Are missing values important for earnings forecasts? A machine learning perspective

Abstract

Analysts' forecasts are one of the most common and important estimators for firms' future earnings. However, they are challenging to fully utilize because of missing values. This study applies machine learning techniques to estimate missing values in individual analysts' forecasts and subsequently to predict firms' future earnings based on both estimated and observed forecasts. After estimating missing values, forecast error is reduced by 41% compared to the mean forecast, suggesting that missing values after estimating are indeed useful for earnings forecasts. We analyze multiple estimation methods and show that the out-performance of matrix factorization (MF) is consistent using different evaluation measures and across firms. Finally, we propose a stochastic gradient descent based coupled matrix factorization (CMF) to augment the estimation quality of missing values with multiple datasets. CMF further reduces the error of earnings forecasts by 19% compared to MF with a single dataset.