ESG integration in active systematic strategies.

In the systematic or quantitative rules-based space, the analysts may assess ESG factors at the research stage, typically using a third-party database or using a mix of third-party and internal proprietary data. Compared to the discretionary approaches, the systematic approach does not use individual company assessment. Instead, analysis is applied over a large data set that may contain thousands of companies. Quantitative statistical models are used to identify attractive investment opportunities by identifying which combinations of factors, including ESG factors, deliver value. By aggregating ESG data into an ESG score, the factor is then added to the models that rely on traditional factors such as value, size, momentum, or growth. The strategy created will usually be based on several factors, including ESG factors, that will be used to pick a selection of securities that reflect that combination of factors. The securities in the available universe will then be ranked.

A typical factor portfolio will buy, or go long, the securities in the top decile or percentile of the investment universe and sell, or short, the securities in the bottom decile of the investment universe. The strategy will also need to specify the frequency with which the portfolio will be automatically rebalanced. This is often done monthly. Before the strategy is implemented, it's usually backtested for the analyst to see how much risk-adjusted return would've been generated historically over a long-time horizon because this will help to generate an expectation of future investment returns using this strategy. It also allows them to use different macroeconomic and market scenarios in the analysis. The inclusion of ESG data in the systematic investment process results in upward or downward adjustments to the weights of securities, including to zero. For example, a strong score on a social factor might be sought by the analyst. Systematic approaches can attempt to derive correlations to understand how ESG factors might impact financial performance over time and then weight those ESG factors accordingly. Systematic investors may use algorithmic approaches that use ESG data, for example, data scraped from internet news articles, and then processes ESG data through a rules-based formula to adjust company weights.