Ambiguity Aversion and Beating Benchmarks: Does it Create a Pattern?
Academic Article
Overview
Research
Identity
Other
View All
Overview
abstract
The prior literature on analyst forecasts has focused almost exclusively on firms that just meet or beat the mean or median consensus analyst forecast, without much regard to alternative benchmarks within the forecast distribution. Anecdotal evidence suggests that there is institutional significance to the lowest (minimum) and highest (maximum) analyst earnings forecast. We rigorously explore whether these two new benchmarks actually have incremental significance and, if so, whether there are differences in how managers and investors perceive the importance of these three benchmarks (i.e., minimum, mean, and maximum). Consistent with the theory of investor ambiguity aversion, which predicts an asymmetric market response to good and bad news, our results support the notion that of the three benchmarks we explore, firms act most aggressively to exceed the minimum forecast, followed by the mean, and then finally the maximum. This order is consistently supported by the following evidence: the existence of higher incentives to beat the benchmark; the likelihood of earnings management to beat the benchmark; accrual reversal after firms just barely achieve each benchmark; accrual mispricing around each benchmark; and, finally, a faster incorporation into the stock price of the bad news that a firm misses the minimum than of the good news that a firm meets or beats the maximum. These findings fill a void in academic research on these two new benchmarks and offer a consistent explanation as to why the popular press and managers frequently highlight and discuss beating these benchmarks as a separate and notable achievement. This paper was accepted by Brian Bushee, accounting. Funding: Q. Xin gratefully acknowledges the financial support from the Shenzhen Peacock Plan start-up research [Grant GA11409004] and the Shenzhen Key Research Base of Humanities and Social Sciences [Grant KP191001]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4609 .
