Learning Whom to Trust: Machine-Learning Aggregation of Analyst Earnings Forecasts

Analyst consensus forecasts are widely used in investment decisions and as proxies for market expectations, yet they typically assign equal weight to component forecasts despite predictable differences in quality. We develop a constrained machine-learning framework that predicts each forecast’s relative quality from 29 ex ante analyst, forecast, broker, firm, and consensus characteristics and converts those predictions into positive, normalized weights. Over 21 sequential out-of-sample test years (2005–2025), the quality-weighted consensus reduces the late-horizon mean absolute error by 11.5% relative to the conventional mean and outperforms it in 62.9% of firm-years and every test year. Features locating a forecast within the contemporaneous distribution provide the largest incremental signal, while analyst track records, revisions, and broker attributes add further information. Improvements are larger when analyst coverage and forecast dispersion are high. The results show that forecast quality is inherently relative and that constrained machine learning can materially improve the consensus forecast accuracy by assigning context-dependent weights to individual forecasts based on their characteristics and the composition of the forecast set.