Adjusting for earnings volatility in earnings forecast models

Abstract: Previous research provides evidence for the negative relation between earnings volatility and earnings forecasting. This paper examines if earnings forecast models can adjust for firms’ earnings volatility and improves the forecasts by choosing a specific estimation method and a specific forecast model. The sample is divided into quartiles based on the firms’ earnings volatility, to examine if the choice of estimation method, the full sample (FS) or the first quartile (Q1) method, and the choice of forecast model, the ones by Hou et al. (2012) and Clubb and Wu (2014), matter depending on the firms’ degree of earnings volatility. The forecasts on US firms are compared based on bias and accuracy over the period 2000-2010. The results confirm the negative relation between earnings volatility and earnings forecasting. Furthermore, the choice of estimation method proves to be a way to account for earnings volatility, where the FS method shows to give better forecasts for the highest volatility firms while the Q1 method is to prefer for the lower volatility firms. The choice of model appears to not depend on earnings volatility except for the model by Clubb and Wu (2014) that works better for the lower volatility firms when using the Q1 method.