Publicação
The role of social media in the stock market: twitter sentiment as a predictor of stock returns
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Rizzo, Antonino Emanuele | |
| dc.contributor.author | Costa, Davide Gomes | |
| dc.date.accessioned | 2022-06-22T08:43:39Z | |
| dc.date.available | 2022-06-22T08:43:39Z | |
| dc.date.issued | 2022-01-10 | |
| dc.date.submitted | 2021-12-17 | |
| dc.description.abstract | The recent surge of emerging technologies, combined with the growth of social media securities-related microblogging, instigated academics to explore new proxies for sentiment. This research dissects the association between 1-month lagged Twitter sentiment and stock returns for the S&P500 constituents from 2008 to 2021 through the sentiment analysis of approximately 34.7million tweets. Evidence shows a consistent variation pattern of returns across the scope of the anomaly. Furthermore, abnormal returns associated with high Twitter sentiment are pervasive and significant, particularly for value-weight returns. In contrast, there is insufficient evidence on the pervasiveness of abnormal returns for low Twitter sentiment. Key words: Asset Pricing, Big Data, Sentiment Analysis, Stock Return, Investor Sentiment. | pt_PT |
| dc.identifier.tid | 202973158 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/140471 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | Nova School of Business and Economics | |
| dc.subject | Asset pricing | pt_PT |
| dc.subject | Big data | pt_PT |
| dc.subject | Sentiment analysis | pt_PT |
| dc.subject | Stock return | pt_PT |
| dc.subject | Investor sentiment | pt_PT |
| dc.title | The role of social media in the stock market: twitter sentiment as a predictor of stock returns | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UID/ECO/00124/2013 | |
| oaire.awardTitle | Nova School of Business and Economics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FECO%2F00124%2F2013/PT | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| relation.isProjectOfPublication | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| relation.isProjectOfPublication.latestForDiscovery | 644a3f4f-817b-4d0d-aba6-f98cdca28bc7 | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics | pt_PT |
Ficheiros
Principais
1 - 1 de 1
A carregar...
- Nome:
- 2021-22_fall_40789_davide-costa.pdf
- Tamanho:
- 710.85 KB
- Formato:
- Adobe Portable Document Format