QF or ML research for QR?
Hello
I’ve been accepted into a top Math PhD program.
I would like later to move to quant research.
I’m thinking about two different research areas (topics from most to least prominent):
- Quantitative finance (robust optimisation, RL, model uncertainty, Bayesian sampling, stat arb, stochastic control)
- Theoretical ML (RNN, CNN, Transformer, sequence modelling, forecasting)
The first area could give me projects directly in finance and better connections/networking but I am noticing a lot of current job descriptions for QR referring more and more to ML research skills sometimes even creating different QR posting with one focused for ML researchers. Also the second area would keep me a door open to Tech.
Both of research areas are equally interesting for me.
1 Do firms specifically look for a certain specialised type of QR?
2 Are the first research area topics valid for QR alpha research? I just cant wrap my head around how robust optimisation could lead to profitable results being so natively conservative.