Managerial Incentives and Management Forecast Precision
By Qiang Cheng, Ting Luo, Heng Yue. 2013
154 citations recorded by the source corpus.
ABSTRACT: Managers have great discretion in determining forecast characteristics, but little is known about how managerial incentives affect these characteristics. This paper examines whether managers strategically choose forecast precision for self-serving purposes. Building on the prior finding that the market reaction to vague forecasts is weaker than its reaction to precise forecasts, we find that for management forecasts disclosed before insider sales, more positive (negative) news forecasts are more (less) precise than other management forecasts. The opposite applies to management forecasts disclosed before insider purchases. These results are consistent with managers strategically choosing forecast precision to increase stock prices before insider sales and to decrease stock prices before insider purchases. Additional analyses indicate that the impact of managerial incentives on forecast precision is less pronounced when institutional ownership is high or when disclosure risk is high, and is more p...
Subfields: Accounting, Finance
View Paper: https://www.semanticscholar.org/paper/bd21645f910e89da2afc65d2b2d30f7da90e7333
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In Accounting
- Lara Al-Haddad
- Benjamin Segal
- Mark R. Whittington
- Rencheng Wang
- Gilles Hilary
- Charles Hsu
- A. Gerged
- Anne L. Beatty
In Finance
Citations in the corpus
- The impact of corporate governance mechanisms on real and accrual earnings management practices: evidence from Jordan (2019, background): “(2002) and Cheng et al. (2013) finding a significant negative relationship between AEM and institutional investors, but other studies, such as Roodposhti and Chashmi (2011), find a positive relationship.No significant relationship is found by Peasnell et al. (2005), Alves (2012) and Chen and Zhang (”
- The Bright Side of Managerial Over-Optimism (2016, methodology): “Empirically, we use to measure overconfidence. is the difference between the upper- and lower-end estimates, divided by the absolute value of the mid-point forecast (point estimates have a range of zero, Cheng et al., 2013).”