Sentiment Predictability for Stocks

In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.

[1]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[2]  Eirini Ntoutsi,et al.  Large Scale Sentiment Learning with Limited Labels , 2017, KDD.

[3]  Guido Caldarelli,et al.  Investigating the Relations between Twitter Sentiment and Stock Prices , 2015, ArXiv.

[4]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[5]  Yue Zhang,et al.  Using Structured Events to Predict Stock Price Movement: An Empirical Investigation , 2014, EMNLP.

[6]  Paul C. Tetlock.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2005, The Journal of Finance.

[7]  Sofus A. Macskassy,et al.  More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From , 2007 .

[8]  T. Rao,et al.  Analyzing Stock Market Movements Using Twitter Sentiment Analysis , 2012, ASONAM 2012.

[9]  Min-Yuh Day,et al.  Deep learning for financial sentiment analysis on finance news providers , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[10]  Yue Zhang,et al.  Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.

[11]  Takashi Matsubara,et al.  Deep learning for stock prediction using numerical and textual information , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[12]  Kira Radinsky,et al.  Learning causality for news events prediction , 2012, WWW.

[13]  ChenHsinchun,et al.  Textual analysis of stock market prediction using breaking financial news , 2009 .