Andreas works as a Quantitative Researcher at Goldman Sachs Quantitative Execution Services, with an emphasis in machine learning techniques for execution algorithms. Andreas has a PhD in Information Engineering from the University of Cambridge, focusing on the interface of stochastic control theory and Bayesian machine learning. Andreas’ teaching experience included several engineering undergraduate courses, including Inference and Machine Learning, Linear Algebra, Probability, Control and Signal Processing. During his PhD, he has also worked at Informetis Europe as a Machine Learning Algorithm Engineer, developing efficient Bayesian inference techniques for smart electricity meter applications. Andreas also holds a BA and an MEng degree in Electrical and Information Sciences from Trinity College, University of Cambridge, during which he received the G-Research and The Technology Partnership (TTP) awards. His Master’s thesis was in collaboration with British Cycling, developing a racing cyclist fitness predictor.

Modelling intraday risk and flow co-movement to improve trading performance
Markets around the globe exhibit strong varying intraday characteristics. As a consequence, modelling the underlying intraday market dynamics is crucial in optimising trading execution. In this talk, we discuss the effect that modelling intraday flow co-movement and intraday risk have in creating optimal trade schedules, while also taking into consideration the individual stock’s market microstructure, providing useful insights. Our methodology relies on unsupervised learning techniques to identify the most important drivers of intraday market dynamics at stock level.

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