Trading Strategies to Exploit Blog and News Sentiment


  • Wenbin Zhang Stony Brook University
  • Steven Skiena Stony Brook University


Social Media Analysis, Financial Analysis, Sentiment Analysis, Data Mining


We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified.




How to Cite

Zhang, W., & Skiena, S. (2010). Trading Strategies to Exploit Blog and News Sentiment. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 375-378. Retrieved from