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

Young‐Soo Choi, Svetlana Mira and Nick Taylor

Accounting and Finance, 2022, vol. 62, issue S1, 1143-1188

Abstract: The aim of this paper is to compare the information and resource endowments possessed by different analyst types, classified by both location and employment within the context of seven emerging Asian markets. Our results show that local analysts are more accurate than expatriate and global analysts when we consider all earnings forecasts. However, when we control for the segregated herding behaviour of analysts, we find that herding local forecasts are one of the least accurate compared to other herding forecasts. By contrast, bold local (that is, non‐herding local) forecasts are more accurate than all other bold forecasts. This suggests that the information endowment of bold local analysts is superior to the information and resource endowments of bold expatriate analysts. We show that the superior accuracy of bold local forecasts does not stem from business group affiliations, investment banking relationships, demand for local analysts’ services or the specialisation of analysts vis‐à‐vis countries or sectors. We consistently find that bold local analysts are better at assessing the earnings of the firm they forecast. Our results show that the prior documented advantage of local analysts in terms of forecasting accuracy is driven by the bold local analysts, with herding locals diluting this effect. To the best of our knowledge, this is the first study to explore the segregated herding behaviour of local, expatriate and global analysts, and its impact on relative forecast accuracy across analyst types.

Date: 2022
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