Please wait a minute...
Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2020.0063
Current Issue | Archive | Adv Search |
Research on the Relationship between Heterogeneous of Financial News and Stock Market
Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong
(Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China)
(Big Data Institute, Wuhan University, Wuhan 430072, China)
(Institute of Finance and economics, Shanghai University of Finance and Economics,Shanghai 200433, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper discussed the relationship between different types of financial news and stock market by classifying financial news. [Methods] We used Word2vec +k-means to cluster news texts, and VAR model is used to analyze how different types of news affect the stock market and how the change of stock market reacted to news from the time dimension. [Results] The news emotion effect and information effect under different categories can significantly affect the trading volume, amplitude and return of the stock market, but the three categories of news have different emphasis on the impact on the stock market. The stock market yield and trading volume reflect the emotional differences and news length respectively, but are still affected by the news category. [Limitations] We analyzed the relationship between stock market and news from the perspective of the whole stock market without considering the differences between stock.  [Conclusions] There is an interaction mechanism between news and stock market, and there is a time lag effect. News category is a key variable in the interaction between the two.

Key words heterogeneous news      VAR      stock market      sentiment analysis      
Published: 04 September 2020
ZTFLH:  F832.5,G35  

Cite this article:

Lv Huakui, Liu Zhenghao, Qian Yuxing, Hong Xudong. Research on the Relationship between Heterogeneous of Financial News and Stock Market . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2020.0063     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Sun Yu, Qiu Jiangnan. Studying Opinion Leaders with Network Analysis and Text Mining[J]. 数据分析与知识发现, 2022, 6(1): 69-79.
[2] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[3] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[4] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[5] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[6] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[7] Yu Bengong, Zhang Shuwen. Aspect-Level Sentiment Analysis Based on BAGCNN[J]. 数据分析与知识发现, 2021, 5(12): 37-47.
[8] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[9] Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong. Relationship Between Financial News and Stock Market Fluctuations[J]. 数据分析与知识发现, 2021, 5(1): 99-111.
[10] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[11] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[12] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[13] Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[14] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
[15] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn