The effect of news and public mood on stock movements

By Qing Li, Tiejun Wang, Ping Li, Ling Liu, Qixu Gong, Yuanzhu Peter Chen. 2014

251 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Qing Li
2. Yuanzhu Peter Chen
3. Ping Li
4. Erik Cambria
5. Frank Xing
6. Roy E. Welsch
7. LiLing Jiang
8. Enrique Herrera-Viedma

In Finance

1. Erik Cambria
2. Frank Xing
3. Roy E. Welsch
4. Qing Li
5. Yuanzhu Peter Chen
6. Ping Li
7. LiLing Jiang
8. Yuntao Yao

In Management Science and Operations Research

1. Erik Cambria
2. Frank Xing
3. Roy E. Welsch
4. Qing Li
5. Yuanzhu Peter Chen
6. Ping Li
7. LiLing Jiang
8. Philip S. Yu

Citations in the corpus

• Improving stock market prediction via heterogeneous information fusion (2017, background): “How news articles affect the trading activities is studied in [33], and the sentiment impacts are also testified.”
• A Tensor-Based Information Framework for Predicting the Stock Market (2016, methodology): “A randomization test [Lavrenko et al. 2000; Li et al. 2014a] was conducted to determine if the investment income of the TeSIA algorithm was statistically significant.”
• Natural language based financial forecasting: a survey (2017, methodology): “[124] Number of tuples occurrences Naı̈ve Bayes & Association Rules Experimentally tuned k-NN [59] Trend possibility distribution n-gram Language model Conditional probability maximization [40] Tf-idf weighted key words Support Vector Machine Split-and-merge segmentation [2] Text classification Regr”
• Sentiment analysis: A review and comparative analysis of web services (2015, methodology): “The previous subsections have shown the effectiveness of the different tools using standard collections; nonetheless, in other applications, tools for evaluating sentiments have been used to address real problems, for example, estimating votes in elections through Tweets [89], recommending products ”
• Tensor-Based Learning for Predicting Stock Movements (2015, unknown): “Recent studies show that news articles play an important role in short-term stock movements (Fama and French 1993; Li et al. 2014a).”
• Deep learning for financial sentiment analysis on finance news providers (2016, background): “[1] Therefore, in this study we assume that those investors, who only receive information from one source, will still have different efficiencies of transferring into investment decisions due to different influence levels.”