Summer associates in our Cross-Asset Quantitative Investment Strategy Research program will have the opportunity for up to three rotations during the program with the machine learning group within US equity strategy, quantitative credit strategy, rates and rate derivatives strategy, equity derivatives research and the quantitative systematic investment strategies team.
During the program summer associates will conduct primary research in areas that can include:
Applying advanced machine learning techniques to develop strategies for harvesting risk-premia and asset allocation
Investigate how markets are influenced by macro and sentiment factors using natural language processing and deep learning
Using data science tools for modelling rates curves and positioning, portfolio construction and forecasting market anomalies
Identify leading indicators of distress in credit markets and model inflection points in earning with real-time and big data sets
Model financial derivatives to identify systematically mispriced assets in order to build alpha generating trading strategies
Developing smart dynamic hedging solutions for more efficient risk management of client assets