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Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods.
Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation
Robert F Engle. 1982 · 1982
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Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev. 1986 · 1986
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Stacked generalization
David H Wolpert. 1992 · 1992
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Support vector regression machines
Harris Drucker, Christopher JC Burges, Linda Kaufman, Alex Smola, Vladimir Vapnik, et al. 1997 · 1997
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Learning the kernel matrix with semidefinite programming
Gert RG Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I Jordan. 2004 · 2004
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Support vector machine applications in computational biology
William Stafford Noble et al. 2004 · 2004
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Predicting risk from financial reports with regression
Shimon Kogan, Dimitry Levin, Bryan R Routledge, Jacob S Sagi, and Noah A Smith. 2009 · 2009
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The information content of forward-looking statements in corporate filings–a naïve bayesian machine learning approach
Feng Li. 2010 · 2010
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Multiple kernel learning algorithms
Mehmet Gönen and Ethem Alpaydın. 2011 · 2011
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Financial volatility forecasting with range-based autoregressive volatility model
Hongquan Li and Yongmiao Hong. 2011 · 2011
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When is a liability not a liability? textual analysis, dictionaries, and 10-ks
Tim Loughran and Bill McDonald. 2011 · 2011
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A comprehensive look at financial volatility prediction by economic variables
Charlotte Christiansen, Maik Schmeling, and Andreas Schrimpf. 2012 · 2012
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Estimation of monthly volatility: An empirical comparison of realized volatility, garch and acd-icv methods
Shouwei Liu and Yiu Kuen Tse. 2013 · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
Document-level sentiment classification: An empirical comparison between svm and ann
Rodrigo Moraes, JoãO Francisco Valiati, and Wilson P GaviãO Neto. 2013 · 2013
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Financial keyword expansion via continuous word vector representations
Ming-Feng Tsai and Chuan-Ju Wang. 2014 · 2014
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A semiparametric gaussian copula regression model for predicting financial risks from earnings calls
William Yang Wang and Zhenhao Hua. 2014 · 2014
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Deep learning for event-driven stock prediction
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015 · 2015
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Predicting abnormal returns from news using text classification
Ronny Luss and Alexandre d’Aspremont. 2015 · 2015
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Topic modeling based sentiment analysis on social media for stock market prediction
Thien Hai Nguyen and Kiyoaki Shirai. 2015 · 2015
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Detecting risks in the banking system by sentiment analysis
Clemens Nopp and Allan Hanbury. 2015 · 2015
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Financial sentiment analysis for risk prediction
Chuan-Ju Wang, Ming-Feng Tsai, Tse Liu, and Chin-Ting Chang. 2013 · 2013
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Semantic Frames to Predict Stock Price Movement
Boyi Xie, Rebecca J Passonneau, and Leon Wu. 2013 · 2013
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The information content of mandatory risk factor disclosures in corporate filings
John L Campbell, Hsinchun Chen, Dan S Dhaliwal, Hsin-min Lu, and Logan B Steele. 2014 · 2014
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Evaluating sentiment analysis evaluation: A case study in securities trading
Siavash Kazemian, Shunan Zhao, and Gerald Penn. 2014 · 2014
Cited alongside, same era.
Uncertainty in neural network word embedding: Exploration of threshold for similarity
Navid Rekabsaz, Mihai Lupu, and Allan Hanbury. 2016a
Cited in the paper.
Generalizing translation models in the probabilistic relevance framework
Navid Rekabsaz, Mihai Lupu, Allan Hanbury, and Guido Zuccon. 2016b
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The ever-expanding 10-k: Why are 10-ks getting so much longer (and does it matter)?
Travis Dyer, Mark H Lang, and Lorien Stice-Lawrence. 2016 · 2016
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Learning the kernel matrix via predictive low-rank approximations
Martin Stražar and Tomaž Curk. 2016 · 2016
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Exploration of a threshold for similarity based on uncertainty in word embedding
Navid Rekabsaz, Mihai Lupu, Allan Hanbury, and Guido Zuccon. 2017 · 2017
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