Forecasting Earnings Using k-Nearest Neighbors

We use a simple k -nearest neighbors algorithm (hereafter, k -NN*) to forecast earnings. k -NN* forecasts of one-, two-, and three -year -ahead earnings are more accurate than those generated by popular extant forecasting approaches. k -NN* forecasts of two- and three-year (one -year) -ahead EPS and aggregate three-year EPS are more (less) accurate than those generated by analysts. The association between the unexpected earnings implied by k -NN* and the contemporaneous market -adjusted return (i.e., the earnings association coefficient (EAC)) is positive and exceeds the EAC on unexpected earnings implied by alternate approaches. A trading strategy that is long (short) firms for which k -NN* predicts positive (negative) earnings growth earns positive risk -adjusted returns that exceed those earned by similar trading strategies that are based on alternate forecasts. The k -NN* algorithm generates an empirically reliable ex ante indicator of forecast accuracy that identifies situations when the k -NN* EAC is larger and the k -NN* trading strategy is more profitable.