Cited By
View all- Vieira da Silva FRoman NCarvalho A(2020)Stock market tweets annotated with emotionsCorpora10.3366/cor.2020.020315:3(343-354)Online publication date: Nov-2020
An investigation into correlations between financial sentiment and prices in financial markets
Pages 99 - 108
Abstract
There is now a small but growing literature showing some relationship between sentiment contained within blogs, online news article and message boards and price movements in financial markets. Typically, researchers use keyword searching to find financially relevant messages, then rate them in terms of their how positive or negative the sentiment they contain is in relation to prices.
Through an exploratory analysis of the statistical nature of word frequency movements on Twitter, we highlight some issues with this approach and define how a sentiment variable could be constructed to generate well specified linear regression models.
We then address a second issue of how to model time. Current research has used units of a day or week for both sentiment and price series. There is no discussion in the literature in this area as to what the best unit of time might be, or indeed, if there is a weekly topology to sentiment price correlations. We present two models which explore how these factors affect sentiment-price correlations.
Finally we present results correlating financial sentiment on Twitter to the price of the Standard and Poor's Index of 500 Leading Shares. We report both contemporaneous (R squared values up to 0.35) and predictive correlations (R squared values up to 0.27) between our sentiment metric and prices. Scale and weekly topology both appear significant factors that would benefit inclusion in future models.
References
[1]
Antweiler, W., & Frank, M. Z. Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59, 2 (2004), 1259--1294.
[2]
Antweiler, Werner, & Frank, M. Z. Do US stock markets typically overreact to corporate news stories? Unpublished working paper, University of Minnesota, (2006), 1--22.
[3]
Bollen, J., Mao, H., & Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science, 2, 1 (2011), 1--8.
[4]
Chan, W. Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics, 70, 2 (2003), 1--48.
[5]
Chen, H., De, P., Hu, Y., & Hwang, B. Sentiment revealed in social media and its effect on the stock market. Proc. Statistical Signal Processing Workshop 2011, IEEE, (2011), 25--28.
[6]
Cutler, D. M., Poterba, J. M., & Summers, L. H. What moves stock prices? The Journal of Portfolio Management, 15, 3 (1989), 4--12.
[7]
Das, S. R., & Chen, M. Y. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53, 9 (2007), 1375--1388.
[8]
Fama, E. F. Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49 (1998), 283--306.
[9]
Gibbons, M., & Hess, P. Day of the Week Effects and Asset Returns. The Journal of Business, 54, 4 (1981), 579--596.
[10]
Granger, C., & Newbold, P. Spurious regressions in econometrics. Journal of econometrics, 2, (1974), 111--120.
[11]
Li, F. (2006). Do stock market investors understand the risk sentiment of corporate annual reports? Available at SSRN, 1--53.
[12]
Loughran, T., & Mcdonald, B. When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66, 1 (2011), 35--65.
[13]
Mao, H., Counts, S., & Bollen, J. Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data. Arxiv preprint arXiv: 1112.1051 (2011), 1--10.
[14]
Phillips, P., & Perron, P. Testing for a unit root in time series regression. Biometrika, 75, 2 (1988), 335--346.
[15]
Sadka, R. Momentum and post-earnings-announcement drift anomalies: The role of liquidity risk. Journal of Financial Economics, 80, 2 (2006), 309--349.
[16]
Tetlock, P. C. Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62, 3 (2007), 1139--1168.
[17]
Tetlock, P. C, Saar-Tsechansky, M., & Macskassy, S. More Than Words: Quantifying Language to Measure Firms' Fundamentals. The Journal of Finance, 63, 3 (2008), 1437--1467.
[18]
Yang, J., & Leskovec, J. Patterns of temporal variation in online media. Proc. of the fourth ACM international conference on Web search and data mining (2011), 177--186
[19]
Zhang, X., Fuehres, H., & Gloor, P. A. Predicting Stock Market Indicators Through Twitter "I hope it is not as bad as I fear". Procedia. Social and Behavioral Sciences, 26, (2009), 55--62.
Index Terms
- An investigation into correlations between financial sentiment and prices in financial markets
Recommendations
Estimating the impact of domain-specific news sentiment on financial assets
AbstractThe influence of news on financial markets has been studied by extracting opinions and sentiment from text using content analysis and natural language processing techniques, and then using this measure to estimate the impact of ...
Topic-dependent sentiment analysis of financial blogs
TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinionWhile most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment ...
On the Integration of Production and Financial Hedging Decisions in Global Markets
We study the integrated operational and financial hedging decisions faced by a global firm who sells to both home and foreign markets. Production occurs either at a single facility located in one of the markets or at two facilities, one in each market. ...
Comments
Information & Contributors
Information
Published In
May 2013
481 pages
ISBN:9781450318891
DOI:10.1145/2464464
- Conference Chairs:
- Hugh Davis,
- Harry Halpin,
- Alex Pentland,
- Program Chairs:
- Mark Bernstein,
- Lada Adamic,
- Harith Alani,
- Alexandre Monnin,
- Richard Rogers
Copyright © 2013 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 May 2013
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
WebSci '13
Sponsor:
Acceptance Rates
Overall Acceptance Rate 245 of 933 submissions, 26%
Upcoming Conference
Websci '25
- Sponsor:
- sigweb
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 371Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
Cited By
View all- Vieira da Silva FRoman NCarvalho A(2020)Stock market tweets annotated with emotionsCorpora10.3366/cor.2020.020315:3(343-354)Online publication date: Nov-2020
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in