{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37802"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37802","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Outsider trading: trading on twitter sentiment","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Stevens, Joshua"],"institution":"Department of Finance and Tax","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["van Rensburg, Paul"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-22T22:23:29Z","subjects":["sentiment analysis","twitter","event study"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37802","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["van Rensburg, Paul"]},{"key":"dc:creator","label":"Author","values":["Stevens, Joshua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-20T13:44:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-20T13:44:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2022"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Finance and Tax"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MCom"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["sentiment analysis","twitter","event study"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/37802"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Outsider trading: trading on twitter sentiment"]}]}],"canonical_facts":{"dc:contributor.advisor":["van Rensburg, Paul"],"dc:creator":["Stevens, Joshua"],"dc:date.accessioned":["2023-04-20T13:44:01Z"],"dc:date.available":["2023-04-20T13:44:01Z"],"dc:date.issued":["2022"],"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.uri":["http://hdl.handle.net/11427/37802"],"dc:publisher.department":["Department of Finance and Tax"],"dc:subject":["sentiment analysis","twitter","event study"],"dc:title":["Outsider trading: trading on twitter sentiment"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MCom"]},"updated_at":"2026-07-22T22:23:29Z"}