Giuliano is an Executive Director in the Quantitative Execution Services team at Goldman Sachs. Prior to this, he worked at Macquarie and PIMCO. He also spent six years in the Quant research team at UBS. Giuliano has a PhD in economics from Cambridge University, and worked for three years as a college lecturer in economics at Cambridge before joining the finance industry on a full-time basis. Giuliano’s Masters degree is from the LSE and his first degree is from Bocconi University in Milan. He has worked on a wide range of topics, including pairs trading, low volatility, the tracking error of global ETFs, cross asset strategies, downside risk and applications of machine learning to finance. His academic research has been published in the Journal of Econometrics and the Journal of Empirical Finance.
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.