To this end, we introduce in our recent research paper a multi-objective investment framework which we deem ‘3D investing’. We demonstrate that such framework results in the ‘best possible’ solution when jointly considering more than two portfolio objectives.
The new paper by David Blitz, Mike Chen, Clint Howard and Harald Lohre demonstrates how traditional mean-variance portfolio optimization can be enhanced by adding sustainability as a third goal, using carbon footprint and SDG (see Figure 1 below) as examples. We find the 3D investment approach generally outperforms the traditional 2D model with sustainability constraints. Our historical simulations show that 3D investing yields higher sustainability metrics and expected returns than a constraint-only method. However, using constraints in sustainable investing still has merit. A combined strategy, blending a flexible sustainability constraint with integrating sustainability into the optimization process, offers a balance between return, risk, and sustainability goals. For aggressive sustainability aims, the 3D approach, which explicitly incorporates sustainability alongside alpha and risk, is ideal.
Figure 1: 3D vs 2D investing performance when targeting higher SDG scores
Source: Robeco. The graph shows that when targeting higher SDG scores above the benchmark, the 3D investing approach is superior to using a portfolio constraint from an after-cost return perspective. This is especially true when targeting more ambitious sustainability goals in lower tracking error quant portfolios .
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