Accuracy of Analysts' Earnings Estimates

Abstract: This thesis investigates consensus and individual analyst firm accuracy in forecasts of earnings per share (EPS) for U.S. stocks in 2009–2018. Moreover, we investigate if the analysts’ forecasting predictiveness is affected by the size of the company which is observed. Finally, we examine if differently weighted models can beat an equally weighted consensus forecast. We find statistical evidence that analysts’ forecasts of EPS have predictive power. Furthermore, we find that the size of a company impacts the predictive ability of analysts. Analysts of larger companies, included in S&P 500, are more accurate in their forecasts, relative to analysts of smaller companies, included in Russell 2000. Finally, after categorizing the analyst firms by predictiveness, we create models to explore the possibility of beating the average with weighted combinations. Our results show that none of the suggested models are statistically significantly different from the consensus and therefore we cannot conclude that differently weighted models outperform an equally weighted consensus.