Publicação
Twenty-five years (1998-2023) of earnings conference calls
| dc.contributor.author | Maia, Rodrigo dos Reis | |
| dc.contributor.author | Bravo, Jorge Miguel | |
| dc.contributor.institution | NOVA Information Management School (NOVA IMS) | |
| dc.contributor.institution | Information Management Research Center (MagIC) - NOVA Information Management School | |
| dc.date.accessioned | 2024-11-13T22:16:49Z | |
| dc.date.available | 2024-11-13T22:16:49Z | |
| dc.date.issued | 2024-09-27 | |
| dc.description | Maia, R. D. R., & Bravo, J. M. (2024). Twenty-five years (1998-2023) of earnings conference calls: a bibliometric review focusing on artificial intelligence [poster]. Poster session presented at Data Research Meetup by MagIC, Lisbon, Portugal. --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020). | |
| dc.description.abstract | In capital markets, information is the foundation of investment decisions. Transparent corporate disclosures are crucial for guiding investor choices, especially in strategic asset allocation (Bravo & Silva, 2006; Simões et al., 2021). Earnings disclosures and conference calls are pivotal (Healy & Palepu, 2001), allowing senior management to engage stakeholders through real-time, multimedia formats. Public companies regularly conduct quarterly earnings calls, ensuring a consistent flow of financial information. Research has explored how management’s voluntary disclosures during these calls enhance investor understanding and prevent stock underpricing (Graham et al., 2005). Over the last 25 years, this study area has evolved, reflecting technological advancements in corporate communication, such as videocasts. Recent research incorporates artificial intelligence (AI) tools like natural language processing (NLP), sentiment analysis, and audio/image recognition to analyse earnings calls (Druz et al., 2020; Aromi & Clements, 2021; Fiset et al., 2021). This poster aims to map the current research landscape on earnings calls through a bibliometric analysis, identifying trends, gaps, and future research opportunities. By utilising advanced analytical methods, this work contributes to the evolving understanding of corporate communication and its influence on market behaviour. | en |
| dc.description.version | publishersversion | |
| dc.description.version | unpublished | |
| dc.format.extent | 1 | |
| dc.format.extent | 854766 | |
| dc.identifier.other | PURE: 102920288 | |
| dc.identifier.other | PURE UUID: 45aebb01-2d05-42df-b2d4-74af56a61267 | |
| dc.identifier.other | ORCID: /0000-0002-7389-5103/work/171695058 | |
| dc.identifier.uri | http://hdl.handle.net/10362/175144 | |
| dc.language.iso | eng | |
| dc.peerreviewed | no | |
| dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT | |
| dc.relation | Information Management Research Center | |
| dc.subject | SDG 8 - Decent Work and Economic Growth | |
| dc.subject | SDG 12 - Responsible Consumption and Production | |
| dc.title | Twenty-five years (1998-2023) of earnings conference calls | en |
| dc.title.subtitle | a bibliometric review focusing on artificial intelligence [poster] | en |
| dc.type | conference poster | |
| degois.publication.issue | 1 | |
| degois.publication.title | Data Research Meetup by MagIC | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UIDB/04152/2020 | |
| oaire.awardTitle | Information Management Research Center | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/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 | restrictedAccess | |
| relation.isProjectOfPublication | 3274bdb3-4dd3-4bbe-8f74-d34190081f87 | |
| relation.isProjectOfPublication.latestForDiscovery | 3274bdb3-4dd3-4bbe-8f74-d34190081f87 |
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