOI: 10.5220/0010172103250332

in Bibtex Style

@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) - Volume 1: KDIR},
year={2020},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010172103250332},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - Stock Trend Prediction using Financial Market News and BERT
SN - 978-989-758-474-9
AU - Wei F.
AU - Nguyen U.
PY - 2020
SP - 325
EP - 332
DO - 10.5220/0010172103250332
PB - SciTePress