@article {Sokolovjfds.2021.1.070, author = {Alik Sokolov and Kyle Caverly and Jonathan Mostovoy and Talal Fahoum and Luis Seco}, title = {Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction}, elocation-id = {jfds.2021.1.070}, year = {2021}, doi = {10.3905/jfds.2021.1.070}, publisher = {Institutional Investor Journals Umbrella}, abstract = {The authors propose an approach that combines modern machine learning techniques in natural language processing with portfolio optimization to incorporate views of companies{\textquoteright} 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{\textendash}Litterman portfolio framework and finally compare the performance of an ESG-tilted portfolio against a standard Black{\textendash}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 constructionKey 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.}, issn = {2640-3943}, URL = {https://jfds.pm-research.com/content/early/2021/06/30/jfds.2021.1.070}, eprint = {https://jfds.pm-research.com/content/early/2021/06/30/jfds.2021.1.070.full.pdf}, journal = {The Journal of Financial Data Science} }