Paper Type
Short
Paper Number
PACIS2025-1681
Description
This study investigates the impact of crowdsourced analysts’ forecasts on firms' earnings management behavior. Firms are subject to the pressure of meeting the analysts’ earnings (i.e., EPS) forecasts when announcing their earnings. Failing to meet the forecasts leads to negative market reactions. Using comprehensive datasets that integrate sell-side forecasts and crowdsourced forecasts, we find that although crowdsourced forecasts reduce the pessimistic bias in Wall Street analysts’ forecasts, firms are more, rather than less, engaged in meeting-or-beating the analysts’ forecasts. Excessive crowdsourced forecasts lead to pressure, and Estimize’s consensus becomes a new benchmark for firms’ earnings management. Our results highlight the dark side of the crowdsourced forecasts in causing excessive earnings management pressure, and the mixed role of crowdsourced forecasts on correcting bias as well as introducing extra financial pressure.
Recommended Citation
Lu, Huxi and LIU, Yidi, "Another Wall Street: How Crowdsourcing Restrikes the Balance of Firms’ Meet-or-Beat Earnings Expectations (MBE)" (2025). PACIS 2025 Proceedings. 1.
https://aisel.aisnet.org/pacis2025/is_design/isdesign/1
Another Wall Street: How Crowdsourcing Restrikes the Balance of Firms’ Meet-or-Beat Earnings Expectations (MBE)
This study investigates the impact of crowdsourced analysts’ forecasts on firms' earnings management behavior. Firms are subject to the pressure of meeting the analysts’ earnings (i.e., EPS) forecasts when announcing their earnings. Failing to meet the forecasts leads to negative market reactions. Using comprehensive datasets that integrate sell-side forecasts and crowdsourced forecasts, we find that although crowdsourced forecasts reduce the pessimistic bias in Wall Street analysts’ forecasts, firms are more, rather than less, engaged in meeting-or-beating the analysts’ forecasts. Excessive crowdsourced forecasts lead to pressure, and Estimize’s consensus becomes a new benchmark for firms’ earnings management. Our results highlight the dark side of the crowdsourced forecasts in causing excessive earnings management pressure, and the mixed role of crowdsourced forecasts on correcting bias as well as introducing extra financial pressure.
Comments
Design