Trading Strategies to Exploit Blog and News Sentiment

Wenbin Zhang · Steven Skiena

253 citations0/0 files storedProceedings of the International AAAI Conference on Web and Social Media

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Financial Markets and Investment Strategies Sentiment Analysis and Opinion Mining Stock Market Forecasting Methods

Abstract

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.

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References

2004Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards 2008More Than Words: Quantifying Language to Measure Firms' Fundamentals 1988What Moves Stock Prices?2003Stock price reaction to news and no-news: drift and reversal after headlines

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