Earnings Prediction with Dupont Components and Calibration by Life Cycle

Soliman (2008) finds that separating return on net operating assets (RNOA) into DuPont components, profit margin (PM) and asset turnover (ATO), improves prediction of future RNOA. Dickinson (2011) finds that a firm’s life cycle stage explains changes in future RNOA. Vorst and Yohn (2018) find that life cycle calibration improves prediction more than industry grouping in prediction models that do not include the DuPont components. We unite and extend the above studies by using data updated since the early 2000’s and performing out-of-sample tests. We show that the DuPont components continue to improve prediction of one-year-ahead RNOA. Industry grouping and life cycle calibration using DuPont components improve prediction further. The improvement by life cycle calibration is stronger for mature companies, more R&D-intensive companies, less capital-intensive companies, and companies in less concentrated industries. Sell-side equity analysts and investors appear to initially rely more on basic prediction models than the expanded models that include DuPont components, industry grouping, and life cycle calibration. While there is some evidence of investor surprise associated with the expanded models, hedge portfolios formed based on the expanded model predictions do not produce abnormal returns