Estimating private information usage amongst analysts: evidence from UK earnings forecasts

Abstract

This paper introduces a methodology for estimating the likelihood of private information usage amongst earnings analysts. This is achieved by assuming that one group of analysts generate forecasts based on the underlying dynamics of earnings, while all other analysts are assumed to issue forecasts based on the prevailing consensus forecast. Given this behavioural dichotomy, we are able to derive (and estimate) a structural econometric model of forecast behaviour, which has implications regarding the determinants of analysts' private information endowments and forecast accuracy over the forecast horizon.

Local versus foreign analysts' forecast accuracy: does herding matter?

“…Finally, H 0 : γ 3 + γ 4 = 0, H 0 : γ 3 + γ 5 = 0 and H 0 : γ 3 = 0 in Table 4 report the coefficient difference and significance between the bold forecasts and herding forecasts of local, expatriate and global, respectively. As expected, they are all negative and significant, indicating that bold forecasts are significantly less accurate than herding forecasts on average (Salamouris and Muradoglu, 2010;Mira and Taylor, 2011).…”

“…Specifically, their lack of resource endowments may force them to mimic the behaviour of others. In so doing they may be able to produce more accurate forecasts (e.g., Mira and Taylor, 2011). In addition, local analysts in emerging markets are less confident compared to foreign analysts when making earnings forecasts.…”

“…There is well-documented evidence that analysts tend to herd when making earnings forecasts or stock recommendations (see, e.g., Welch, 2000;Gleason and Lee, 2003;Clement and Tse, 2005;Jegadeesh and Kim, 2010;Lee and Lee, 2015;Frijns and Huynh, 2018;Lin, 2018), and that such behaviour leads to improvements in the quality of forecasts (see, e.g., Ke and Yu, 2006;Salamouris and Muradoglu, 2010;Mira and Taylor, 2011). 3 However, there are no studies in the field that examine whether the propensity to herd can be explained by the location and employment characteristics of analysts.…”

“…Clement and Tse (2005) and Huang et al, (2017), for example, use US data and find that bold forecasts are more accurate than herding forecasts. However, Salamouris and Muradoglu (2010) and Mira and Taylor (2011) use UK data and show that bold forecasts are less accurate than herding forecasts. To the best of our knowledge, there are no prior studies exploring the impact of boldness on the accuracy of forecasts in an international or Asian setting.…”