Outsider trading: trading on twitter sentiment
| dc.contributor.advisor | van Rensburg, Paul | |
| dc.contributor.author | Stevens, Joshua | |
| dc.date.accessioned | 2023-04-20T13:44:01Z | |
| dc.date.available | 2023-04-20T13:44:01Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-20T13:42:38Z | |
| dc.description.abstract | This study aims to establish if a relationship between the investor sentiment generated from social media posts, such as Tweets, and the return on securities exists. If a relationship exists, one would be able to obtain an informational advantage from public information and outperform the market on a risk-adjusted basis. This would give the “outsider” information processed the predictive power of insider information, hence the title of the paper. The study makes use of Bloomberg's social activity data, which through natural language processing, allows for investor sentiment to be obtained by analysing a combination of Twitter and Stock Twits posts. This paper makes use of a three-prong approach, firstly examining if investor sentiment is a predictor of next-day returns. Next, an event study methodology is used to examine the optimal holding period, which can further be expanded to test market efficiency. Lastly, this paper considers the asymmetric risk aversion as outlined by Kahneman and Tversky (1979). Results show that there is little to no correlation between sentiment and next day returns. There is evidence for a multi-day holding period being optimal but statistically insignificant and there is no evidence found for asymmetric risk aversion. | |
| dc.identifier.apacitation | Stevens, J. (2022). <i>Outsider trading: trading on twitter sentiment</i>. (). ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/37802 | en_ZA |
| dc.identifier.chicagocitation | Stevens, Joshua. <i>"Outsider trading: trading on twitter sentiment."</i> ., ,Faculty of Commerce ,Department of Finance and Tax, 2022. http://hdl.handle.net/11427/37802 | en_ZA |
| dc.identifier.citation | Stevens, J. 2022. Outsider trading: trading on twitter sentiment. . ,Faculty of Commerce ,Department of Finance and Tax. http://hdl.handle.net/11427/37802 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Stevens, Joshua AB - This study aims to establish if a relationship between the investor sentiment generated from social media posts, such as Tweets, and the return on securities exists. If a relationship exists, one would be able to obtain an informational advantage from public information and outperform the market on a risk-adjusted basis. This would give the “outsider” information processed the predictive power of insider information, hence the title of the paper. The study makes use of Bloomberg's social activity data, which through natural language processing, allows for investor sentiment to be obtained by analysing a combination of Twitter and Stock Twits posts. This paper makes use of a three-prong approach, firstly examining if investor sentiment is a predictor of next-day returns. Next, an event study methodology is used to examine the optimal holding period, which can further be expanded to test market efficiency. Lastly, this paper considers the asymmetric risk aversion as outlined by Kahneman and Tversky (1979). Results show that there is little to no correlation between sentiment and next day returns. There is evidence for a multi-day holding period being optimal but statistically insignificant and there is no evidence found for asymmetric risk aversion. DA - 2022 DB - OpenUCT DP - University of Cape Town KW - sentiment analysis KW - twitter KW - event study LK - https://open.uct.ac.za PY - 2022 T1 - Outsider trading: trading on twitter sentiment TI - Outsider trading: trading on twitter sentiment UR - http://hdl.handle.net/11427/37802 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37802 | |
| dc.identifier.vancouvercitation | Stevens J. Outsider trading: trading on twitter sentiment. []. ,Faculty of Commerce ,Department of Finance and Tax, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37802 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Finance and Tax | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | sentiment analysis | |
| dc.subject | ||
| dc.subject | event study | |
| dc.title | Outsider trading: trading on twitter sentiment | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MCom |