Are All Forecasts Made Equal? Conditioning Models on Fit to Improve Accuracy

Abstract

We present a parsimonious method of improving forecasts and show that fit, the discrepancy between model forecasts and realized values, is persistent for individual stocks. Conditioning on fit profoundly affects the forecast error for future and out-of-sample returns. Forecasts of stock price direction with the best (worst) decile of historical fit are correct 63.6% (49.2%) of the time and are significantly different from the unconditioned model’s 56% accuracy. We find that superior factor forecasts are essential to profit from model conditioning and conclude that analysts who possess superior factor estimates can dramatically improve their forecasts through the technique we present.