Our research into sustainability integration in quantitative investment strategies shows that such strategies lend themselves well to integrating secondary objectives, such as reducing carbon intensity, next to the primary objective of realizing attractive risk-adjusted returns. This is due to the large investment universe that the strategy can screen for what we view as the most attractive opportunities.
If a company with high carbon emissions issues a bond with attractive factor scores, the portfolio construction algorithm is often able to find a bond with similar factor scores, but with lower carbon intensity. This efficiently tilts the portfolio towards bonds of more sustainable companies, without having to forgo a lot of factor exposure.
We assessed the performance impact of imposing a constraint on the carbon intensity of our global multi-factor credit and global multi-factor high yield strategies, over a 1994-2019 backtest period.2 The constraint specified that the weighted average carbon intensity of the portfolio should be lower than that of the benchmark. The impact on the backtested performance was limited to a few basis points per annum. We have also researched more ambitious carbon reduction targets, e.g. 10% lower carbon intensity than the benchmark, or 20%, 30%, etc., and found that these are associated with modest reductions in the expected outperformance of the strategies.
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One may wonder why, in this backtested model, reducing the carbon intensity of the multi-factor strategies came with only a limited reduction in outperformance, even though imposing constraints in a factor strategy generally led to lower factor exposures. We think there are three reasons for this:
We believe that the described approach helps to reduce transition risks in the portfolio, while still providing attractive risk-adjusted return potential.
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