Sentiment in Big Tech’s Investor Relations: Does the Discourse Predict Future Stock Movements?

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dc.contributor.author Goldberg, David
dc.contributor.author Hong, Sukhwa
dc.contributor.author Villacis Calderon, Eduardo
dc.date.accessioned 2023-12-26T18:36:44Z
dc.date.available 2023-12-26T18:36:44Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 12ad8707-d043-414d-bb35-9ffe2d6d977a
dc.identifier.uri https://hdl.handle.net/10125/106512
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Science and Machine Learning to Support Business Decisions
dc.subject investor relations
dc.subject sentiment analysis
dc.subject stock returns
dc.subject text mining
dc.title Sentiment in Big Tech’s Investor Relations: Does the Discourse Predict Future Stock Movements?
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Financial disclosures are crucial for understanding a firm's status and future performance. While previous research has focused on written disclosures like press releases and reports, these documents have limitations in that they are carefully crafted one-way communication from firms to the public. Our study explores the predictive possibility of communications during investor relations calls. These calls capture unscripted narratives from between firms’ senior leadership and industry analysts. By examining the interplay between the tone of public questions and senior leadership's responses, we investigate to what extent this interaction predicts a firm's future performance. We find that average question sentiment has a persistent positive association with average stock price in the successive quarter, but answer sentiment was not a significant predictor. Our study offers a fresh perspective on financial disclosures and highlights the value of oral communications and their tones in gaining insights into firms' prospects.
dcterms.extent 7 pages
prism.startingpage 1130
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Data Science and Machine Learning to Support Business Decisions