Financial Analysts’ Understanding of Accounting Matching and Their Earnings Forecast Accuracy

Abstract

This study examines the relation between analysts’ understanding of accounting matching and their earnings forecast accuracy. By creating an innovative measure of analysts’ understanding of accounting matching, I find that financial analysts who better understand firms’ accounting matching produce more accurate earnings forecasts, and their earnings forecasts are more accurate than the earnings forecasts based on the historical expense-revenue relation. The findings suggest that better understanding of firms’ accounting matching helps financial analysts to determine the components of firms’ accounting numbers, facilitates analysts forecast decision process and reflects analysts’ valuable insights into the properties of firms’ earnings and their abilities to produce earnings forecasts with greater accuracy, and that since analysts use various information as input to forecast earnings, their forecasts are more accurate than the earnings forecasts that are solely based on the historical expense-revenue relation.