Relative accuracy of analysts’ earnings forecasts over time: a Markov chain analysis

Abstract

The main purpose of this paper is to analyze the time patterns of individual analysts’ relative accuracy ranking in earnings forecasts using a Markov chain model. Two levels of stochastic persistence are found in analysts’ relative accuracy over time. Factors underlying analysts’ performance persistence are identified and they include analyst’s length of experience, workload, and the size and growth rate of firms followed by the analyst. The strength and the composition of these factors are found to vary markedly in different industries. The findings support the general notion that analysts are heterogeneous in their accuracy in earnings forecasts and that their superior/inferior performance tends to persist over time. An analysis based on a refined measure of analysts’ forecast accuracy ranking that strips off firm-specific factors further enhances the empirical validity of the findings. These findings provide a concrete basis for researchers to further explore why and how analysts perform differently in the competitive market of investment information services.