Distributed Optimisation of a Portfolio's Omega

We investigate portfolio selection with an alternative objective function in a distributed computing environment. More specifically, we optimise a portfolio's 'Omega' which is the ratio of two partial moments of the portfolio's return distribution. Since finding optimal portfolios under such a performance measure and realistic constraints is a non-convex problem, we suggest to solve the problem with a heuristic method called Threshold Accepting (TA). TA is a very flexible technique as it requires no simplifications of the problem and allows for a straightforward implementation of all kinds of constraints. Applying the algorithm to actual data, we find that TA is well-capable of solving this type of problem. Furthermore, we show that the computations can easily be distributed which leads to considerable speedups.

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