This dissertation examines the general research issue of whether the components of earnings are informative and specifically 1) how analysts consider earnings components when predicting future earnings and 2) whether the information content in, and analysts’ use of, earnings components have changed through time. Although earnings components have predictive value for future earnings based on each component’s persistence, extant research provides only a limited understanding of whether and how analysts consider this when forecasting. Using an integrated income statement and balance sheet framework to estimate the persistence of earnings components, I first establish that disaggregation based on the earnings components framework in this study is helpful to predict future earnings and helps explains contemporaneous returns. I then find evidence suggesting that although analysts consider the persistence of various earnings components, they do not fully integrate this information into their forecasts. Interestingly, analysts appear to be selective in their incorporation of the information in earnings components, seeming to ignore information from components indicating lower persistence, which results in higher forecast errors. Conversely, when a firm’s income is concentrated in high persistence items, analysts appear to incorporate the information into their forecasts, reducing their forecast errors. I also report that the usefulness of components relative to aggregate earnings has dramatically and continuously increased over the past several decades, and contemporaneous returns appear to be much better explained by earnings components than aggregate earnings (than historically). Finally, the relation between analyst forecast errors and the differential persistence of earnings components has also declined over time, indicating that analysts appear to recognize the increasing importance of earnings components through time. / text

I explore whether participation on Estimize.com, a crowdsourced earnings-forecasting platform aimed primarily at novices, improves professional analysts’ forecast accuracy and career outcomes. Estimize provides its contributors with frequent and timely feedback on their forecast performance and offers them a new channel for disseminating their forecasts to a wider public, features that could help analysts improve their forecast accuracy and raise their online visibility. Using proprietary data obtained from Estimize and a difference-in-differences research design, I find that IBES analysts who are active on Estimize improve their EPS forecast accuracy by 13% relative to the sample-mean forecast error, as well as reduce forecast bias. These improvements in performance vary predictably in ways consistent with learning through feedback. Additionally, I find increased market reaction to the positive earnings-forecasts revisions issued by analysts who are active on Estimize. I also find that analysts active on Estimize enjoy incremental positive career outcomes after controlling for forecast accuracy. My results suggest that professional analysts can learn to become better forecasters through online feedback and consequently garner more attention from the market. My results also suggest analysts can improve their career outcomes by gaining additional online visibility. / Business Administration/Accounting