Giuliano De Rossi heads the European Quantitative Research team at Macquarie based in London. He joined from PIMCO where he was an analyst in the Credit and Equity Analytics and Asset Allocation teams. Prior to this he worked for six years in the Quant research team at UBS. He 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 and downside risk. His academic research has been published in the Journal of Econometrics and the Journal of Empirical Finance.

How Machine Learning Can Help Stock Pickers
We consider a new approach to analyse the vast amount of information available about the portfolio positions of institutional investors over time. Our goal is to use machine learning to analyse the stock picks of active equity funds. Recommender systems have been employed for a wide range of applications: Suggesting books, hotels, scientific papers and even new social connections. Here we aim to identify stocks that are likely to be bought by a given portfolio manager based on his or her own existing stock picks and the recent trading activity of other investors. By aggregating the results we seek to build a new signal related to the institutional demand for a given stock analysed by Koijen and Yogo (2016).

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