Robert Luce

Mathematical portfolio optimization is a tool for maximizing the expected return, minimizing the risk, or optimizing related measures for a portfolio of investments. Quantitative analysts and portfolio managers use portfolio optimization software to support them in making investment decisions. With Gurobi’s MIP technology it is possible to incorporate discrete decisions in the portfolio selection, like fixed costs, transaction limits, and cardinality constraints. The effectiveness of backtesting with discrete constraints is heavily dependent on the solver performance: The more strategies and scenarios can be run, the more alpha. In this session we will share best practices for modeling, implementing and tuning discrete decision models for optimal performance.