Robust Optimization by Constructing Near-Optimal Portfolios

13 Pages Posted: 23 Oct 2017

Date Written: October 16, 2017


Many investors use optimization to determine their optimal investment portfolio. Unfortunately, optimal portfolios are sensitive to changing input parameters, i.e., they are not robust. Traditional robust optimization approaches aim for an optimal and robust portfolio which, ideally, is the final investment decision. In practice, however, portfolio optimization supports but seldomly replaces the investment decision process. In this paper, we present an approach that both solves the robustness problem and aims to support rather than replace the investment decision process. The method determines a region with near-optimal portfolios which, especially in light of the robustness problem, are all good allocation decisions. Then, as is already common practice, an investor can bring in expert opinion or additional information to select a preferred near-optimal portfolio. We will show that the region of near-optimal portfolios is significantly more robust than the optimal portfolio itself.

Keywords: Robust optimization, Portfolio optimization, Support vector machines, Near-optimal allocations, Asset allocation

JEL Classification: C61, G11

Suggested Citation

van der Schans, Martin and de Graaf, Tanita, Robust Optimization by Constructing Near-Optimal Portfolios (October 16, 2017). Available at SSRN: or

Martin Van der Schans (Contact Author)

Ortec Finance ( email )

Orly Centre
Barajasweg 10
Amsterdam, 1043 CP

Tanita De Graaf


Houtsingel 5
PO Box 75
Zoetermeer, 2700 AB

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