Abstract
“Stock movements are influenced not only by historical prices, but also by information outside the market such as social media and news about the stock or related stock. In practice, news or prices of a stock in one day are normally impacted by different days with different weights, and they can influence each other. In terms of this issue, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. First, we propose three new technical indicators based on raw prices according to the finance theory as the basic encode of the prices of each day. Then, we introduce a coattention mechanism to capture the sufficient context information between text and prices across every day within a time window. Based on the mutual promotion and influence of text and price at different times, we obtain more sufficient stock representation. We perform extensive experiments on the real-world StockNet dataset and the experimental results demonstrate the effectiveness of our method.”- Anthology ID:
- 2022.ccl-1.86
- Volume:
- Proceedings of the 21st Chinese National Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Nanchang, China
- Editors:
- Maosong Sun (孙茂松), Yang Liu (刘洋), Wanxiang Che (车万翔), Yang Feng (冯洋), Xipeng Qiu (邱锡鹏), Gaoqi Rao (饶高琦), Yubo Chen (陈玉博)
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 973–984
- Language:
- English
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.ccl-1.86/
- DOI:
- Cite (ACL):
- Zheng Yangjia, Li Xia, Ma Junteng, and Chen Yuan. 2022. Fundamental Analysis based Neural Network for Stock Movement Prediction. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 973–984, Nanchang, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Fundamental Analysis based Neural Network for Stock Movement Prediction (Yangjia et al., CCL 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.ccl-1.86.pdf
- Data
- StockNet