@inproceedings{jain-agrawal-2024-fb-gan,
title = "{FB}-{GAN}: A Novel Neural Sentiment-Enhanced Model for Stock Price Prediction",
author = "Jain, Jainendra Kumar and
Agrawal, Ruchit",
editor = "Chen, Chung-Chi and
Liu, Xiaomo and
Hahn, Udo and
Nourbakhsh, Armineh and
Ma, Zhiqiang and
Smiley, Charese and
Hoste, Veronique and
Das, Sanjiv Ranjan and
Li, Manling and
Ghassemi, Mohammad and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.finnlp-1.9",
pages = "85--93",
abstract = "Predicting stock prices remains a significant challenge in financial markets. This study explores existing stock price prediction systems, identifies their strengths and weaknesses, and proposes a novel method for stock price prediction that leverages a state-of-the-art neural network framework, combining the BERT language model for sentiment analysis on news articles and the GAN model for stock price prediction. We introduce the FB-GAN model, an ensemble model that leverages stock price history and market sentiment score for more accurate stock price prediction and propose effective strategies to capture the market sentiment. We conduct experiments on stock price prediction for five major equities (Amazon, Apple, Microsoft, Nvidia, and Adobe), and compare the performance obtained by our proposed model against the existing state-of-the-art baseline model. The results demonstrate that our proposed model outperforms existing models across the five major equities. We demonstrate that the strategic incorporation of market sentiment using both headlines as well summaries of news articles significantly enhances the accuracy and robustness of stock price prediction.",
}
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%0 Conference Proceedings
%T FB-GAN: A Novel Neural Sentiment-Enhanced Model for Stock Price Prediction
%A Jain, Jainendra Kumar
%A Agrawal, Ruchit
%Y Chen, Chung-Chi
%Y Liu, Xiaomo
%Y Hahn, Udo
%Y Nourbakhsh, Armineh
%Y Ma, Zhiqiang
%Y Smiley, Charese
%Y Hoste, Veronique
%Y Das, Sanjiv Ranjan
%Y Li, Manling
%Y Ghassemi, Mohammad
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F jain-agrawal-2024-fb-gan
%X Predicting stock prices remains a significant challenge in financial markets. This study explores existing stock price prediction systems, identifies their strengths and weaknesses, and proposes a novel method for stock price prediction that leverages a state-of-the-art neural network framework, combining the BERT language model for sentiment analysis on news articles and the GAN model for stock price prediction. We introduce the FB-GAN model, an ensemble model that leverages stock price history and market sentiment score for more accurate stock price prediction and propose effective strategies to capture the market sentiment. We conduct experiments on stock price prediction for five major equities (Amazon, Apple, Microsoft, Nvidia, and Adobe), and compare the performance obtained by our proposed model against the existing state-of-the-art baseline model. The results demonstrate that our proposed model outperforms existing models across the five major equities. We demonstrate that the strategic incorporation of market sentiment using both headlines as well summaries of news articles significantly enhances the accuracy and robustness of stock price prediction.
%U https://aclanthology.org/2024.finnlp-1.9
%P 85-93
Markdown (Informal)
[FB-GAN: A Novel Neural Sentiment-Enhanced Model for Stock Price Prediction](https://aclanthology.org/2024.finnlp-1.9) (Jain & Agrawal, FinNLP-WS 2024)
ACL
- Jainendra Kumar Jain and Ruchit Agrawal. 2024. FB-GAN: A Novel Neural Sentiment-Enhanced Model for Stock Price Prediction. In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing @ LREC-COLING 2024, pages 85–93, Torino, Italia. ELRA and ICCL.