News impact on stock price return via sentiment analysis

By Xiaodong Li, Haoran Xie, Li Chen, Jianping Wang, Xiaotie Deng. 2014

487 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Erik Cambria
2. Philip S. Yu
3. Frank Xing
4. Roy E. Welsch
5. Jiang Bian
6. Tie-Yan Liu
7. Yuntao Yao
8. Weiqing Liu

In Finance

1. Yuntao Yao
2. Frank Xing
3. Philip S. Yu
4. Olivier Kraaijeveld
5. B. Fang
6. Roy E. Welsch
7. Yunjia Zhang
8. J. Smedt

In Management Science and Operations Research

1. Yuntao Yao
2. Frank Xing
3. Philip S. Yu
4. Jiang Bian
5. Olivier Kraaijeveld
6. Tie-Yan Liu
7. B. Fang
8. Roy E. Welsch

Citations in the corpus

• Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “Li et al. [16] implements a generic stock price prediction framework using sentiment analysis.”
• Improving stock market prediction via heterogeneous information fusion (2017, background): “One main data source is the news articles [10, 11, 23, 24, 25].”
• Natural language based financial forecasting: a survey (2017, background): “The predictability of Financial News: It seems that most previous studies have confirmed the correlation between public mood and the movement of the market, for instance, [68,49].”
• Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks (2016, unknown): “[3] [4],[5],[6] researched financial textual data with SVM.”
• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, unknown): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
• The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, methodology): “…recent studies, researchers and investors have increasingly acknowledged the power of Natural Language Processing (NLP) and text mining approaches to extract sentiment from news, as shown with the Thomson Reuters MarketPsych Indices (TRMI) or in the works of Mao et al. (2011) and Li et al. (2014).”
• Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model (2016, background): “Both investors and speculators in the market would like to make better profits by analyzing market information [4].”