​Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?

Financial analysts provide information in their research reports and thereby help forming expectations of a firm’s future business performance. Thus, it is essential to recognize analysts who provide the most precise forecasts and the accounting literature identifies characteristics that help finding the most accurate analysts. Tanja Klettke detects new relationships and identifies two new determinants of earnings forecast accuracy. These new determinants are an analyst’s “general forecast effort” and the “number of supplementary forecasts”. Within two comprehensive empirical investigations she proves these measures’ power to explain accuracy differences. Tanja Klettke’s research helps investors and researchers to identify more accurate earnings forecasts.

We analyze earnings forecasting errors made by financial analysts for 18 European countries over the 1995-2006 period. We use the Heston-Rouwenhorst approach to unravel country-, industry-, and firm-specific effects as a source of variation in financial analysts' earnings forecast errors. We first estimate each effect with a dummy variable regression, and then decompose the variance of forecast errors into different effects. We provide evidence that the differences among countries, industrial sectors, or analyst following offer a weak explanation for differences in forecast errors. Country effects however largely dominate industry and analyst following effects on European stock markets. By contrast, the type of earnings - profits or losses - and variations in earnings - increases or decreases - play a significant role in the performance of financial analysts.

Studying the determinants of management forecast precision is important because a better understanding of the factors affecting management's choice of forecast precision can provide investors and other users with cues about the characteristics of the information contained in the forecasts. In addition, as regulators assess the regulation of voluntary management disclosures, they need to better understand how managers choose among forecast precision disclosure alternatives. Using 16,872 management earnings forecasts collected from 1995 through 2004, we provide strong evidence that forecast precision is negatively associated with the magnitude of the forecast surprise and that this negative association is stronger when the forecast is bad news than when it is good news. We also find that forecast precision is negatively associated with the absolute magnitude of the forecast error that proxies for the forecast uncertainty that managers face when they issue forecasts, and that the negative association is stronger when forecast errors are negative. These results are consistent with greater liability concerns related to bad news forecasts and negative forecast errors, respectively. Our study provides educators and researchers with important insights into management's choice of earnings forecast precision, which is a component of the voluntary disclosure process that is not well understood.

This thesis examines the determinants of analysts' reactions to firms' earnings management. I present a model showing that analysts revise their forecasts according to their forecast errors revealed by earnings announcements and reporting biases embedded in reported earnings. The model further demonstrates that the relationship between forecast revisions and reporting biases can be affected by analysts' forecasting ability, the inherent uncertainty of whether reporting biases have occurred, as well as analysts' incentives. To empirically test the model's prediction regarding analysts' forecasting ability, I use analysts' firm-specific experience, size of their brokerage firm, and the number of industries they follow as proxies. Consistent with the model's prediction, I provide evidence showing that well-experienced analysts adjust more for earnings management while analysts following a greater number of industries adjust less for earnings management. Sensitivity analysis using analyst's historical firm-specific forecast accuracy as an alternative measure of forecasting ability further supports the hypothesis that analysts with better forecasting ability adjust more for earnings management. Moreover, analysts adjust less for earnings management when the inherent uncertainty of the reporting bias is greater. Specifically, analysts adjust less for earnings management when: (1) the past volatility of discretionary accruals is high; and (2) the firm has a marked propensity to smooth earnings. There is little evidence that affiliated analysts adjust less for earnings management than unaffiliated analysts. However, analysts adjust more for earnings management in the post-Reg FD period than in the pre-Reg FD period, which is consistent with Regulation FD achieving its objective of strengthening analysts' incentives to issue unbiased forecasts.