Corporate use of the X social media platform and analyst forecasting

This study aimed to investigate whether the corporate use of X (formerly Twitter) for disseminating financial information, as well as likes, comments, and retweets related to this information, can improve analysts' forecasting accuracy. The dissemination of information on X signals additional evidence for stakeholders in the organization, such as analysts. Multiple linear regression was employed, with robust standard errors. The analysis consisted of 2,548 observations from the period of 2013 to 2019. The results demonstrate that analysts' forecast error is maximized according to the reactions to the posts. Likes, comments, and retweets from the previous quarter influence the accuracy of analysts' forecasts in the current quarter. A possible explanation for this result may lie in the fact that companies publish excessively optimistic information. This leads to an optimistic reaction from users, consequently inducing analysts to err. Further analysis examined the interaction between profitable companies during the period and the variables from X. The results of the interaction between the variables and the companies' profits indicated that profitable companies that tweet demonstrate a greater forecast error from analysts compared to profitable companies that do not tweet. The research highlighted that social media can be an important channel for companies to disseminate financial information. Furthermore, the interaction between companies and stakeholders, or among stakeholders themselves on X, facilitates communication, information circulation, and enables feedback—factors that, together, influence stakeholders in the company, such as analysts.

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Publication Details

Journal
Revista Catarinense da Ciência Contábil
Published
2024-10-18
DOI
https://doi.org/10.16930/2237-7662202435192
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Corporate use of the X social media platform and analyst forecasting

Roberto Carlos Klann, Mikaéli da Silva Giordani
Revista Catarinense da Ciência Contábil
Big Data and Business Intelligence
article

Corporate use of the X social media platform and analyst forecasting

Roberto Carlos Klann, Mikaéli da Silva Giordani
article en

Abstract

This study aimed to investigate whether the corporate use of X (formerly Twitter) for disseminating financial information, as well as likes, comments, and retweets related to this information, can improve analysts' forecasting accuracy. The dissemination of information on X signals additional evidence for stakeholders in the organization, such as analysts. Multiple linear regression was employed, with robust standard errors. The analysis consisted of 2,548 observations from the period of 2013 to 2019. The results demonstrate that analysts' forecast error is maximized according to the reactions to the posts. Likes, comments, and retweets from the previous quarter influence the accuracy of analysts' forecasts in the current quarter. A possible explanation for this result may lie in the fact that companies publish excessively optimistic information. This leads to an optimistic reaction from users, consequently inducing analysts to err. Further analysis examined the interaction between profitable companies during the period and the variables from X. The results of the interaction between the variables and the companies' profits indicated that profitable companies that tweet demonstrate a greater forecast error from analysts compared to profitable companies that do not tweet. The research highlighted that social media can be an important channel for companies to disseminate financial information. Furthermore, the interaction between companies and stakeholders, or among stakeholders themselves on X, facilitates communication, information circulation, and enables feedback—factors that, together, influence stakeholders in the company, such as analysts.

Revista Catarinense da Ciência ContábilVol. 23
Universidade Federal de Santa Maria (BR)
Openalex Percentile: Top 72%
Big Data and Business Intelligence
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