Introducing a portfolio analysis and construction philosophy, consistent with the academically validated principles of factor investing, and integrating finance and sustainability into an intuitive framework.
Portfolio construction is a powerful financial technology that investors can leverage to access more efficient investment opportunities in the face of uncertainty and to implement their financial and extra-financial preferences. Efficiency is largely obtained thanks to the benefits of diversification, allegedly the only free lunch in finance. However, as useful as diversification may be, it alone is not sufficient to construct a portfolio because some sources of common risk cannot be fully diversified away. Identifying and quantifying these common risks, generally called systematic risks, requires knowledge of the correlation between stocks. Additionally, investors searching for efficient investment opportunities need to also identify robust sources of long-term performance to inform their portfolio selection and allocation processes. Absent any market anomaly, the robust sources of performance should be found precisely amongst the aforementioned systematic risks, because investors require a premium for bearing those risks that cannot be fully diversified away and that hurt a portfolio mostly in bad times.
A large body of financial research on asset pricing and factor investing is available to guide investors in their search for efficient portfolios and, in line with academic literature, Scientific Portfolio considers factors as the optimal means to clearly visualize the drivers of both risk and potential long-term performance in an equity portfolio. Every systematic risk factor is characterized by its ability to explain the common variations in stock returns, but academia distinguishes between those that are expected to generate excess returns in the long run, we call them fundamental risk factors, and those that are not known to attract a risk premium. The latter, which include for instance sector-based factors, are unrewarded factors but still contribute to the risk and short-term performance of a portfolio and remain important for risk management purposes.
In the absence of a particular view on the market, and provided an investor is willing to accept the risk of deviating from the broad-based cap-weighted strategy, Scientific Portfolio’s investment philosophy is that over the long term, well-constructed systematic investment portfolios are those that are mostly exposed to rewarded risks and that have managed to reduce most of their stock-specific idiosyncratic risk thanks to diversification. When modifying or constructing a portfolio to meet a specific objective, it is therefore important to keep in mind the principle of maintaining well-diversified, rewarded factor exposures. This is especially relevant for investors wishing to pursue an extra-financial (ESG) objective while meeting financial constraints related to their fiduciary responsibility.
In practice, implementing or identifying portfolios in line with the above principles requires a comprehensive, yet parsimonious and actionable, risk model. Scientific Portfolio recognizes that building a risk model is perhaps as much of an art as it is a science. There are three common types of factor models used by academics and industry practitioners: i) statistical models, where both factors and factor exposures, i.e., betas, are assumed unobservable and need to be estimated, ii) fundamental time series models which are popular with academics and where factors are assumed observable but not betas, and iii) fundamental cross-sectional models, used by popular risk engines in the industry and where betas are assumed observable but not factors.
Having reviewed the various approaches to factor modeling, we have designed our risk model to have the intuitive appeal of a time series approach, the flexibility of a cross-sectional approach, and the parsimony of a statistical approach, along with a very strong ability to explain risk.
We scan a portfolio using seven (rewarded) fundamental factors and ten (unrewarded) sector-based risk factors. The seventeen estimated betas (factor exposures) represent a full risk identification card of the portfolio. Our model appropriately disentangles the informational overlaps likely to be found between fundamental betas and sector betas, and at the same time reduces the dimensionality of the data to avoid overspecification. This produces an exhaustive decomposition of the systematic risk of the portfolio across seventeen risk contributions, each associated with an intuitive and meaningful risk dimension.
Traditionally, once a set of financially efficient portfolios have been identified or implemented, investors would determine their optimal allocation by selecting the efficient portfolio that best aligns with their risk and return preferences or constraints. The advent of ESG investing requires enriching the traditional investment framework while staying true to academically validated principles and carefully anticipating the possible effects of ESG on financial performance, financial risk and investor preferences.
As far as performance is concerned, academic research currently provides no conclusive evidence of significant outperformance associated with incorporating extra-financial objectives in an investment strategy. In other words, virtue is its own reward and investors should not expect to “do well” in the long run as a result of “doing good”.
As far as risk is concerned, it is important to judge ESG through the same lens and framework as other traditional types of risk and identify those ESG-related issues that may have a material financial impact on an investment portfolio. In this context, the question of whether ESG may lead to the introduction of new systematic risk factors is still debated in academic literature. Some empirical evidence has been provided for specific dimensions of ESG (e.g., climate transition risks) but this cannot be affirmed for all ESG criteria. For this reason, institutional investors need to carefully consider the implications of proactively managing ESG risks in their portfolios with regards to their fiduciary duties.
Finally, ESG issues may affect the individual preferences of investors who wish to pursue extra-financial objectives instead of, or in conjunction with, financial objectives. Portfolio construction is also a financial technology that helps investors customize their investments to their own preferences, and Scientific Portfolio offers ESG impact functionalities to investors interested in following a double materiality approach (i.e., both the new financial risks linked to ESG issues and the extra-financial impacts of finance on the environment or on society).
For those investors also looking to consider extra-financial objectives, Scientific Portfolio offers a framework that is consistent with the original and historical concept of sustainable development (dating back to conversations between Jefferson and Lafayette in the 18th century about the “self-evident” nature of the “rights of future generations”) which is very close to a do no harm (DNH) injunction.
Exclusion strategies are naturally associated with a DNH approach and enable investors to align their entire investment universe with the original concept of sustainable development while retaining the flexibility to rebalance or adjust weights within a portfolio to manage its financial risk and return profile. These exclusion strategies therefore ensure that a portfolio, irrespective of its allocation, does not contribute negatively to sustainable development. This approach is likely to be more robust out of sample than score-based approaches that rely on the ex-ante maximization of the portfolio’s weighted-average score.
We implement our DNH philosophy by relying on a set of commonly accepted extra-financial objectives, namely the United Nations’ 17 Sustainable Development Goals (SDGs) announced in 2015, which are used by investors to assess and manage the contribution of their portfolio to one or several SDGs. While most of the current SDG frameworks for investors focus on the positive contributions of stocks to SDGs, Scientific Portfolio considers it a priority to identify potential negative contributions to SDGs. Therefore, we have developed a comprehensive and objective framework that identifies a company’s activities or behaviors that might have a negative impact on each SDG and its corresponding official Targets. Our ESG impact framework also relies on two other levers of action to pursue extra-financial objectives: reallocation and engagement. These two levers are relevant for portfolios aiming to go beyond DNH by generating a positive impact (“do good”) or more generally for the dynamic assessment (that is, not just at a single point in time) of a portfolio’s ESG impact, e.g., whether it is evolving towards or away from sustainability, or whether it is aligned with a given climate pathway.
Sustainable investment therefore leaves investors with more choices. The latter will be largely influenced by complex personal priorities or preferences which will likely make consensus-based approaches somewhat elusive. As a result, investors will require customization capabilities and transparency to construct their very own “financially-and-extra-financially-optimal” portfolios. The search for the optimal portfolio should be grounded on a comprehensive set of analytics that identify the relevant portfolio allocation actions and that help investors navigate possible finance/ESG trade-offs.