The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Tweets and Trades: the Information Content of Stock Microblogs
Open accessAbstract
Microblogging forums (e.g., Twitter) have become a vibrant online platform for exchanging stock‐related information. Using methods from computational linguistics, we analyse roughly 250,000 stock‐related messages (so‐called tweets) on a daily basis. We find an association between tweet sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility. In contrast to previous related research, we also analyse the mechanism leading to an efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice.
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Regression Analysis of Count Data
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Twitter mood predicts the stock market
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Cited by
The Effects of Twitter Sentiment on Stock Price Returns
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138441 · Cited by 395
News or Noise? Using Twitter to Identify and Understand Company-specific News Flow
Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014) · Journal of Business Finance & Accounting · doi:10.1111/jbfa.12086 · Cited by 160
Self-citationNews or Noise? The Stock Market Reaction to Different Types of Company-Specific News Events
Sprenger, T. O., & Welpe, I. M. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1734632 · Cited by 28
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Metadata
- Authors
- Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M.
- Venue
- European Financial Management
- Publisher
- Wiley
- Published
- 2013-05-29
- Volume
- 20
- Issue
- 5
- Pages
- 926-957
- ISSN
- 1354-7798 (ISSN-L)
- 1468-036X
- OpenAlex
- W2333981896
- Cited by
- 561
- References
- 65
- FWCI
- 69.22
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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