Portfolio optimization models are used extensively in finance to determine how to best re-balance a portfolio, such that maximum returns can be achieved at minimum risk.
Convex optimization has been the norm for tackling these problems since their introduction by Harry Markowitz in 1952. However, the world has evolved, and the financial industry has long desired to extend this model with more complex mathematics to better describe the real world. This has not been possible, until now.
We are thrilled to introduce the finance plugin for the Octeract global solver, which can solve numerous difficult mathematical structures to global optimality. The solver can impose the following constraints:
- Cardinality constraints (integer). The solver is able to calculate the globally optimal number of stocks for the re-balanced portfolio without any user intervention.
- Tracking error constraints (non-convex). Active funds have long had difficulties in determining the global sweet spot between risk and returns. Octeract's proprietary algorithm empowers its users by allowing them to navigate the solution space with unparalleled precision.
- Active share constraints (discontinuous/non-smooth). The active share is particularly important for active funds, as research has shown that funds with high active shares tend to out-perform the index. Octeract technology provides the capability to identify the globally optimal active share, for the first time. This is particularly useful for active funds, as now they are able to, not only have greater potential of out-performing the index, but also to be in a position of proving to their clients that they are not simply doing passive tracking.
We are currently accepting orders and offering trials upon request. For more info, contact us at: firstname.lastname@example.org.