Multi-objective portfolio optimisation
Problem
Organisations use portfolio analysis to examine how their business activities and investment or stock portfolios contribute to their results. For optimal risk and return distribution, scenarios are calculated and compared with objectives. As more variables are added or the analysis period is extended, these scenarios become more complex. This can lead to a large problem that is difficult to calculate or needs to be simplified, potentially resulting in suboptimal analysis.
Solution
TNO, together with Rabobank, investigated whether quantum computing can help optimise loan portfolios. In this case, financial objectives of the global loan portfolio in the agriculture sector were combined with environmental and sustainability goals. "We wanted not only a traditional analysis of revenue, profit, and return, but also to see the impact of our portfolio on the Paris climate goals," says Mischa Vos of Rabobank. The problem was reformulated as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. Two QUBO formulations were presented, each with a different focus.
Benefit
Mischa Vos: “The result pleasantly surprised us. We quickly had an analysis that can serve as a basis for future decisions.” Frank Phillipson of TNO was also satisfied: “This practical example shows the potential of quantum computers.” Vos adds: “The board decides, but Rabobank wants to contribute to the 2035 climate goals. Our portfolio plays a crucial role in this. What do we invest in and what do we not?”
Read more about the results in this article and see how quantum computing can tackle complex optimisation problems in the financial sector. It also highlights the potential of quantum computing for more efficient and robust portfolio analyses. Or watch the video.
