Growing A Green Decision Tree — Machine Learning And ESG

Growing a Green Decision Tree—Machine Learning and ESG

Assuming that the growth of environmental, social, and governance-based investing is not yet at, but is approaching, a tipping point, and assuming that knowing that tipping point with precision will yield alpha, can machine learning help find it?

That is the question posed in a recent paper by the global head of credit markets for LBBW, Joachim Erhardt. He holds that the tipping point will come about both because of regulatory pressures around the world and because of product innovation, as exemplified by green bonds and green ETFs.

Inadequate Evidence and an Expected Tipping Point

Erhardt considers the evidence that ESG improves performance and finds it inadequate. He calls this “difficult to isolate” in a statistically significant way. The results of studies on this point “vary with time frame, the investment strategy, portfolio constraints, the specific ESG criteria and whether the chosen benchmarks are at all appropriate or representative on a broader scale.”

In Erhardt’s view, the evidence is so unconvincing because idiosyncratic or cyclical downturns are especially unforgiving for the ESG-relevant portion of a portfolio. This is what will be changed when a tipping point is reached, and the question is no longer “why use ESG criteria” but “why not?” That will reduce volatility for the  assets, improving performance.

How can this point be identified? If by machine learning, what can we say about how the algorithm(s) will warn us once we are about there?

Boosting the Decision Trees, and What It Teaches

The system that Erhardt has in mind is a gradiant-boosted decision trees system. A “decision tree” is an easy enough idea. Either A or B. If A, then C or D. But if B, then C/D is out of the question and what follows is either Y or Z. Such forking of branches makes up a tree-like diagram pretty quickly. “Boosting” means the use of learning algorithms in a series to create a strong learner out of weak learners. At each stage, care must be taken to keep the boosting from becoming an over-fitting, to allow the system as a whole to generalize. This care-taking involves the “gradiant” of our “gradiant boosting.”

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