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        <title>Fundamental Analysis based Neural Network for Stock Movement Prediction</title>
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            <title>Proceedings of the 21st Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Maosong</namePart>
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    <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.”</abstract>
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%0 Conference Proceedings
%T Fundamental Analysis based Neural Network for Stock Movement Prediction
%A Yangjia, Zheng
%A Xia, Li
%A Junteng, Ma
%A Yuan, Chen
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G English
%F yangjia-etal-2022-fundamental
%X “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.”
%U https://aclanthology.org/2022.ccl-1.86
%P 973-984

Markdown (Informal)

[Fundamental Analysis based Neural Network for Stock Movement Prediction](https://aclanthology.org/2022.ccl-1.86) (Yangjia et al., CCL 2022)

• Fundamental Analysis based Neural Network for Stock Movement Prediction (Yangjia et al., CCL 2022)

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.