Earnings Forecasting in a Global Stock Selection Model and Efficient Portfolio Construction and Management

Abstract

Stock selection models often use analysts’ expectations, momentum, and fundamental data. We found support for composite modeling using these sources of data for global stocks during 1997-2011. We found additional evidence to support the use of SunGard APT and Axioma multi-factor models for portfolio construction and risk control. Three levels of testing of stock selection and portfolio construction models were developed and estimated. We created portfolios for January 1997-December 2011. We report three conclusions: (1) analyst’s forecasts information has been rewarded by the global market from January 1997-December 2011; (2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow, and sales, and momentum in a stock selection model to identify mispriced securities; and (3) the portfolio returns of the multi-factor risk-controlled portfolios allow us to reject the null hypothesis for the data mining corrections test. The earnings forecasting variable dominates our composite model in terms of its impact on stock selection.