Grigorios Papamanousakis is Deputy Head of Systematic Asset Solutions within Aberdeen Asset Management joining Aberdeen via the SWIP Acquisition in April 2014. His main responsibilities involve producing the quantitative investment framework for the SAA and TAA, as well as, quantitative client-centric absolute return strategies. Grigorios joined SWIP in January 2013 from Royal Bank of Scotland’s Economic Capital Modelling team where he worked on valuation, stress-testing and capital modelling of the global loan and credit portfolios of RBS Group. Grigorios holds a BSc and MSc in Applied Mathematics from the National Technical University of Athens and a dual MSc in Financial Mathematics from Heriot-Watt and University of Edinburgh. Grigorios is a PhD Candidate in Financial Modelling in the Department of Actuarial Mathematics and Statistics of Heriot-Watt University.
Machine Learning for Tactical Asset Allocation Decisions
On this presentation we describe how we use Machine learning for forecasting the relative performance of various asset classes (rates, credit, equities, commodities, etc.) from an asset management perspective. How we define the question to the machine based on different client risk profiles, performance targets and machine learning algorithms. We finally emphasize on the market factors selection, data cleansing, signal processing and high performance computing.