Identifying and following expert investors in stock microblogs

Roy Bar-Haim*, Elad Dinur, Ronen Feldman, Moshe Fresko, Guy Goldstein

*Corresponding author for this work

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

80 Scopus citations

Abstract

Information published in online stock investment message boards, and more recently in stock microblogs, is considered highly valuable by many investors. Previous work focused on aggregation of sentiment from all users. However, in this work we show that it is beneficial to distinguish expert users from non-experts. We propose a general framework for identifying expert investors, and use it as a basis for several models that predict stock rise from stock microblogging messages (stock tweets). In particular, we present two methods that combine expert identification and per-user unsupervised learning. These methods were shown to achieve relatively high precision in predicting stock rise, and significantly outperform our baseline. In addition, our work provides an in-depth analysis of the content and potential usefulness of stock tweets.

Original languageAmerican English
Title of host publicationEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages1310-1319
Number of pages10
StatePublished - 2011
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2011 - Edinburgh, United Kingdom
Duration: 27 Jul 201131 Jul 2011

Publication series

NameEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

ConferenceConference on Empirical Methods in Natural Language Processing, EMNLP 2011
Country/TerritoryUnited Kingdom
CityEdinburgh
Period27/07/1131/07/11

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