Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by com...

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Autores principales:	Jun Zhang, Xuedong Chen
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/097e5d26ef0a4a8bb0dea7cf6067cc00
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spelling	oai:doaj.org-article:097e5d26ef0a4a8bb0dea7cf6067cc002021-11-29T00:55:35ZSocially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction1607-887X10.1155/2021/7390887https://doaj.org/article/097e5d26ef0a4a8bb0dea7cf6067cc002021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7390887https://doaj.org/toc/1607-887XAlthough socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.Jun ZhangXuedong ChenHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Jun Zhang Xuedong Chen Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
description	Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.
format	article
author	Jun Zhang Xuedong Chen
author_facet	Jun Zhang Xuedong Chen
author_sort	Jun Zhang
title	Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
title_short	Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
title_full	Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
title_fullStr	Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
title_full_unstemmed	Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction
title_sort	socially responsible investment portfolio construction with a double-screening mechanism considering machine learning prediction
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/097e5d26ef0a4a8bb0dea7cf6067cc00
work_keys_str_mv	AT junzhang sociallyresponsibleinvestmentportfolioconstructionwithadoublescreeningmechanismconsideringmachinelearningprediction AT xuedongchen sociallyresponsibleinvestmentportfolioconstructionwithadoublescreeningmechanismconsideringmachinelearningprediction
_version_	1718407752041103360

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

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