Forecasting company earnings
Robeco has been a pioneer in the field of quantitative stock selection since the early ‘90s. In 1994 the first stock selection models were used in Robeco equity strategies. Following the success of these models in practice, in 2002 Robeco launched a 100% quantitative equity product line. This expanded over the years, currently spanning a wide range of investment strategies, with different regional exposures and risk-return characteristics.
Of course, we continuously try to improve our strategies. One of the focus areas is to improve generic variable definitions. A popular generic value variable is “Forward Earnings to Price” a measure that compares analysts’ earnings forecasts for a company with its current stock price. However, research shows that analysts’ earnings forecast are biased and proposes alternative methods to forecast profitability (see Hou et al., 2012, or So, 2013).
The main purpose of this project is to develop a proprietary earnings forecast model. You will conduct a literature overview and investigate documented and new factors that could predict future earnings. Once these factors are identified, the question is whether they lead to earnings forecast that are superior to analyst forecasts or trailing earnings: i.e. is the predicted earnings to price yield more effective for quantitative stock selection than traditional earnings to price measures. The starting point of this project could be to replicate the earnings forecast model in Hou et al. (2012).
The project covers the entire quant model development cycle: conducting a literature review, analyzing the data, programming the back-tests, analyzing the results, discussing results with researchers and portfolio managers, writing a research report and giving a presentation. As with all Super Quant internships, the assignment will be supervised by an experienced empirical researcher of Robeco’s Quantitative Strategies department. Practical feedback will be provided by several equity portfolio managers. Creative, analytic and programming skills are essential in order to successfully complete the project.
Gerakos, J., & Gramacy, R. B. (2013). Regression-based earnings forecasts. Chicago Booth Research Paper.
Hou, K., Van Dijk, M. A., & Zhang, Y. (2012). The implied cost of capital: A new approach. Journal of Accounting and Economics, 53(3), 504-526.
So, E. C. (2013). A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?. Journal of Financial Economics, 108(3), 615-640.