Expertise
Portfolio Construction & Investment Data Science
9 Professionals
13 years average experience
Trieste
Our portfolio construction and investment data science capabilities span strategic and tactical asset allocation, style and factor analysis, the design of customised solutions for our clients, and the exploration of AI and machine-learning models to strengthen our entire investment offering and exploit risk-adjusted returns.
We use in-house market analysis and capabilities to optimise and implement portfolio construction. Our analysis and solutions can all be enriched through our ability to perform liability projections, CFM simulations, SCR estimates, what-if scenarios and back-testing. We can break down portfolio risk and returns according to statistical and fundamental factors.
Meanwhile, our investment data science team develops AI and machine-learning in order to optimise our research, investment, reporting, ESG and risk processes.
Enrico Scarin
Head of Portfolio Construction & Investment Data Science
Industry experience: 18 years
We define Strategic Asset Allocation which combines clients’ needs and market expectations to target the long-term risk/ return profile
AA portfolio construction is based on optimization studies and Efficient Frontier analysis and is performed with dedicated tools developed internally
Source: Generali Asset Management S.p.A. Società di gestione del risparmio, data as at 30.06.2023.
Using in-house market analysis, we optimize ex-ante Tactical Asset Allocation (TAA) tilts to exploit risk-adjusted short-term expected returns
TAA implementation, monitoring and continuous steering of Model Portfolios are performed accordingly
We design customized solutions for LDI/CDI clients
Specific KPIs/KRIs as well as regulatory capital constraints are embedded in the analysis
Every study can be enriched through liability projections, CFM simulations, SCR estimates, what-if scenarios and back-testing
We can break down portfolio risk and returns according to statistical and fundamental factors
Ex-ante/post factorial exposure analysis
Consistency check of factor exposure implementation
Our Data Science unit explores and develops AI & ML models to strengthen the whole offering
Main initiatives cover NLP algorithms, data-driven TAA inputs through ML algorithms, web application, database designing and services in Cloud
