Start Date
16-8-2018 12:00 AM
Description
Since 2008, there has been a dramatic increase in the number of firms communicating financial information on social media. Additionally, companies face increased market pressures to meet or exceed analysts’ expectations to avoid negative impacts on firm value. This study bridges two streams of research (management expectations and firms’ use of social media) to investigate whether social media, specifically Twitter, provides a new tool to guide analysts’ forecasts to meet or exceed earnings expectations. We use propensity score matching to isolate the effects of social media on analysts’ forecasts and to provide a plausible explanation for why firms strategically disseminate negative news on social media. We collect data from Compustat, IBES, and Twitter to answer the proposed research question. Our preliminary analysis finds a significant positive relationship between the change in analyst forecasts and the number and valence of relevant tweets.
Recommended Citation
Nabity, Teagen; Sorensen, Trevor; and Johnson, Peter, "Go Tell It on the Internet: Twitter Effects on Expectations Management" (2018). AMCIS 2018 Proceedings. 10.
https://aisel.aisnet.org/amcis2018/AccountingIS/Presentations/10
Go Tell It on the Internet: Twitter Effects on Expectations Management
Since 2008, there has been a dramatic increase in the number of firms communicating financial information on social media. Additionally, companies face increased market pressures to meet or exceed analysts’ expectations to avoid negative impacts on firm value. This study bridges two streams of research (management expectations and firms’ use of social media) to investigate whether social media, specifically Twitter, provides a new tool to guide analysts’ forecasts to meet or exceed earnings expectations. We use propensity score matching to isolate the effects of social media on analysts’ forecasts and to provide a plausible explanation for why firms strategically disseminate negative news on social media. We collect data from Compustat, IBES, and Twitter to answer the proposed research question. Our preliminary analysis finds a significant positive relationship between the change in analyst forecasts and the number and valence of relevant tweets.