Environmental, social and governance criteria on thousands of companies are assessed using Big Data analytics, following which the Forward Lucy methodology is then applied. We follow three broad steps.

1. SUSTAINABLE BIG DATA ANALYTICS

We analyse environmental, social and governance criteria on thousands of companies with the help of Big Data analytics, through which we can order/systematise the vast amounts of data available to us. The underlying data are taken from three sources: from reports generated by the companies themselves, public news sources and NGO activity. We can make sense of this data without having to pour over reports from all the companies ourselves, using algorithms instead to get the needed insight, making us more efficient and time-effective at ESG analysis compared to a traditional approach. In other words, we bring our systematic investment approach to Forward Lucy Sustainable as well!

2. SCORING

Given the large amounts of data involved, we place a lot of emphasis on structuring. And as you can see above, this means dividing the ESG criteria into various ‘sub topics’, which are then used to produce a score for each ESG pillar. For example, we score ‘Environmental Management’ as part of the Environment pillar and we assess for instance how many environment-related lawsuits the company has faced or to what extent it invests in managing its carbon footprint. We aggregate all these data across the three pillars to come up with a final score, which gives us the right framework to ensure we exclude the bad companies and can focus on the good ones.

3. THE RESULT

We build a portfolio that is firmly tilted toward companies with sustainable business models and in so doing can avoid those that are more susceptible to scandals. This is done by building the sustainability factor into Lucy’s factor rotation model and always keeping it ‘on’, thereby ensuring we always have a bias towards the more sustainable firms. In summary, we can help investors make a positive social impact, but at the same time also limit risk and, by using a sophisticated process, also not neglect returns.