Analyst's earnings forecasts and earnings revisions are well-known drivers of stock price movements and are widely applied in both academic research and quantitative stock selection models. This generic investment signal recommends to go long the stocks that recently have been upgraded by analysts and to short the stocks that recently have been downgraded.
Robeco continuously works on improving its models' signals and definitions. This internship's objective is to dive deeper into a detailed analyst forecast database, preferably from different angles, and test some innovative ideas presented in both the academic literature and broker reports. Examples of these ideas include, but are not limited to:
This project involves all steps of the quant model development cycle. For this project, we envision the following steps:
This project's preferred candidate has a solid background in econometrics, statistics, or mathematics, with know-how or willingness to dive deep into machine-learning techniques. Additionally, we require good programming skills (preferably in Python) as well as interest and enthusiasm for financial markets.
Chan, Jegadeesh, and Lakonishok (1996) “Momentum strategies”, Journal of Finance 51
Bew, Harvey, Ledford, Radnor, and Sinclair (2019) “Modeling analysts' recommendations via Bayesian machine learning”, Journal of Financial Data Science 1(1)