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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/7390887 |
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| author | Jun Zhang Xuedong Chen |
| author_facet | Jun Zhang Xuedong Chen |
| author_sort | Jun Zhang |
| collection | DOAJ |
| 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 |
| id | doaj-art-097e5d26ef0a4a8bb0dea7cf6067cc00 |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-097e5d26ef0a4a8bb0dea7cf6067cc002025-08-20T02:03:16ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/7390887Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning PredictionJun Zhang0Xuedong Chen1School of Management and EngineeringSchool of Management and EngineeringAlthough 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.http://dx.doi.org/10.1155/2021/7390887 |
| spellingShingle | Jun Zhang Xuedong Chen Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction Discrete Dynamics in Nature and Society |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2021/7390887 |
| work_keys_str_mv | AT junzhang sociallyresponsibleinvestmentportfolioconstructionwithadoublescreeningmechanismconsideringmachinelearningprediction AT xuedongchen sociallyresponsibleinvestmentportfolioconstructionwithadoublescreeningmechanismconsideringmachinelearningprediction |