Textual analysis of stock market prediction using breaking financial news: The AZFin text system

By Robert P. Schumaker, Hsinchun Chen. 2009

918 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Natural Language Processing, Time-Series & Forecasting

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. Erik Cambria
2. Frank Xing
3. Roy E. Welsch
4. Qing Li
5. Stefan Feuerriegel
6. Yuanzhu Peter Chen
7. Ping Li
8. Mathias Kraus

In Finance

1. Erik Cambria
2. Frank Xing
3. Roy E. Welsch
4. Mathias Kraus
5. Stefan Feuerriegel
6. Qing Li
7. Yuanzhu Peter Chen
8. Ping Li

In Management Science and Operations Research

1. Weiwei Jiang
2. Stefan Feuerriegel
3. Erik Cambria
4. Frank Xing
5. Roy E. Welsch
6. Vadlamani Ravi
7. M. Ugur Gudelek
8. Omer Berat Sezer

In Natural Language Processing

1. Erik Cambria
2. Frank Xing
3. Roy E. Welsch
4. Qing Li
5. Stefan Feuerriegel
6. Ping Li
7. Xiaodong Li
8. Yuanzhu Peter Chen

In Time-Series & Forecasting

1. Stefan Feuerriegel
2. Vadlamani Ravi
3. B. Shravan Kumar
4. Qing Li
5. M. Ugur Gudelek
6. Omer Berat Sezer
7. A. Ozbayoglu
8. Ping Li

Citations in the corpus

• Improving stock market prediction via heterogeneous information fusion (2017, background): “One main data source is the news articles [10, 11, 23, 24, 25].”
• Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “The components are adapted from the systems described in [12, 8, 9, 10, 11].”
• News events prediction using Markov logic networks (2018, background): “Stock market prediction [17] poses a similar problem.”
• Can Online Emotions Predict the Stock Market in China? (2016, background): “Instead of emotion on social media [11], some researchers examined textual representations in financial news articles for stock prediction [5,15].”
• 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”
• The effect of news and public mood on stock movements (2014, methodology): “Subsequent research [34,35] has adopted this window in their stock prediction studies.”
• Operational Transparency on Crowdfunding Platforms: Effect on Donations for Emergency Response (2019, neutral): “…analyzed user-generated content to measure the financial health of firms and markets (Antweiler and Frank 2004, Li 2010, Loughran and Mcdonald 2011, Schumaker and Chen 2009, Tetlock et al. 2008), to forecast customer preferences and choice (Cao et al. 2011, Decker and Trusov 2010, Li and Wu 2010,…”
• A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis (2017, positive): “Authors in (Schumaker and Chen, 2009) use the bag-of-words and named entities for predicting stock market.”