Which project or research challenge at Aspect has been the most rewarding for you so far?
The projects I have enjoyed the most are those that show how theoretical ideas can be used in real-world situations. For example, applying stochastic calculus to model trading strategies or using operations research methods for building portfolios both highlight how theory and practice can work together.
What emerging themes in systematic investing do you find most fascinating right now?
One aspect I find intriguing is how, following the boom in Machine Learning and AI, systematic investing has chosen to incorporate these techniques in practice. Specifically, the use of ML for feature selection, rather than solely for prediction, is interesting and serves as a bridge between alternative data and systematic investing.
How do you see technology shaping the future of quantitative research and portfolio construction?
Technology is central to both systematic investing and Aspect. Because of this, I believe that technological expertise is essential for today’s quant researchers. As computationally intensive approaches like machine learning and general optimisation become more common, it is crucial to understand and leverage technology effectively.
