Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile
This project builds upon the foundation laid in the first phase by enhancing the personalization of investment advice using ChatGPT, focusing specifically on aligning risk profiles according to an investor's existing stock portfolio. Traditional approaches to investment advice often overlook...
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| Format: | Final Year Project / Dissertation / Thesis |
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2024
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| Online Access: | http://eprints.utar.edu.my/6392/1/22ACB00648_FYP.pdf http://eprints.utar.edu.my/6392/ |
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| _version_ | 1850177482785816576 |
|---|---|
| author | Chin, Zhi Liang |
| author_facet | Chin, Zhi Liang |
| author_sort | Chin, Zhi Liang |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description |
This project builds upon the foundation laid in the first phase by enhancing the personalization of investment advice using ChatGPT, focusing specifically on aligning risk profiles according to an investor's existing stock portfolio. Traditional approaches to investment advice often overlook the unique composition and characteristics of an investor’s portfolio, which can significantly impact their risk tolerance and investment strategy. This project aims to address this gap by providing tailored investment recommendations that are not only based on the investor's risk profile but also on their current portfolio holdings. By analysing both the risk appetite and the composition of the investor’s stock portfolio, the system utilizes ChatGPT to deliver personalized and dynamic investment suggestions. This approach enables the model to better understand the investor’s preferences, such as balancing risk levels, optimizing for growth or stability, and identifying potential diversification opportunities. By incorporating portfolio analysis, the system can offer more targeted recommendations that align with the investor’s financial goals and risk tolerance, thereby improving decision-making processes and investment outcomes. This phase represents a significant step forward in the use of large language models for investment advice, enhancing their ability to provide more accurate and relevant suggestions tailored to individual circumstances. The project’s goal is to empower investors of all experience levels to make more informed and strategic investment decisions based on their unique risk profile and portfolio composition. Hence, this is the proposal for a project called “Leveraging ChatGPT (Large Language Models) For Portfolio Evaluation Based on Investor Risk Profile”. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6392 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my-utar-eprints.63922025-11-14T09:28:52Z Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile Chin, Zhi Liang QA76 Computer software T Technology (General) This project builds upon the foundation laid in the first phase by enhancing the personalization of investment advice using ChatGPT, focusing specifically on aligning risk profiles according to an investor's existing stock portfolio. Traditional approaches to investment advice often overlook the unique composition and characteristics of an investor’s portfolio, which can significantly impact their risk tolerance and investment strategy. This project aims to address this gap by providing tailored investment recommendations that are not only based on the investor's risk profile but also on their current portfolio holdings. By analysing both the risk appetite and the composition of the investor’s stock portfolio, the system utilizes ChatGPT to deliver personalized and dynamic investment suggestions. This approach enables the model to better understand the investor’s preferences, such as balancing risk levels, optimizing for growth or stability, and identifying potential diversification opportunities. By incorporating portfolio analysis, the system can offer more targeted recommendations that align with the investor’s financial goals and risk tolerance, thereby improving decision-making processes and investment outcomes. This phase represents a significant step forward in the use of large language models for investment advice, enhancing their ability to provide more accurate and relevant suggestions tailored to individual circumstances. The project’s goal is to empower investors of all experience levels to make more informed and strategic investment decisions based on their unique risk profile and portfolio composition. Hence, this is the proposal for a project called “Leveraging ChatGPT (Large Language Models) For Portfolio Evaluation Based on Investor Risk Profile”. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6392/1/22ACB00648_FYP.pdf Chin, Zhi Liang (2024) Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile. Final Year Project, UTAR. http://eprints.utar.edu.my/6392/ |
| spellingShingle | QA76 Computer software T Technology (General) Chin, Zhi Liang Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title | Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title_full | Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title_fullStr | Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title_full_unstemmed | Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title_short | Leveraging ChatGPT (large language models) for portfolio evaluation based on investor risk profile |
| title_sort | leveraging chatgpt (large language models) for portfolio evaluation based on investor risk profile |
| topic | QA76 Computer software T Technology (General) |
| url | http://eprints.utar.edu.my/6392/1/22ACB00648_FYP.pdf http://eprints.utar.edu.my/6392/ |
| url_provider | http://eprints.utar.edu.my |