An intraday market risk management approach based on textual analysis

By Sven S. Groth, Jan Muntermann. 2011

175 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. Frank Xing
2. Vadlamani Ravi
3. Teh Ying Wah
4. Arman Khadjeh Nassirtoussi
5. David Chek Ling Ngo
6. Saeed Reza Aghabozorgi
7. Roy E. Welsch
8. Erik Cambria

In Finance

1. Frank Xing
2. Vadlamani Ravi
3. B. Shravan Kumar
4. Teh Ying Wah
5. Arman Khadjeh Nassirtoussi
6. David Chek Ling Ngo
7. Saeed Reza Aghabozorgi
8. Roy E. Welsch

In Management Science and Operations Research

1. Vadlamani Ravi
2. B. Shravan Kumar
3. Erik Cambria
4. Frank Xing
5. Roy E. Welsch
6. Teh Ying Wah
7. Arman Khadjeh Nassirtoussi
8. David Chek Ling Ngo

Citations in the corpus

• Decision support from financial disclosures with deep neural networks and transfer learning (2017, neutral): “They have shown a strong influence on financial markets and have been a popular choice in previous research [24, 25].”
• Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “…Binary, Dictionary value Huang et al. (2010) Simultaneous terms, ordered pairs Synonyms replacement Weighted based on the rise/fall ratio of index Groth and Muntermann (2011) Bag-of-words Feature scoring methods using both Information Gain and Chi-Squared metrics TF-IDF Schumaker et al. (2012)…”
• 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”
• A survey of the applications of text mining in financial domain (2016, background): “(2013) Corporate News SVM, SVR Up/ Down Schumaker et al. (2012) US Financial News SVR Error Rate Groth and Muntermann (2011) German ad hoc Announcements NB, k-NN, SVM, NN Up/ Down Bollen et al. (2010) Tweets SOFNN Error Rate Huang et al. (2010) Financial News Weighted AR Up/ Down Li (2010) The US…”
• Text mining for market prediction: A systematic review (2014, methodology): “Top 30 concepts Binary, TF-IDF Rachlin et al. (2007) Bag-of-words, commonly used financial values Most influential keywords list (Automatic extraction) TF, Boolean, Extractor software output Tetlock et al. (2008) Bag-of-words for negative words Pre-defined dictionary.”