The selection of an optimal portfolio plan is a gradual and iterative process. It involves multiple selection criteria working simultaneously. It is necessary to compare and weigh the advantages and disadvantages of each candidate plan to reach an optimal plan. Depending on different cases, different advanced algorithms may be applied together to reach an optimal plan, including rank and cut, linear programming, genetic algorithm, multi-objective algorithm, hierarchical clustering method, and quantitative risk analysis algorithm. The following section illustrates the different algorithms supported in Thales PMO product and their applicable scenarios:
- Rank and Cut
- Linear Programming
- Genetic Algorithm
- Multi-Objective Algorithm
- Hierarchical Clustering
- Quantitative Risk Analysis
For more insights on how to use each algorithm in different use cases, please download our Portfolio Optimization Algorithm Use Case memo.