Twitter Sentiment and Stock Returns
(2019) NEKN02 20191Department of Economics
- Abstract
- This study aims to investigate if the sentiment expressed on Twitter has an effect on individual stock returns. We use a uniquely large data-set consisting of129 million tweets related to 31 companies over a 15-month period. The sentiment is derived on a daily basis with Loughran and McDonald’s established finance-focused method, as well as with Vader, a sentiment analysis tool developed specially for social media. Using a panel data regression, we empirically test Twitter sentiment effectual forecasting power on individual stock returns. Our main findings are: 1) the sentiment derived from Twitter can help predict individual stock returns up to two days ahead 2) the individual stock returns are more sensitive to ’cashtag’-tweets than... (More)
- This study aims to investigate if the sentiment expressed on Twitter has an effect on individual stock returns. We use a uniquely large data-set consisting of129 million tweets related to 31 companies over a 15-month period. The sentiment is derived on a daily basis with Loughran and McDonald’s established finance-focused method, as well as with Vader, a sentiment analysis tool developed specially for social media. Using a panel data regression, we empirically test Twitter sentiment effectual forecasting power on individual stock returns. Our main findings are: 1) the sentiment derived from Twitter can help predict individual stock returns up to two days ahead 2) the individual stock returns are more sensitive to ’cashtag’-tweets than general-company tweets 3) Twitter data can be used to explain the stock return volatility 4) Loughran and McDonald Lexicon-based method outperforms Vader. The results indicate that Twitter data is a suitable data source to understand and forecast stock market movements. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8984394
- author
- Wolf, Fredrik LU and Bergdorf, Oskar LU
- supervisor
- organization
- course
- NEKN02 20191
- year
- 2019
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- sentiment analysis, Twitter, stock return, volatility, prediction, forecasting
- language
- English
- id
- 8984394
- date added to LUP
- 2019-08-08 10:28:30
- date last changed
- 2019-08-08 10:28:30
@misc{8984394, abstract = {{This study aims to investigate if the sentiment expressed on Twitter has an effect on individual stock returns. We use a uniquely large data-set consisting of129 million tweets related to 31 companies over a 15-month period. The sentiment is derived on a daily basis with Loughran and McDonald’s established finance-focused method, as well as with Vader, a sentiment analysis tool developed specially for social media. Using a panel data regression, we empirically test Twitter sentiment effectual forecasting power on individual stock returns. Our main findings are: 1) the sentiment derived from Twitter can help predict individual stock returns up to two days ahead 2) the individual stock returns are more sensitive to ’cashtag’-tweets than general-company tweets 3) Twitter data can be used to explain the stock return volatility 4) Loughran and McDonald Lexicon-based method outperforms Vader. The results indicate that Twitter data is a suitable data source to understand and forecast stock market movements.}}, author = {{Wolf, Fredrik and Bergdorf, Oskar}}, language = {{eng}}, note = {{Student Paper}}, title = {{Twitter Sentiment and Stock Returns}}, year = {{2019}}, }