Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction

Abstract

The authors propose an approach that combines modern machine learning techniques in natural language processing with portfolio optimization to incorporate views of companies’ environment, social, and governance (ESG) performance. This is automatically done through curating and subsequently converting large-scale news data into portfolio management decisions. They train a machine learning news data classifier to automatically identify several key ESG issues in news data over time. They then aggregate these issues over time to generate a views vector under the Black–Litterman portfolio framework and finally compare the performance of an ESG-tilted portfolio against a standard Black–Litterman portfolio. They also show how this can be achieved at scale, in a fully automated manner, and with consistency over large periods of time. Their methodology thus demonstrates a reasonable and agile method for asset managers to incorporate ESG considerations into their portfolios free of any exclusionary frameworks and without sacrificing performance.

TOPICS: ESG investing, quantitative methods, statistical methods, big data/machine learning, portfolio construction

Key Findings

• ▪ The authors describe a theoretical framework for using an automated NLP system to incorporate ESG criteria into portfolio optimization decisions.

• ▪ The authors demonstrate the technical implementation details for incorporating ESG signals into augmented portfolio weights.

• ▪ The authors demonstrate the competitive performance of such a portfolio through a long-term historical backtest with the S&P 500 Index.