Capturing Investor Sentiment: Advancing Predictability in Finance with Computer Science Approaches

This discussion paper reviews both finance and computer science literature on sentiment. The toolset for addressing the sentiment analysis problem in computer science goes beyond methods commonly used in finance literature. The broader toolset of computer science has potential to improve the performance of sentiment measurement and predictability in financial markets.

[1]  Mingxi Wang,et al.  Investor Sentiment and the Cross-Section of Stock Returns , 2009 .

[2]  Chenchuramaiah T. Bathala.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2007 .

[3]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[4]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[5]  Eric Brill,et al.  Reducing the human overhead in text categorization , 2006, KDD '06.

[6]  Gjorgji Madjarov,et al.  Twitter Sentiment Analysis Using Deep Convolutional Neural Network , 2015, HAIS.

[7]  Thomas Renault,et al.  Intraday online investor sentiment and return patterns in the U.S. stock market , 2017 .

[8]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[9]  Michael T. Cliff,et al.  Investor Sentiment and Asset Valuation , 2001 .

[10]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[11]  Malcolm P. Baker,et al.  Global, Local, and Contagious Investor Sentiment , 2009 .

[12]  Klaus-Robert Müller,et al.  Explaining Recurrent Neural Network Predictions in Sentiment Analysis , 2017, WASSA@EMNLP.

[13]  Zhi Da,et al.  The Sum of All FEARS: Investor Sentiment and Asset Prices , 2013 .

[14]  Enrique Herrera-Viedma,et al.  Sentiment analysis: A review and comparative analysis of web services , 2015, Inf. Sci..

[15]  Michael W. Brandt,et al.  Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil , 2019, Journal of Empirical Finance.

[16]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[17]  Mike Y. Chen,et al.  Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web , 2001 .

[18]  Kyoungok Kim,et al.  Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction , 2014, Pattern Recognit..

[19]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[20]  Mike Thelwall,et al.  A Study of Information Retrieval Weighting Schemes for Sentiment Analysis , 2010, ACL.

[21]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[22]  Werner Antweiler,et al.  Is All that Talk Just Noise? The Information Content of Internet Stock Message Boards , 2001 .