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Trading strategies to exploit blog and news sentiment

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

162 Scopus citations

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.

Original languageEnglish
Title of host publicationICWSM 2010 - Proceedings of the 4th International AAAI Conference on Weblogs and Social Media
PublisherAAAI Press
Pages375-378
Number of pages4
Edition1
ISBN (Print)9781577354451
DOIs
StatePublished - 2010
Event4th International AAAI Conference on Weblogs and Social Media, ICWSM 2010 - Washington, DC, United States
Duration: May 23 2010May 26 2010

Publication series

NameICWSM 2010 - Proceedings of the 4th International AAAI Conference on Weblogs and Social Media
Number1
Volume4

Conference

Conference4th International AAAI Conference on Weblogs and Social Media, ICWSM 2010
Country/TerritoryUnited States
CityWashington, DC
Period05/23/1005/26/10

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