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Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction

Alik Sokolov, Kyle Caverly, Jonathan Mostovoy, Talal Fahoum and Luis Seco
The Journal of Financial Data Science Summer 2021, jfds.2021.1.070; DOI: https://doi.org/10.3905/jfds.2021.1.070
Alik Sokolov
is the managing director of machine learning at RiskLab at the University of Toronto in Toronto, Canada
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Kyle Caverly
is a machine learning researcher at RiskLab at the University of Toronto in Toronto, Canada
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Jonathan Mostovoy
is the managing director of research and partnerships at RiskLab at the University of Toronto in Toronto, Canada
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Talal Fahoum
is a research analyst at RiskLab at the University of Toronto in Toronto, Canada
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Luis Seco
is the head of RiskLab and director of the mathematical finance program at the University of Toronto in Toronto, Canada, CEO of GGSJ Centre, and CEO of Sigma Analysis & Management, Ltd
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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.

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The Journal of Financial Data Science: 4 (2)
The Journal of Financial Data Science
Vol. 4, Issue 2
Spring 2022
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Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction
Alik Sokolov, Kyle Caverly, Jonathan Mostovoy, Talal Fahoum, Luis Seco
The Journal of Financial Data Science Jun 2021, jfds.2021.1.070; DOI: 10.3905/jfds.2021.1.070

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Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction
Alik Sokolov, Kyle Caverly, Jonathan Mostovoy, Talal Fahoum, Luis Seco
The Journal of Financial Data Science Jun 2021, jfds.2021.1.070; DOI: 10.3905/jfds.2021.1.070
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