Social Media Information and Analyst Forecasts

Document Type

Peer-Reviewed Article

Publication Date

2019

Abstract

Purpose: The purpose of this paper is to examine whether corporate social media information helps improve analysts’ forecast accuracy. Design/methodology/approach: This study uses hand-collected information on S&P 500 firms’ official Facebook pages and uses posts and reactions to such posts to measure corporate Facebook information. Multivariate regression models are estimated to test the relationship between analysts’ forecast accuracy and corporate Facebook information. Findings: The results indicate that analysts forecast accuracy is unresponsive to posts. However, analyst forecast errors are decreasing in reactions to posts. These findings are robust to the inclusion of control variables, firm and time fixed effects, and alternative specifications of forecast errors and different pre-forecast time windows. Research limitations/implications: This study has some limitations. It focuses only on the S&P 500 firms, which are large and generally provide better information to the market. The sample period coincides with the early period of the corporate Facebook culture. However, more recent data sets are likely to provide stronger results. Practical implications The findings of this study provide support for “information generation” role of social media and show that reactions to corporate Facebook posts are the new and unique information generated from corporate social media activities, which help information intermediaries in improving their forecasting accuracy. Originality/value: This study makes an important contribution to the literature by separating the information dissemination role of social media from information generation role and establishes the first evidence on how corporate social media information affects forecast accuracy of financial analysts.

DOI

10.1108/MF-07-2018-0323

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