Artificial intelligence/machine learning for wealth management on mobile device
Traditionally, portfolio management involves balancing a portfolio with different assets using statistical methods of analysis. These analyses are typically performed by portfolio managers or expert investors. For the amateur investor, the level of research required to form a solid understanding...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156611 |
| _version_ | 1811693767541915648 |
|---|---|
| author | Sim, Eccles Jia Xuen |
| author2 | Ng Wee Keong |
| author_facet | Ng Wee Keong Sim, Eccles Jia Xuen |
| author_sort | Sim, Eccles Jia Xuen |
| collection | NTU |
| description | Traditionally, portfolio management involves balancing a portfolio with different assets using
statistical methods of analysis. These analyses are typically performed by portfolio managers or
expert investors. For the amateur investor, the level of research required to form a solid
understanding of assets can be unmanageable. In the absence of time, tools, or level of
information to match the experts, this project explores artificial intelligence solutions that may
aid in reducing the analytical gap between amateur investors and financial experts.
Our goal is to create an application that is intuitive to an amateur investor while maintaining the
technicalities required for deep valuations of portfolio assets. Apart from the ability to learn and
predict optimal allocations of portfolios, the application provides supplementary features
automating the analysis of a portfolio using standard modern portfolio theory (MPT) frameworks.
The mobile application is developed using the Dart programming language along with the Flutter
Framework. A variant of the deep reinforcement learning algorithm known as proximal policy
optimization (PPO) is used as the agent to learn an investor’s portfolio and suggest optimal stock
allocations for maximized returns.
It is imperative to note that this mobile application is a proof of concept and is not financial
advice.
Keywords: Analysis; Reinforcement Learning; Mobile Application; Amateur Investor |
| first_indexed | 2024-10-01T06:56:55Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156611 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:56:55Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1566112022-04-21T04:58:25Z Artificial intelligence/machine learning for wealth management on mobile device Sim, Eccles Jia Xuen Ng Wee Keong School of Computer Science and Engineering AWKNG@ntu.edu.sg Engineering::Computer science and engineering Traditionally, portfolio management involves balancing a portfolio with different assets using statistical methods of analysis. These analyses are typically performed by portfolio managers or expert investors. For the amateur investor, the level of research required to form a solid understanding of assets can be unmanageable. In the absence of time, tools, or level of information to match the experts, this project explores artificial intelligence solutions that may aid in reducing the analytical gap between amateur investors and financial experts. Our goal is to create an application that is intuitive to an amateur investor while maintaining the technicalities required for deep valuations of portfolio assets. Apart from the ability to learn and predict optimal allocations of portfolios, the application provides supplementary features automating the analysis of a portfolio using standard modern portfolio theory (MPT) frameworks. The mobile application is developed using the Dart programming language along with the Flutter Framework. A variant of the deep reinforcement learning algorithm known as proximal policy optimization (PPO) is used as the agent to learn an investor’s portfolio and suggest optimal stock allocations for maximized returns. It is imperative to note that this mobile application is a proof of concept and is not financial advice. Keywords: Analysis; Reinforcement Learning; Mobile Application; Amateur Investor Bachelor of Engineering (Computer Science) 2022-04-21T04:58:24Z 2022-04-21T04:58:24Z 2022 Final Year Project (FYP) Sim, E. J. X. (2022). Artificial intelligence/machine learning for wealth management on mobile device. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156611 https://hdl.handle.net/10356/156611 en SCSE21-0080 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Sim, Eccles Jia Xuen Artificial intelligence/machine learning for wealth management on mobile device |
| title | Artificial intelligence/machine learning for wealth management on mobile device |
| title_full | Artificial intelligence/machine learning for wealth management on mobile device |
| title_fullStr | Artificial intelligence/machine learning for wealth management on mobile device |
| title_full_unstemmed | Artificial intelligence/machine learning for wealth management on mobile device |
| title_short | Artificial intelligence/machine learning for wealth management on mobile device |
| title_sort | artificial intelligence machine learning for wealth management on mobile device |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/156611 |
| work_keys_str_mv | AT simecclesjiaxuen artificialintelligencemachinelearningforwealthmanagementonmobiledevice |