Recommendation-Forecast Consistency and Earnings Forecast Quality

Abstract

SYNOPSIS: We investigate the implications of recommendation-forecast consistency for the informativeness of stock recommendations and earnings forecasts and the quality of analysts' earnings forecasts. Stock recommendations and earnings forecasts are often issued simultaneously and evaluated jointly by investors. However, the two signals are often inconsistent with each other. Defining a recommendation-forecast pair as consistent if both of them are above or below their existing consensus, we find that 58.3 percent of recommendation-forecast pairs are consistent in our sample. We document that consistent pairs result in much stronger market reactions than inconsistent pairs. We show that analysts making consistent recommendation forecasts make more accurate and timelier forecasts than do analysts making inconsistent recommendation forecasts, suggesting that consistent analysts make higher-quality earnings forecasts. We extend the literature on informativeness of analyst research by showing that recommendation-forecast consistency is an important ex ante signal regarding both firm valuation and earnings forecast quality. Investors and researchers can use consistency as a salient, ex ante signal to identify more informative analyst research and superior earnings forecasts. Data Availability: All data are available from public sources.