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Using an in-depth analysis of data through statistical and mathematical methods and machine learning techniques, we identify investment opportunities in liquid assets in equities, credit, foreign exchange and commodity futures.
Our focus is on a rigorous process of research, systematic observation, data collection and experimentation, formulating an investment hypothesis and then backtesting, using multiple approaches, to stress-test the strategies under different market scenarios.
With over 25 years of experience in structuring, marketing and trading interest rate and credit products, Nazzaro has worked in Zurich, London and New York. Previously, he worked as an independent advisor to Apollo Global Management in emerging market sovereign credit strategies. His career has taken him to senior roles in Jefferies LLC, Merrill Lynch, Deutsche Bank and Bear Stearns. He started his career as a trainee at Swiss Bank Corporation in Zurich and London.
Nazzaro holds a BSc (Hons) in Economics and Mathematical Sciences from the Open University and a BA (Double Hons) in Modern Languages from the University of Manchester.
Emiliano is currently completing his Ph.D. at the Universitat Politècnica de Catalunya, with a thesis on high fidelity simulations for hydrogen combustion assisted with machine learning techniques. As a data scientist, he has also worked on and has taught applying data-driven methods for turbulent combustion while a researcher at the Barcelona Supercomputing Center. Prior to moving to Spain, Emiliano worked at Andes Wealth Management, a large multi-family office based in Buenos Aires, where he developed portfolio optimization methods using machine learning data-driven decisions. He also developed an algorithm for QuTiP (the largest python repository for quantum physics) as part of a unitary fund grant.
He holds a Masters degree in Physics from the University of Buenos Aires.
Augusto is a senior researcher at the Department of Computational Applications for Science and Engineering (CASE) at the Barcelona Supercomputing Center. His work involves the development of physical models and numerical methods for multi-physics applications related to turbulent flows, combustion, multiphase flows, heat transfer, computational aerodynamics, and high-performance computing (HPC), with a particular focus on GPU programming and parallel computing in heterogeneous systems. He has also created applications to developing automated trading algorithms, and simulation testing.
He holds a Ph.D. in Civil Engineering from the University of Barcelona, specializing in the stabilization of the Navier-Stokes equations for free surface, non-hydrostatic, and incompressible flows using the finite element method and a master’s degree in Numerical Methods in Engineering also from the University of Barcelona.
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2023 | “Forsyte Quantitative Strategies”, “Forsyte” are trade names of Forsyte Investment and Trade, S.L. (collectively known as “Forsyte” - trademark pending).
Photographs on this website are from Agnieszka Piatkowska