Task Complexity, Analyst Expertise and Accuracy of Earnings Forecasts

Abstract

Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

Citation

Ghosh, D. and Olsen, L. (2022), "Task Complexity, Analyst Expertise and Accuracy of Earnings Forecasts", Karim, K.E. (Ed.) Advances in Accounting Behavioral Research (Advances in Accounting Behavioural Research, Vol. 25), Emerald Publishing Limited, Leeds, pp. 103-130. https://doi.org/10.1108/S1475-148820220000025005