Prior research has shown improvements in analysts' forecast accuracy around various events (e.g. new disclosure regulations or cross-listings), but these studies do not consider a change in the composition and ability of analysts providing forecasts over time. By studying foreign firms cross-listing on U.S. stock exchanges, we find that analyst composition changes by over 50 percent during the three-year period around cross-listing. We show that cross-listing is associated with a shift away from analysts who are less accurate forecasters and toward analysts who are more accurate forecasters. This shift in analyst composition accounts for a significant improvement of 9.5 percent in analyst forecast accuracy. In addition, we document that changes in both analyst ability and public information disclosure affect analyst forecast accuracy around cross-listing. Our results indicate that researchers should control for changes in analyst composition and ability when measuring the impact of specific events on analyst forecast accuracy.

This paper reports the results of an experiment that examines how analyst forecast accuracy (i.e., how close an analyst's forecast is to realized earnings) and forecast boldness (i.e. how far the analyst's forecast is from the consensus forecast) affect the analyst's perceived credibility and investors' willingness to rely on and purchase the analyst's future reports. We hypothesize and find that forecast boldness magnifies the effect of forecast accuracy on these variables. That is, analysts who provide accurate, bold forecasts experience more positive consequences than those who provide accurate, non-bold forecasts, and analysts who provide inaccurate, bold forecasts experience more negative consequences than those who provide inaccurate, non-bold forecasts. We also find that these effects are not symmetric - the negative consequences of being bold and inaccurate exceed positive consequences of being bold and accurate. Our results are not sensitive to the level of the analyst's prior reputation.

This study examines the impact of unidentifiable individual differences among financial analysts on the cross section of their earnings forecast accuracy. Various psychological factors, such as decision style and personality traits, are documented to impact individuals' decision making. However, analysts' individual differences in such psychological factors are not captured by identifiable personal attributes employed in finance literature, such as years of experience. In this paper, we employ the concept of analyst fixed effects to control for unidentifiable individual differences. Examining the factors related to forecast accuracy, we document that controlling for these unidentifiable analyst-specific effects improves model fitting, and changes the explanatory power of some of the traditionally used independent variables in the literature. We confirm that the analyst's firm-specific experience, the intensity of following that a firm receives, and the forecast horizon are all significantly and consistently related to forecast accuracy. However, we find that analyst general experience and coverage complexity lose explanatory power when individual differences are controlled for. Furthermore, we document that analyst general experience is not monotonically associated with better accuracy, and that analysts only benefit from increased general experience during the early to middle stages of their career. Finally, we observe that when analysts' individual differences are controlled for, the boldness of a forecast revision is no longer a significant determinant of the improvement of accuracy. This is one of the first studies to highlight the necessity of recognizing individual differences among financial analysts. We argue that this treatment advances the literature of analyst forecast performance, and closely relates financial agents' decision making to psychology theories of decision style and personality.

Facing limited attention constraints, financial analysts must strategically choose which information to pay attention to and which information to ignore when making earnings forecasts. I rely on rational inattention theory to develop and test hypotheses on factors that determine analyst attention and how analyst attention affects forecast accuracy. I construct a novel measure of attention that varies across stocks followed by the same analyst during the same fiscal period. I find that analyst attention is decreasing in the marginal cost of paying attention and that analysts allocate attention to firms with earnings that are difficult to forecast, suggesting that analysts suffer from a limited attention constraint. Importantly, I find that attentive analysts are more accurate, and the effect of attention is larger for inexperienced analysts and stocks with highly volatile earnings. Finally, I find that attentive analysts are less likely to revise forecasts upwards and are less optimistic even after controlling for firm information environment and analyst ability.

This research will concentrate on the changes in earnings forecasts, forecast accuracy and forecast dispersion for growth and value stocks after Reg FD. Each topic is presented in a separate essay. The first essay tests if growth and value stock returns respond more to forecasted earnings changes than they do to changes in earnings and whether these stock returns respond in a different fashion before and after Reg FD. This phenomenon is stronger for growth stock portfolio strategies than it is for value stock portfolios. After Reg FD, the overall impact of earnings expectations on stock returns is smaller, especially for growth stock returns. The second essay examines financial analysts' earnings forecast accuracy in value and growth stocks before and after the introduction of Reg FD. Accuracy for both stock groups (value and growth stocks) has improved after the introduction of Reg FD. The results in this essay provide additional evidence indicating that analysts did not just misinterpret available news but consciously tried to maintain relationships with managers. However, Reg FD efficiently limited these relationships between managers of growth firms and analysts so that the monetary advantage from manipulating earnings forecasts before the introduction of Reg FD no longer exists. The third essay evaluates the hypothesis stating that forecast dispersion, on both growth and value stock returns, has increased after the introduction Reg FD. However, the increased dispersion found at the second quarter of 2001 drastically dissipates at the second quarter of 2002, although value stock forecast dispersion before earnings announcement and value stock belief jumbling remain higher. The results in this essay suggest that corporate voluntary disclosure created a greater variety of opinions and, therefore, more uncertainty about value stocks. Also, value stock returns have a stronger inverse relationship with dispersion because financial analysts have become more uncertain about value firms' performance. The bigger the disagreement about a stock's value, the higher the market price relative to the true value of the stock, and the lower its future return.