Revolutionising portfolio management with large language model
The project focuses on harnessing the capabilities of Large Language Models (LLMs) to enhance the functionality and user experience of Robo-Advisor applications. The primary objective is to develop a sophisticated system capable of providing personalised portfolio recommendations and facilitating...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/174914 |
| _version_ | 1811678712301617152 |
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| author | Kee, Kai Teng |
| author2 | Ng Wee Keong |
| author_facet | Ng Wee Keong Kee, Kai Teng |
| author_sort | Kee, Kai Teng |
| collection | NTU |
| description | The project focuses on harnessing the capabilities of Large Language Models
(LLMs) to enhance the functionality and user experience of Robo-Advisor
applications. The primary objective is to develop a sophisticated system capable of
providing personalised portfolio recommendations and facilitating fund exploration
for investors. Through the integration of advanced natural language processing
techniques and the implementation of a Retriever-Augmented Generation (RAG)
architecture, the application aims to deliver tailored investment advice based on
individual risk profiles and investment preferences. Additionally, the system aims to
offer an intuitive interface for users to explore various investment options, analyse
performance metrics, and make informed decisions. To ensure users have access to
up-to-date information on their portfolios, a robust data pipeline has been set up to
continuously ingest and process market data daily. |
| first_indexed | 2024-10-01T02:57:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/174914 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:57:37Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1749142024-04-19T15:45:22Z Revolutionising portfolio management with large language model Kee, Kai Teng Ng Wee Keong School of Computer Science and Engineering AWKNG@ntu.edu.sg Computer and Information Science The project focuses on harnessing the capabilities of Large Language Models (LLMs) to enhance the functionality and user experience of Robo-Advisor applications. The primary objective is to develop a sophisticated system capable of providing personalised portfolio recommendations and facilitating fund exploration for investors. Through the integration of advanced natural language processing techniques and the implementation of a Retriever-Augmented Generation (RAG) architecture, the application aims to deliver tailored investment advice based on individual risk profiles and investment preferences. Additionally, the system aims to offer an intuitive interface for users to explore various investment options, analyse performance metrics, and make informed decisions. To ensure users have access to up-to-date information on their portfolios, a robust data pipeline has been set up to continuously ingest and process market data daily. Bachelor's degree 2024-04-16T04:35:11Z 2024-04-16T04:35:11Z 2024 Final Year Project (FYP) Kee, K. T. (2024). Revolutionising portfolio management with large language model. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174914 https://hdl.handle.net/10356/174914 en SCSE23-0201 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Kee, Kai Teng Revolutionising portfolio management with large language model |
| title | Revolutionising portfolio management with large language model |
| title_full | Revolutionising portfolio management with large language model |
| title_fullStr | Revolutionising portfolio management with large language model |
| title_full_unstemmed | Revolutionising portfolio management with large language model |
| title_short | Revolutionising portfolio management with large language model |
| title_sort | revolutionising portfolio management with large language model |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/174914 |
| work_keys_str_mv | AT keekaiteng revolutionisingportfoliomanagementwithlargelanguagemodel |