Sam Radnor joined quantPORT in 2016 as a Quant Researcher concentrating on developing systematic models and trading systems. His focus is on non-traditional data sets; specifically, the use of proprietary data collected by the group from their expansive broker network and other non-standard data for trading liquid equities. Prior to joining quantPORT he was a portfolio manager at AHL | Man Group where he ran a variety of quantitative strategies having entered the hedge fund industry in 2007. Areas of particular interest are machine learning, handling non-standard data and portfolio construction.
Sam holds a PhD from Imperial College London in Theoretical Physics where his research covered modelling few-cycle pulses in Nonlinear Optics.
Link to paper shortly to appear in The Journal of Financial Data Science
“Modelling Analysts’ Recommendations via Bayesian Machine Learning”
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3269284