Can we Automate Earnings Forecasts and Beat Analysts?

Can we design statistical models to predict corporate earnings which either perform as well as, or even better than analysts? If we can, then we might consider automating the process, and notably apply it to small and international firms which typically have either sparse or no analyst coverage. There are at least two challenges: (1) analysts use real-time data whereas statistical models often rely on stale data and (2) analysts use potentially large set of observations whereas models often are frugal with data series. In this paper we introduce newly-developed mixed frequency regression methods that are able to synthesize rich real-time data and predict earnings out-of-sample. Our forecasts are shown to be systematically more accurate than analysts' consensus forecasts, reducing their forecast errors by 15% to 30% on average, depending on forecast horizon.