Abstract
AbstractThis paper examines the advantages of aggregate measures of analyst characteristics to researchers and investors interested in explaining differences in analysts' forecasting performance. We show while single‐characteristic and factor‐based measures reflecting attributes such as forecasting experience, access to resources and portfolio complexity vary significantly in the extent to which each explains analyst forecasting performance, equal‐weighted composite measures based on single characteristics or on factors extracted from those characteristics are consistently associated with forecasting bias arising from a range of indicators of reduced earnings quality. These aggregate measures of analyst characteristics require no additional data beyond traditional archival sources and offer a useful method of testing the impact of analyst characteristics on their forecasting performance.
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