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Deep learning methods including LSTM and SVM are used to compare ESG-integrated portfolios with standard benchmarks. ESG portfolios show comparable results while supporting sustainability objectives, including Sharpe ratio outcomes and risk-return improvements. 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Factors Impact on Financial Performance using Machine Learning  \nMSc Research Project  \nMSc in Data Analytics  \nAkansha Sharma  \nStudent ID: x23194961  \nSchool of Computing National College of Ireland  \nSupervisor: Prof. William Clifford  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Akansha Sharma |\n| --- | --- |\n| Student ID: | x23194961 |\n| Programme: | MSc in Data Analytics |\n| Year: | 2024 |\n| Module: | MSc Research Project |\n| Supervisor: | Prof.; William Clifford |\n| Submission Due Date: | 12/12/2024 |\n| Project Title: | Investment Portfolio Optimization and ESG Factors Impact on Financial Performance using Machine Learning |\n| Word Count: | 8453 |\n| Page Count: | 23 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Akansha Sharma |\n| --- | --- |\n| Date: | 29th January 2025 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nInvestment Portfolio Optimization and ESG Factors Impact on Financial Performance using Machine  \nLearning  \nAkansha Sharma  \nx23194961  \nAbstract  \nThis study aims to examine the effects of incorporating ESG factors in the investment portfolio and improving the performance of the portfolio using machine learning. The rationale for the study arises from the increasing relevance of sustainable investment and the challenges of integrating ESG factors into investment decision-making to increase returns and manage risks profitably. By applying deep learning technologies, such as LSTM and SVM, the paper contrasts the efficiency of ESG portfolios with standard ones.  \nThe study explores that comparable financial results can be achieved while aiding sustainability goals with ESG-integrated portfolios. The integration of ESG factors in the portfolio showed a Sharpe ratio of 0.538 and balanced performance metrics, close to the Sharpe ratio (0.541) of the non-ESG portfolio. Portfolios optimized for specific ESG components, such as Environmental Risk (Sharpe Ratio:  \n0.505; ESG Score: 0.841) provided a better fit to sustainability criteria while slightly lower financial parameters. Therefore, these results demonstrate the effectiveness of ESG factors in improving investment approaches and providing optimized results regarding the Sharpe ratio, volatility, and maximum drawdown.  \nConsequently, it becomes possible to tap into the potential of machine learning in expanding the ESG portfolio while delivering significant improvements in ethically and financially attainable results. Hence, for the creation of comprehensive, efficient, responsible, and sustainable investment practices, this research contributes to ESG integration frameworks. Future work may expand on these results by exploring broader data sets and more complex techniques to define po","cbCaiq9zmqvm0amg","https://ap.wps.com/l/cbCaiq9zmqvm0amg","pdf",2190506,25,"English","# Abstract\n# 1 Introduction\n## 1.1 Overview\n## 1.2 Research Questions and Objectives","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to evaluate how adding ESG factors to an investment portfolio and optimizing it with machine learning affects financial performance and risk management.\"},{\"question\":\"Which machine learning methods are used for ESG portfolio optimization?\",\"answer\":\"The work applies deep learning and related machine learning approaches including LSTM and SVM, alongside heuristic models.\"},{\"question\":\"What evidence is presented regarding the performance of ESG-integrated portfolios?\",\"answer\":\"The study reports that ESG portfolios achieve balanced performance and show Sharpe ratio results close to non-ESG portfolios, while also improving alignment with sustainability criteria.\"}]","Investment Portfolio Optimization and ESG Factors Impact on Financial Performance using Machine Learning - MSc Research Project | PDF",63]