We examine whether attribution bias leads managers who have experienced short-term forecasting success to become overconfident in their ability to forecast future earnings. Importantly, this form of overconfidence is endogenous and dynamic. We also examine the effect of this cognitive bias on the managerial credibility. Consistent with the existence of dynamic overconfidence, managers who have predicted earnings accurately in the previous four quarters are less accurate in their subsequent earnings predictions. These managers also display greater divergence from the analyst consensus and are more precise. Lastly, investors and analysts react less strongly to forecasts issued by overconfident managers.

We re-investigate the market pricing of special items, with particular emphasis on how managers " frame " these non-operating earnings components via their inclusion or exclusion from " street " earnings, the earnings numbers that firms disclose in their press releases and that analysts track and forecast. When managers include the special items in " street earnings (i.e., " street " = GAAP), the market overprices them, believing the special items to be more persistent than they actually are. As a result, there is a negative relationship between the special items and future stock returns in the following year. However, when managers exclude special items from " street " earnings (i.e., " street " not equal GAAP), the market recognizes their transitory characteristic and the relationship between special items and returns is insignificant in the following year. We also demonstrate that the decision to include (exclude) special items with (from) " street " earnings is associated with whether inclusion or exclusion of special items a) increases earnings numbers, b) smoothes the earnings series, or c) helps managers to meet earnings benchmarks. These results suggest that the decision to include or exclude special items from " street " earnings is associated with managerial incentives to manage earnings numbers rather than signal the persistence of special items.

Rules implemented by the U.S. Securities and Exchange Commission in 2003 impose additional disclosure and filing requirements on firms publicly disclosing non-GAAP earnings. We find the regulations produced (1) modest declines in the frequency of special- and other-item exclusions, (2) a decline in exclusion magnitude, (3) a modest decline in the probability disclosed earnings meet or beat forecasts, and (4) a decline in the association between returns and forecast errors. Our results suggest that, while the regulations reduced firms’ use of non-GAAP disclosures to improve performance perceptions, they also reduced firms’ willingness to use non-GAAP earnings to convey permanent earnings.

We show empirically that analysts who display more consistent forecast errors have greater ability to affect prices, and that this effect is larger than that of stated accuracy. These results lead to three implications. First, consistent analysts are less likely to be demoted and are more likely to be nominated All Star analysts. Second, analysts strategically deliver downward‐biased forecasts to increase their consistency (if at the expense of stated accuracy). Finally, the benefits of consistency and of “lowballing” (accuracy) are increasing (decreasing) in institutional investors’ presence.

Bundling managerial earnings guidance with quarterly earnings announcements (EAs) has become an increasingly common practice. This study investigates the impact of bundled guidance on analysts' forecast revisions. Our findings indicate that analysts respond more to bundled guidance than non‐bundled guidance. This effect increases with analysts' time pressure and cognitive constraints around the EA. Analysts' revisions also incorporate more of the bundled management guidance when accompanied by additional information, such as conference calls. We further find that analysts revise their forecasts more quickly following bundled guidance than non‐bundled guidance. Together, these findings are consistent with the notion that analysts place more weight on bundled guidance than on non‐bundled guidance in their forecast revisions as bundled guidance facilities analysts' timely forecast revisions following EAs. Finally, we find that analysts' forecast revisions following bundled guidance generate significant market reactions. Our findings enhance our understanding of analysts' information processing and shed light on why bundling can be an effective guidance strategy.

We investigate whether non-GAAP earnings disclosures increase stock price crash risk. Consistent with non-GAAP disclosures allowing managers to inflate investors’ perceptions about firm performance, our results indicate that income increasing non-GAAP reporting increases crash risk. We also find that managers can use non-GAAP reporting as a substitute for earnings management to withhold bad news from investors (the traditional explanation for crashes). Finally, we find a positive association between non-GAAP reporting and the likelihood of subsequent events that can trigger a crash. Overall, our evidence is consistent with some non-GAAP disclosures exposing investors to risks of large and sudden price declines.

We posit that management forecasts, which are predictable transformations of realized earnings without random errors, are more informative than unbiased forecasts, which manifest small but unpredictable errors, even if biased forecasts are less accurate. Consistent with this intuition, we find that managers who make consistent forecasting errors have a greater ability to influence investor reactions and analyst revisions, even after controlling for the effect of accuracy. This effect is more economically significant and statistically robust than that of forecast accuracy. More sophisticated investors and experienced analysts are found to have a better understanding of the benefits of consistent management forecasts.

Human estimation and inference are subject to systematic biases such as overconfidence and over-optimism. In contrast to prior research that has identified multiple negative consequences of these biases, we focus on positive effects. We empirically examine a setting in which over-optimism a) is a related but different bias from overconfidence, b) emerges dynamically in a rational economic framework, and c) generates higher managerial effort. Importantly, this additional effort improves firm profitability and market value.