مقاله انگلیسی رایگان در مورد تغییرات پوشش و دقت پیش بینی سود- Sage 2018

The forecast of earnings per share (EPS) is a key ingredient to security valuation models, and there is a long-standing interest in the determinants and characteristics associated with EPS forecast accuracy.1 Prior research has found that financial analyst characteristics such as past forecast accuracy, forecasting experience, number of firms followed, and the size of their broker firm affect forecast accuracy (e.g., Brown, 2001; Clement, 1999; Clement & Tse, 2005; Jacob, Lys, & Neale, 1999). In this article, we investigate whether forecast accuracy is higher or lower when an analyst adds or drops coverage for a firm (i.e., their first and last forecast), relative to their peer analysts.2 The effect on EPS forecast accuracy when analysts add or drop coverage is contentious. On one hand, Mikhail, Walther, and Willis (2003) and Jacob et al. (1999) find that higher firm-specific experience generally correlates with higher forecast accuracy. This suggests that the first forecast of an analyst would be less accurate, whereas their last forecast would be more accurate. On the other hand, McNichols and O’Brien (1997) predict the opposite result. They hypothesize that analysts exert extra effort when issuing their first forecast, whereas their last forecast is associated with sample selection bias (i.e., analysts drop coverage when they are no longer accurate). Consistent with their hypothesis, they find that ‘‘forecasts for newly added stocks are more accurate . . . while forecasts for dropped stocks are less accurate’’ (p. 187). We find that both streams of conflicting literature are only partially correct (and partially wrong). Using analysts’ quarterly earnings forecasts from the Institutional Brokers’ Estimate System (I/B/E/S) database from 1985 to 2012, we find the forecast accuracy to be lower in both cases. That is, when an analyst adds or drops coverage for a firm (i.e., their first and last forecast), their forecast accuracy is generally lower relative to their peer analysts. Our research design differs from McNichols and O’Brien (1997) in the following three ways: First, we use a paired-sample analysis, where we compare the forecast accuracy between an analyst and his peer analysts for the same firm and at the same time. Thus, our results are not affected by confounding firm effects or year effects. Second, our results are robust to both univariate and multivariate regression analyses, which takes into account various analyst characteristics affecting forecast accuracy. As a contrast, McNichols and O’Brien (1997, Table 4) use only simple univariate analysis and unpaired two-sample test.3 Third, following Clement and Tse (2005), we scale our forecast accuracy variable to range between 0 and 1. Thus, our results are less susceptible to extreme outliers. Otherwise, the mean forecast accuracy (when aggregated across firms) will be skewed toward the subset of samples with high absolute forecast error. In addition, we consider various alternative explanations for our findings: First, we examine whether our finding of less accurate forecasts from analysts who add or drop coverage is related to the rookie or retiring analysts. We define a forecast as made by a rookie (retiring) analyst if that forecast is made in the first (last) year that the analyst appears in the I/B/E/S database.